Project Management for Research

The tools you need to make your research project a success.

This toolkit includes a variety of tools for managing your research projects including recommendations for general project management software and tools to help you and your team manage activities from grant writing to implementation and project closeout.

Explore the toolkit below:

Grant Writing + Project Development

A Gantt Chart is a popular project management tool; it is a type of bar chart that illustrates a project’s schedule. The chart allows for organizing and viewing project activities and tasks against pre-established timeframes.

Gantt Chart Template Gantt Chart Instructions Gantt Chart Example

Graphic display of the flow or sequence of events that a product or service follows; it shows all activities, decision points, rework loops and handoffs.

Process maps allow the team to visualize the process and come to agreement on the steps of a process as well as examine which activities are duplicated. Process maps are used to:

  • Capture current and new process information
  • Identify the flow of a process
  • Identify responsibility of different business functions
  • Clearly show hand-off between functions
  • Identify value added and non-value added activities
  • Train team members in new process

Process Map Template Process Mapping Guide Process Map Example 1 Process Map Example 2

The Data Management Plan (DMP) defines the responsibilities related to the entry, ownership, sharing, validation, editing and storage of primary research data.

A data management plan must not only reflect the requirements of the protocol/project but also comply with applicable institutional, state and federal guidelines and regulations. The DMP Tool details your agencies expectations, has suggested language for REDCap and exports a properly formatted plan.

DMP Tool NIH Data Management & Sharing (DMS) Policy

The Project Charter's purpose is to define at a high level what the Project Team will deliver, what resources are needed and why it is justified.

The Project Charter also represents a commitment to dedicate the necessary time and resources to the project. It can be especially useful when organizing a multi-disciplinary, internally funded team. The document should be brief (up to three pages maximum).   

Project Charter Template Project Charter Instructions Project Charter Example

Milestones are an effective way to track major progress in your research project.

A Gantt Chart is an effective tool for setting and tracking milestones and deliverables. It is a type of bar chart that illustrates a project’s schedule.  

The proposal budget should be derived directly from the project description.

The proposal budget should follow the format specified by the sponsor. The Office of Sponsored Programs Budget Preparation webpages provide descriptions of the standard budget categories, lists of typical components of those categories, Ohio State rates where appropriate and other details to help ensure your budget is complete. Budget Preparation Resources from Office of Research The 398 grant form from the NIH is a template that includes standard categories required for an NIH grant (and many others) that you can use to develop a preliminary budget.

PHS 398 Forms PHS 398 Budget form for Initial Project Period Template PHS 398 Budget Form for Entire Proposal Project Template

The Risk Assessment and Mitigation Plan first assists the research team in anticipating risk that may occur during the research project before it happens.

The plan then specifies when to act to mitigate risk by defining thresholds and establishing action plans to follow. As a fundamental ethical requirement research risks are to be minimized to the greatest extent possible for all research endeavors. This includes not only prompt identification measures but also response, reporting and resolution. Risk Assessment and Mitigation Plan Template Risk Assessment and Mitigation Plan Example

The Work Breakdown Structure (WBS) organizes the research project work into manageable components.

It is represented in a hierarchical decomposition of the work to be executed by the research project team. It visually defines the scope into manageable chunks that the team can understand.  WBS Instructions and Template WBS Structure Example

Implementation

A Gantt Chart is a popular project management tool; it is a type of bar chart that illustrates a project’s schedule.

The chart allows for organizing and viewing project activities and tasks against pre-established timeframes. A Gantt Chart can also be used for tracking milestones and major progresses within your research project.

The purpose is to define at a high level what the Project Team will deliver, what resources are needed and why it is justified.   

It is represented in a hierarchical decomposition of the work to be executed by the research project team. It visually defines the scope into manageable chunks that the team can understand.  WBS Instructions + Template WBS Structure Example

A communications plan facilitates effective and efficient dissemination of information to the research team members and major stakeholders in the research project.

It describes how the communications will occur; the content, security, and privacy of those communications; along with the method of dissemination and frequency.

Communications Plan Template Communications Plan Example

The Data Management Plan (DMP) defines the responsibilities related to the entry, ownership, sharing, validation, editing, and storage of primary research data.

A data management plan must not only reflect the requirements of the protocol/project but also comply with applicable institutional, state, and federal guidelines and regulations. The DMP Tool details your agencies expectations, has suggested language for REDCap, and exports a properly formatted plan.

DMP Tool DMP Tool Instructions Ohio State Research Guide: Data

The chart allows for organizing and viewing project activities and tasks against pre-established timeframes. Gantt Chart Template Gantt Chart Instructions Gantt Chart Example

This tool helps you capture details of issues that arise so that the project team can quickly see the status and who is responsible for resolving it.

Further, the Issue Management Tool guides you through a management process that gives you a robust way to evaluate issues, assess their impact, and decide on a plan for resolution.

Issue Management Tool Template Issue Management Tool Instructions Issue Management Example

A Pareto Chart is a graphical tool that helps break down a problem into its parts so that managers can identify the most frequent, and thus most important, problems.

It depicts in descending order (from left to right) the frequency of events being studied. It is based on the Pareto Principle or “80/20 Rule”, which says that roughly 80% of problems are caused by 20% of contributors. With the Pareto Principle Project Managers solve problems by identifying and focusing on the “vital few” problems. Managers should avoid focusing on “people” problems. Problems are usually the result of processes, not people.

Pareto Chart Template Pareto Chart Instructions Pareto Chart Example

Closeout, Transfer + Application

Completing a project means more than finishing the research. 

There remain financial, personnel, reporting, and other responsibilities. These tasks typically need to be completed within a timeline that begins 60 to 90 days before the project end date and 90 days after. Specifics will vary depending on the project and the funding source. The Office of Sponsored Programs “Project Closeout” webpage provides a description closeout issues, a list of PI Responsibilities and other details to help ensure your project is in fact complete.  Project Closeout Checklist Project Closeout Resources from Office of Research

A communications plan facilitates effective and efficient dissemination of information to the research team members and major stakeholders in the research project. 

It describes how the communications will occur; the content, security and privacy of those communications; along with the method of dissemination and frequency.

Project Management Software

An open-source project management software similar to Microsoft Project.

OpenProject  has tools to create dashboards, Gantt Charts, budgets, and status reports. Activities can be assigned to team members and progress monitored. OpenProject also has a tool for Agile Project Management. While the software is free, OpenProject must be installed and maintained on a local server and there will probably be costs associated with this. Talk to your departmental or college IT staff.

A secure, web-based project management system.

Basecamp  offers an intuitive suite of tools at a minimal cost: ~$20/month or free for teachers. Basecamp facilitates collaboration between research team members with features such as to-do lists, messaging, file sharing, assignment of tasks, milestones, due dates and time tracking.  

A project management tool that organizes tasks, activities, responsibilities and people on projects.

Trello can help manage research projects by keeping everyone on time and on task. It uses a distinctive interface based on cards and lists and may be especially useful for smaller projects.

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Types of Research – Explained with Examples

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  • By DiscoverPhDs
  • October 2, 2020

Types of Research Design

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

  • Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
  • Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

Methodology Research

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

Types of Research Methodology

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

Descriptive Research Design

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

Action Research Methods

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

What is Scientific Misconduct?

Scientific misconduct can be described as a deviation from the accepted standards of scientific research, study and publication ethics.

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Research Methods - University of Southampton Library

Project Management

Learning outcomes:.

After you complete this section you will be able to:

  • Learn how to create an action plan to successfully complete your project
  • Develop strategies to manage your time effectively
  • Learn useful tips for successful team work
  • Learn how to manage risks associated with completing a project

What is project management?

The Project Management Institute (PMI) defines a project as a “temporary endeavor undertaken to create a unique product, service, or result.” The ‘endeavour’ or ‘unique product’ in this case is your research project. It is about how you manage all aspects of writing your project so that you deliver a good piece of work by the deadline date for submission.

Project management (PM) involves setting clear goals, breaking down your project into bite-size tasks with clear deadlines, developing time management strategies to accomplish these tasks, and working with your supervisor and with other students. It also involves monitoring your project to ensure you are on schedule.

This short video gives tips on how to plan and monitor your project to ensure that you complete your research project on time.

To summarise :

In order to successfully complete your research project, you will need to:

  • Set goals . An example of a goal is to list all of the tasks that you would like to accomplish in the first or second semester. 
  • Break down tasks . An example would be to arrange your tasks monthly and weekly and daily. If you have four writing goals for September, then you can place one in each week of the month. If you have two, then give yourself two weeks for each.
  • Implement strategies for accomplishing your tasks.
  • Monitor and adjust the project as you go along.

The library holds a number of books about project management for students which cover additional information about managing your research project. The following are recommended. Search the library’s online catalogue to find the books and their location in the library.

The  next section is all about time management, looking at tips and tools to keep you on track.

types of research in project management

Research-Methodology

Project Management

types of research in project management

Activities undertaken by companies can be divided into two groups: its daily usual business operations and projects that are devised and implemented in order to achieve a specific objective, usually measures and activities that will assist in profit maximisation in short or/and long-term perspective.

Nowadays project management is used by companies not only to achieve a specific objective like building a new facilities or introducing a new software, project management as also being used as an efficient tool to achieve strategic objectives of a company.

This first part of the report analyses the reasons as why organizations are using project management in order to achieve their strategic objectives. It starts with the introduction of the problem and a brief analysis of the evolution of project management in order to study the background of the issue. Then the importance of project management in achieving strategic objectives is discussed together with the challenges likely to occur in linking project management and organizational objectives.

II. The Evolution of Project Management

The analysis of the evolution of project management gives us necessary knowledge and perspective required to assess the role of project management in achieving strategic objectives in a due manner.

Project management started as a tactical method mainly used to facilitate the completion of individual projects and initiatives such as introducing new software, initiating a new program or creating a new system. The beginning and popularization of project management is widely linked to the major work of Drucker “The Practice of Management” (1954). According to Panico (2002), from the 1940’s through the 1980’s PM was mainly thought to be a practice used by people working in engineering, construction and military.

The Development of modern project management can be divided into following four periods (Azzopardi, 2010):

1. The period before 1958 when the development of technology had shortened the timeframe of project completion. Also the invention of Gantt chart by Henry Gant assisted the development of project management.

2. 1958-1979: The principles of management sciences began to be applied to project management. The development of project management tools like CPM, PERT and many others by business scientists within this period aided the further development of project management and resulted in project management to be studied as a separate subject.

3. 1980-1994: The development of information technology had an immense effect on project management. Personal computers made many aspects of project management more simplistic and took project management to a new level.

4. 1994-present: Internet had a huge impact on the development of project management in a way that various project management software were available online and also internet began to be utilised by project managers as a source of information for finding contractors, gaining general knowledge, etc.

III. The Importance of Project Management in Achieving Strategic Objectives

The potential of project management in achieving organizational strategic objectives has been spotted and utilised by organizations with varying degree of success in a way that projects are being planned and implemented in such a manner that it not only solves a current problem or achieves its purpose, but it also contributes to the long term objectives of the organization.

The strategic objectives of the company come from the vision of the company that is the place employees see the company and themselves after a certain period of time. Then this vision translates into mission statement of the company which is used as guidance for all employees of the company, and, at the same time, communicates the message to the public about what the company stands for.

Organizational strategy and objectives are achieved through two elements: the planning and management of operations on high level and project portfolio management. The planning and management of operations on high level relates to the activities of senior level managers who define the ways and methods of how organizational objectives are going to be achieved. The senior level management undertakes the project portfolio planning and management which consist of various projects within the organization, as well as the management of on-going operations.

Each individual project within the project portfolio is devised either to deal with a specific problem concerning on-going operations or to increase the efficiency of on-going operations in some specified ways.

To put it simply the role of project management in achieving organizational objectives consist in the fact that the project management assists the operations through which organization’s strategic objectives are achieved, as well as, project management assists organizational objectives to be achieved being a part of project portfolio management.

Kendall and Rollings (2003), claim that the following conditions should be met in order for the project management to contribute to achieving strategic objectives:

A. Sufficient resources should be provided for project managers. The lack of resources for the project may cause for the project to be delayed and its quality compromised.

B. Project priorities should be constant. It requires efficiency on the planning stage of the project management. Frequently changing project priorities not only waste the resources, but also can cause the de-motivation of the workforce.

C. Projects should be launched only when sufficient resources are available. Thorough financial analysis is required priory to commencing of projects with the great emphasis on the availability of the resources.

D. Projects should be linked to company strategy. It is clear that projects with the aim not linked to the company strategies cannot provide long-term efficiency for the company, thus is considered not efficient use of resources used for the project.

Wessel (2007) differentiates between strategic project management and traditional strategic planning in following ways:

1. Strategic project management supports main business processes of strategic planning, strategic goal setting and enterprise project management

2. Strategic project management provides the opportunity of efficient use of resources employing the analogy of financial resources

3. Strategic project management provides a high level of ROI through an opportunity of managing several projects together

4. Changes on projects are implanted more easily under strategic project management. Three main components of project management can be identified as planning, execution and control (Koskela and Howell, 2001). Careful and thorough planning is one of the main important factors of success in project management. Planning should comprise all aspects of the project to be undertaken, including the timeframe of the project, the amount, availability and sources of resources, delegation of responsibilities among team members involved on the project, and the ways the project can contribute to the achievement of strategic objectives of the company.

The execution phase of the project management is also important and requires the existence and application of required skills and knowledge from project members in order to achieve the aims and objectives of the project in a best possible manner. The importance of control in project management should not to be underestimated. It is the responsibility of project manager and should ensure the high quality of the project and efficient use of the resources available.

IV. Major Challenges in Project Management

There are certain challenges in project management that are going to compromise the quality of projects if not addressed appropriately. The most common issue in project management is the amount of resources decision makers allocate for the projects that sometimes prove to be not enough due to many factors including lack of understanding the complexity of the project by decision makers. How efficiently available resources are going to be used depends on the competency of project manager, but still if required amount of resources are not provided.

Another challenge in project management is lack of skills in project manager and other members. Because each project is unique in some aspect various set of skills are required to complete them efficiently. An employee who is good in doing his usual tasks at workplace may not be efficient in equal manner when involved in a project where flexibility is required.

Duranti (2009) mentions about the possibility of contradiction between project management evaluation criteria and strategic objectives of a company, stating that even if a project cannot be successful when evaluated by management criteria, at the same time, it can be a successful project in terms of achieving company’s strategic objectives.

V. Conclusion

Companies engage in project management that are designed to assist in increasing the efficiency of on-going operations through fixing existing issues or implementing new initiatives. The important role of project management in achieving strategic objectives have been realised by many executives of companies and therefore, project management is used not only to achieve its immediate objectives but also to contribute to the strategic objectives as well.

This report identified the contribution of project management to the strategic objectives of the company through two channels. Firstly, each project is part of the project portfolio management of a company which directly contributes to the company’s strategic objectives. Secondly, projects assist on-going operations which are the main method of achievement of strategic objectives of the company.

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How to use a project management approach to help run research projects

Jon Gunnell explains how to adopt the PRINCE2 project management method to help overcome the many challenges of running a multi-year research project

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Jon Gunnell

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Timeline and calendar for project management

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Academics face numerous pressures on their time even before managing the process of, for example, a five-year research project that needs to deliver real-world benefits.

Such a project at the University of Sheffield’s School of Law – titled Fortitude and funded by the European Research Council – aims to improve the “legal capability” of children in the UK. The project’s ultimate goal is to create gamified learning for children aged from three to 15 that will help them deal with legal issues they encounter in their everyday lives. For example, how does a child engage with a shop assistant who gives them incorrect change?

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It is crucial – and difficult – for an academic team to ensure that a project like this is managed effectively and delivers its objectives. Managing research involves responsibility for other academics who, while accustomed to working independently, may be less familiar with delivering the outputs a project needs – and within a specific deadline. Plus, there may be a requirement to translate theoretical materials into something meaningful in the “real world” – in our case, devising gamified learning that children will use.

Adopting a project management approach in an academic setting – such as the PRINCE2 method , originally devised by the UK government to improve public sector project success and now used worldwide – can address the challenges of running a multi-year research project and avoid overwhelming academic teams.

Project management: the right discipline for managing research projects

A project – according to the PRINCE2 project management method – is defined as ‘‘a temporary organisation that is created for the purpose of delivering one or more business products according to an agreed business case’’.

Having a method to manage this entity means you have a safe and robust framework to operate in. It also helps ensure creativity and effective communication between team members. This is important because, without it, people tend to work in isolation. With a project management structure – including regular team meetings where people discuss problems and identify solutions – a team collaborates and tasks become actions and outputs.

The value of using a best practice method

Best practice project management methods such as PRINCE2 are the result of experts combining knowledge, experience and proven techniques gained from running various projects around the world.

Therefore, by either hiring a qualified project manager to run an academic research project, or training a relevant team member in the method, your project will be run according to clear principles:

− Defined project roles and responsibilities, which means people have clarity and there is less risk of just muddling through.

− A focus on deliverables (products or outputs), which ensures that everyone knows what the project aims to deliver.

− A business case to ensure that the project remains viable during its lifetime.

− Assurance, troubleshooting and audits to keep things on track.

− Learning and continuous improvement to avoid repeating mistakes and enhance quality.

− The ability to work with both an “agile” delivery approach (an evolving way of working involving regular testing and feedback) and a traditional “waterfall” project approach (linear and based on a plan agreed up front). For example, while our overall project approach is waterfall, briefing gaming companies to develop digital games for children is better handled with agile. But in either case, project management provides structure and control.

The key elements in PRINCE2 that help the research management process

There are numerous ways of working outlined in PRINCE2 that can support the management of a research project. These include:

1. The project plan

Having a project plan from the outset helps identify what a long-term project will look like, but with flexibility, as things might change. It also means that everyone involved can see the key milestones throughout the project.

2. Business case

Developing and revisiting a business case ensures that the project either remains viable or otherwise closes. In our project, this involved completing the European Research Council Grant Agreement: a document that brings together all the information necessary to obtain funding for the research project. On an annual basis, we also need to provide financial and scientific reports that outline what’s been spent, what’s been achieved and what’s planned.

3. Project benefits

Identifying benefits acknowledges that a successful project should change something for the better. In a research management context, that could mean discovering something groundbreaking.

4. Specifying business requirements

Identifies what the project requires for success and helps when tendering for suppliers. In our case, we’re now going out to tender with gaming companies to produce digital or physical games for children based on our research. Therefore, we have produced a specification document for the requirements.

5. Identifying risks

Pinpointing risks means anticipating what could impede the project and allows a project manager to find ways of minimising the risks and keeping stakeholders informed. For our project, we have a risk log that captures factors such as teachers’ strikes, which might mean school participants are unavailable at a crucial point. This helps us to replan an activity and keep the project on schedule.

6. Engaging stakeholders

Knowing who the project stakeholders are, mapping them according to their importance and agreeing how to interact with them ensures that they remain engaged throughout. For us, that can include internal stakeholders, such as the head of department in the university and external stakeholders, such as schools, who can support the project – and knowing how often we need to engage with them.

7. Developing a communication plan

Having different methods and channels to communicate with stakeholders is vital to demonstrate the work you’re doing and to share results and learnings. For example, we’ve communicated research findings and successes of the project periodically when attending external conferences and academic events at the university.

8. Regular, formal reporting

Delivering regular reports to a research project’s funding body might cover the latest research findings and how you are managing the budget. Without such reports, your funding could be at risk.

9. Documenting lessons learned

This helps the project team to reflect on different activities and how they could be improved next time. Questioning and capturing what’s gone well, what hasn’t and what you would do differently is also important for future projects.

How a project management method improves project outcomes

A project’s purpose is to deliver something new that will benefit an organisation or department. In other words, provide a positive outcome. In our case, having a project management method in place has helped us to deliver:

− An ethics approach for the project that meets both the University of Sheffield’s and the European Research Council’s requirements.

− A child-centred framework to measure legal capability, developed through research with children from a number of our partner schools.

− A GDPR approach that meets the requirements of the university and ensures the security of all personal data.

− A project website, which we have used as our key channel of communication for both project participants and stakeholders.

Replicating the value of project management in your institution

By including a project manager at the bid stage of a research project, the academic team can get dedicated support for the development of a project plan, which could then accompany their funding bid. And by sharing lessons learned and experiences gained across an institution, this can become the basis for developing and embedding best practice project management within any future projects.

Jon Gunnell is project manager at the University of Sheffield School of Law, UK.

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Click here for more information about the PRINCE2 project management method.

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  • v.20; 2019 Oct 25

Managing Ideas, People, and Projects: Organizational Tools and Strategies for Researchers

Samuel pascal levin.

1 Beverly, MA 01915, USA

Michael Levin

2 Allen Discovery Center at Tufts University, Suite 4600, 200 Boston Avenue, Medford, MA 02155-4243, USA

Primary Investigators at all levels of their career face a range of challenges related to optimizing their activity within the constraints of deadlines and productive research. These range from enhancing creative thought and keeping track of ideas to organizing and prioritizing the activity of the members of the group. Numerous tools now exist that facilitate the storage and retrieval of information necessary for running a laboratory to advance specific project goals within associated timelines. Here we discuss strategies and tools/software that, together or individually, can be used as is or adapted to any size scientific laboratory. Specific software products, suggested use cases, and examples are shown across the life cycle from idea to publication. Strategies for managing the organization of, and access to, digital information and planning structures can greatly facilitate the efficiency and impact of an active scientific enterprise. The principles and workflow described here are applicable to many different fields.

Graphical Abstract

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Information Systems; Knowledge Management

Introduction

Researchers, at all stages of their careers, are facing an ever-increasing deluge of information and deadlines. Additional difficulties arise when one is the Principal Investigator (PI) of those researchers: as group size and scope of inquiry increases, the challenges of managing people and projects and the interlocking timelines, finances, and information pertaining to those projects present a continuous challenge. In the immediate term, there are experiments to do, papers and grants to write, and presentations to construct, in addition to teaching and departmental duties. At the same time, however, the PI must make strategic decisions that will impact the future direction(s) of the laboratory and its personnel. The integration of deep creative thought together with the practical steps of implementing a research plan and running a laboratory on a day-to-day basis is one of the great challenges of the modern scientific enterprise. Especially difficult is the fact that attention needs to span many orders of scale, from decisions about which problems should be pursued by the group in the coming years and how to tackle those problems to putting out regular “fires” associated with the minutiae of managing people and limited resources toward the committed goals.

The planning of changes in research emphasis, hiring, grant-writing, etc. likewise occur over several different timescales. The optimization of resources and talent toward impactful goals requires the ability to organize, store, and rapidly access information that is integrated with project planning structures. Interestingly, unlike other fields such as business, there are few well-known, generally accepted guidelines for best practices available to researchers. Here we lay out a conceptual taxonomy of the life cycle of a project, from brainstorming ideas through to a final deliverable product. We recommend methods and software/tools to facilitate management of concurrent research activities across the timeline. The goal is to optimize the organization, storage, and access to the necessary information in each phase, and, crucially, to facilitate the interconnections between static information, action plans, and work product across all phases. We believe that the earlier in the career of a researcher such tools are implemented and customized, the more positive impact they will exert on the productivity of their enterprise.

This overview is intended for anyone who is conducting research or academic scholarship. It consists of a number of strategies and software recommendations that can be used together or independently (adapted to suit a given individual's or group's needs). Some of the specific software packages mentioned are only usable on Apple devices, but similar counterparts exist in the Windows and Linux ecosystems; these are indicated in Table 1 (definitions of special terms are given in Table 2 ). These strategies were developed (and have been continuously updated) over the last 20 years based on the experiences of the Levin group and those of various collaborators and other productive researchers. Although very specific software and platforms are indicated, to facilitate the immediate and practical adoption by researchers at all levels, the important thing is the strategies illustrated by the examples. As software and hardware inevitably change over the next few years, the fundamental principles can be readily adapted to newer products.

Software Packages and Alternatives

A Glossary of Special Terms

Basic Principles

Although there is a huge variety of different types of scientific enterprises, most of them contain one or more activities that can be roughly subsumed by the conceptual progression shown in Figure 1 . This life cycle progresses from brainstorming and ideation through planning, execution of research, and then creation of work products. Each stage requires unique activities and tools, and it is crucial to establish a pipeline and best practices that enable the results of each phase to effectively facilitate the next phase. All of the recommendations given below are designed to support the following basic principles:

  • • Information should be easy to find and access, so as to enable the user to have to remember as little as possible—this keeps the mind free to generate new, creative ideas. We believe that when people get comfortable with not having to remember any details and are completely secure in the knowledge that the information has been offloaded to a dependable system and will be there when they need it, a deeper, improved level of thinking can be achieved.
  • • Information should be both organized hierarchically (accessible by drill-down search through a rational structure) and searchable by keywords.
  • • Information should be reachable from anywhere in the world (but secure and access restricted). Choose software that includes a cell phone/tablet platform client.
  • • No information should ever be lost—the systems are such that additional information does not clog up or reduce efficiency of use and backup strategies ensure disaster robustness; therefore, it is possible to save everything.
  • • Software tools optimized for specific management tasks should be used; select those tools based on interoperability, features, and the ability to export into common formats (such as XML) in case it becomes expedient someday to switch to a newer product.
  • • One's digital world should be organized into several interlocking categories, which utilize different tools: activity (to-dos, projects, research goals) and knowledge (static information).
  • • One's activity should be hierarchically organized according to a temporal scale, ranging from immediate goals all the way to career achievement objectives and core mission.
  • • Storage of planning data should allow integration of plans with the information needed to implement them (using links to files and data in the various tools).
  • • There should be no stored paper—everything should be obtained and stored in a digital form (or immediately digitized, using one of the tools described later in this document).
  • • The information management tasks described herein should not occupy so much time as to take away from actual research. When implemented correctly, they result in a net increase in productivity.

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The Life Cycle of Research Activity

Various projects occupy different places along a typical timeline. The life cycle extends from creative ideation to gathering information, to formulating a plan, to the execution for the plan, and then to producing a work product such as a grant or paper based on the results. Many of these phases necessitate feedback to a prior phase, shown in thinner arrows (for example, information discovered during a literature search or attempts to formalize the work plan may require novel brainstorming). This diagram shows the product (end result) of each phase and typical tools used to accomplish them.

These basic principles can be used as the skeleton around which specific strategies and new software products can be deployed. Whenever possible, these can be implemented via external administration services (i.e., by a dedicated project manager or administrator inside the group), but this is not always compatible with budgetary constraints, in which case they can readily be deployed by each principal investigator. The PIs also have to decide whether they plan to suggest (or insist) that other people in the group also use these strategies, and perhaps monitor their execution. In our experience, it is most essential for anyone leading a complex project or several to adopt these methods (typically, a faculty member or senior staff scientist), whereas people tightly focused on one project and with limited concurrent tasks involving others (e.g., Ph.D. students) are not essential to move toward the entire system (although, for example, the backup systems should absolutely be ensured to be implemented among all knowledge workers in the group). The following are some of the methods that have proven most effective in our own experience.

Information Technology Infrastructure

Several key elements should be pillars of your Information Technology (IT) infrastructure ( Figure 2 ). You should be familiar enough with computer technology that you can implement these yourself, as it is rare for an institutional IT department to be able to offer this level of assistance. Your primary disk should be a large (currently, ∼2TB) SSD drive or, better, a disk card (such as the 2TB SSD NVMe PCIe) for fast access and minimal waiting time. Your computer should be so fast that you spend no time (except in the case of calculations or data processing) waiting for anything—your typing and mouse movement should be the rate-limiting step. If you find yourself waiting for windows or files to open, obtain a better machine.

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Schematic of Data Flow and Storage

Three types of information: data (facts and datasets), action plans (schedules and to-do lists), and work product (documents) all interact with each other in defining a region of work space for a given research project. All of this should be hosted on a single PC (personal computer). It is accessed by a set of regular backups of several types, as well as by the user who can interact with raw files through the file system or with organized data through a variety of client applications that organize information, schedules, and email. See Table 2 for definitions of special terms.

One key element is backups—redundant copies of your data. Disks fail—it is not a question of whether your laptop or hard drive will die, but when. Storage space is inexpensive and researchers' time is precious: team members should not tolerate time lost due to computer snafus. The backup and accessibility system should be such that data are immediately recoverable following any sort of disaster; it only has to be set up once, and it only takes one disaster to realize the value of paranoia about data. This extends also to laboratory inventory systems—it is useful to keep (and back up) lists of significant equipment and reagents in the laboratory, in case they are needed for the insurance process in case of loss or damage.

The main drive should be big enough to keep all key information (not primary laboratory data, such as images or video) in one volume—this is to facilitate cloning. You should have an extra internal drive (which can be a regular disk) of the same size or bigger. Use something like Carbon Copy Cloner or SuperDuper to set up a nightly clone operation. When the main disk fails (e.g., the night before a big grant is due), boot from the clone and your exact, functioning system is ready to go. For Macs, another internal drive set up as a Time Machine enables keeping versions of files as they change. You should also have an external drive, which is likewise a Time Machine or a clone: you can quickly unplug it and take it with you, if the laboratory has to be evacuated (fire alarm or chemical emergency) or if something happens to your computer and you need to use one elsewhere. Set a calendar reminder once a month to check that the Time Machine is accessible and can be searched and that your clone is actually updated and bootable. A Passport-type portable drive is ideal when traveling to conferences: if something happens to the laptop, you can boot a fresh (or borrowed) machine from the portable drive and continue working. For people who routinely install software or operating system updates, I also recommend getting one disk that is a clone of the entire system and applications and then set it to nightly clone the data only , leaving the operating system files unchanged. This guarantees that you have a usable system with the latest data files (useful in case an update or a new piece of software renders the system unstable or unbootable and it overwrites the regular clone before you notice the problem). Consider off-site storage. CrashPlan Pro is a reasonable choice for backing up laboratory data to the cloud. One solution for a single person's digital content is to have two extra external hard drives. One gets a clone of your office computer, and one is a clone of your home computer, and then you swap—bring the office one home and the home one to your office. Update them regularly, and keep them swapped, so that should a disaster strike one location, all of the data are available. Finally, pay careful attention (via timed reminders) to how your laboratory machines and your people's machines are being backed up; a lot of young researchers, especially those who have not been through a disaster yet, do not make backups. One solution is to have a system like CrashPlan Pro installed on everyone's machines to do automatic backup.

Another key element is accessibility of information. Everyone should be working on files (i.e., Microsoft Word documents) that are inside a Dropbox or Box folder; whatever you are working on this month, the files should be inside a folder synchronized by one of these services. That way, if anything happens to your machine, you can access your files from anywhere in the world. It is critical that whatever service is chosen, it is one that s ynchronizes a local copy of the data that live on your local machine (not simply keeps files in the cloud) —that way, you have what you need even if the internet is down or connectivity is poor. Tools that help connect to your resources while on the road include a VPN (especially useful for secure connections while traveling), SFTP (to transfer files; turn on the SFTP, not FTP, service on your office machine), and Remote Desktop (or VNC). All of these exist for cell phone or tablet devices, as well as for laptops, enabling access to anything from anywhere. All files (including scans of paper documents) should be processed by OCR (optical character recognition) software to render their contents searchable. This can be done in batch (on a schedule), by Adobe Acrobat's OCR function, which can be pointed to an entire folder of PDFs, for example, and left to run overnight. The result, especially with Apple's Spotlight feature, is that one can easily retrieve information that might be written inside a scanned document.

Here, we focus on work product and the thought process, not management of the raw data as it emerges from equipment and experimental apparatus. However, mention should be made of electronic laboratory notebooks (ELNs), which are becoming an important aspect of research. ELNs are a rapidly developing field, because they face a number of challenges. A laboratory that abandons paper notebooks entirely has to provide computer interfaces anywhere in the facility where data might be generated; having screens, keyboards, and mice at every microscope or other apparatus station, for example, can be expensive, and it is not trivial to find an ergonomically equivalent digital substitute for writing things down in a notebook as ideas or data appear. On the other hand, keeping both paper notebooks for immediate recording, and ELNs for organized official storage, raises problems of wasted effort during the (perhaps incomplete) transfer of information from paper to the digital version. ELNs are also an essential tool to prevent loss of institutional knowledge as team members move up to independent positions. ELN usage will evolve over time as input devices improve and best practices are developed to minimize the overhead of entering meta-data. However, regardless of how primary data are acquired, the researcher will need specific strategies for transitioning experimental findings into research product in the context of a complex set of personal, institutional, and scientific goals and constraints.

Facilitating Creativity

The pipeline begins with ideas, which must be cultivated and then harnessed for subsequent implementation ( Altshuller, 1984 ). This step consists of two components: identifying salient new information and arranging it in a way that facilitates novel ideas, associations, hypotheses, and strategic plans for making impact.

For the first step, we suggest an automated weekly PubCrawler search, which allows Boolean searches of the literature. Good searches to save include ones focusing on specific keywords of interest, as well as names of specific people whose work one wants to follow. The resulting weekly email of new papers matching specific criteria complements manual searches done via ISI's Web of Science, Google Scholar, and PubMed. The papers of interest should be immediately imported into a reference manager, such as Endnote, along with useful Keywords and text in the Notes field of each one that will facilitate locating them later. Additional tools include DevonAgent and DevonSphere, which enable smart searches of web and local resources, respectively.

Brainstorming can take place on paper or digitally (see later discussion). We have noticed that the rate of influx of new ideas is increased by habituating to never losing a new idea. This can be accomplished by establishing a voicemail contact in your cell phone leading to your own office voicemail (which allows voice recordings of idea fragments while driving or on the road, hands-free) and/or setting up Endnote or a similar server-synchronized application to record (and ideally transcribe) notes. It has been our experience that the more one records ideas arising in a non-work setting, the more often they will pop up automatically. For notes or schematics written on paper during dedicated brainstorming, one tool that ensures that nothing is lost is an electronic pen. For example, the Livescribe products are well integrated with Evernote and ensure that no matter where you are, anything you write down becomes captured in a form accessible from anywhere and are safe no matter what happens to the original notebook in which they were written.

Enhancing scientific thought, creative brainstorming, and strategic planning is facilitated by the creation of mind maps: visual representations of spatial structure of links between concepts, or the mapping of planned activity onto goals of different timescales. There are many available mind map software packages, including MindNode; their goal is to enable one to quickly set down relationships between concepts with a minimum of time spent on formatting. Examples are shown in Figures 3 A and 3B. The process of creating these mind maps (which can then be put on one's website or discussed with the laboratory members) helps refine fuzzy thinking and clarifies the relationships between concepts or activities. Mind mappers are an excellent tool because their light, freeform nature allows unimpeded brainstorming and fluid changes of idea structure but at the same time forces one to explicitly test out specific arrangements of plans or ideas.

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Mind Mapping

(A and B) The task of schematizing concepts and ideas spatially based on their hierarchical relationships with each other is a powerful technique for organizing the creative thought process. Examples include (A), which shows how the different projects in our laboratory relate to each other. Importantly, it can also reveal disbalances or gaps in coverage of specific topics, as well as help identify novel relationships between sub-projects by placing them on axes (B) or even identify novel hypotheses suggested by symmetry.

(C) Relationships between the central nervous system (CNS) and regeneration, cancer, and embryogenesis. The connecting lines in black show typical projects (relationships) already being pursued by our laboratory, and the lack of a project in the space between CNS and embryogenesis suggests a straightforward hypothesis and project to examine the role of the brain in embryonic patterning.

It is important to note that mind maps can serve a function beyond explicit organization. In a good mapped structure, one can look for symmetries (revealing relationships that are otherwise not obvious) between the concepts involved. An obvious geometric pattern with a missing link or node can help one think about what could possibly go there, and often identifies new relationships or items that had not been considered ( Figure 3 C), in much the same way that gaps in the periodic table of the elements helped identify novel elements.

Organizing Information and Knowledge

The input and output of the feedback process between brainstorming and literature mining is information. Static information not only consists of the facts, images, documents, and other material needed to support a train of thought but also includes anything needed to support the various projects and activities. It should be accessible in three ways, as it will be active during all phases of the work cycle. Files should be arranged on your disk in a logical hierarchical structure appropriate to the work. Everything should also be searchable and indexed by Spotlight. Finally, some information should be stored as entries in a data management system, like Evernote or DevonThink, which have convenient client applications that make the data accessible from any device.

Notes in these systems should include useful lists and how-to's, including, for example:

  • • Names and addresses of experts for specific topics
  • • Emergency protocols for laboratory or animal habitats
  • • Common recipes/methods
  • • Lists and outlines of papers/grants on the docket
  • • Information on students, computers, courses, etc.
  • • Laboratory policies
  • • Materials and advice for students, new group members, etc.
  • • Lists of editors, and preferred media contacts
  • • Lists of Materials Transfer Agreements (MTAs), contract texts, info on IP
  • • Favorite questions for prospective laboratory members

Each note can have attachments, which include manuals, materials safety sheets, etc. DevonThink needs a little more setup but is more robust and also allows keeping the server on one's own machine (nothing gets uploaded to company servers, unlike with Evernote, which might be a factor for sensitive data). Scientific papers should be kept in a reference manager, whereas books (such as epub files and PDFs of books and manuscripts) can be stored in a Calibre library.

Email: A Distinct Kind of Information

A special case of static information is email, including especially informative and/or actionable emails from team members, external collaborators, reviewers, and funders. Because the influx of email is ever-increasing, it is important to (1) establish a good infrastructure for its management and (2) establish policies for responding to emails and using them to facilitate research. The first step is to ensure that one only sees useful emails, by training a good Bayesian spam filter such as SpamSieve. We suggest a triage system in which, at specific times of day (so that it does not interfere with other work), the Inbox is checked and each email is (1) forwarded to someone better suited to handling it, (2) responded quickly for urgent things that need a simple answer, or (3) started as a Draft email for those that require a thoughtful reply. Once a day or a couple of times per week, when circumstances permit focused thought, the Draft folder should be revisited and those emails answered. We suggest a “0 Inbox” policy whereby at the end of a day, the Inbox is basically empty, with everything either delegated, answered, or set to answer later.

We also suggest creating subfolders in the main account (keeping them on the mail server, not local to a computer, so that they can be searched and accessed from anywhere) as follows:

  • • Collaborators (emails stating what they are going to do or updating on recent status)
  • • Grants in play (emails from funding agencies confirming receipt)
  • • Papers in play (emails from journals confirming receipt)
  • • Waiting for information (emails from people for whom you are waiting for information)
  • • Waiting for miscellaneous (emails from people who you expect to do something)
  • • Waiting for reagents (emails from people confirming that they will be sending you a physical object)

Incoming emails belonging to those categories (for example, an email from an NIH program officer acknowledging a grant submission, a collaborator who emailed a plan of what they will do next, or someone who promised to answer a specific question) should be sorted from the Inbox to the relevant folder. Every couple of weeks (according to a calendar reminder), those folders should be checked, and those items that have since been dealt with can be saved to a Saved Messages folder archive, whereas those that remain can be Replied to as a reminder to prod the relevant person.

In addition, as most researchers now exchange a lot of information via email, the email trail preserves a record of relationships among colleagues and collaborators. It can be extremely useful, even years later, to be able to go back and see who said what to whom, what was the last conversation in a collaboration that stalled, who sent that special protocol or reagent and needs to be acknowledged, etc. It is imperative that you know where your email is being stored, by whom, and their policy on retention, storage space limits, search, backup, etc. Most university IT departments keep a mail server with limited storage space and will delete your old emails (even more so if you move institutions). One way to keep a permanent record with complete control is with an application called MailSteward Pro. This is a front-end client for a freely available MySQL server, which can run on any machine in your laboratory. It will import your mail and store unlimited quantities indefinitely. Unlike a mail server, this is a real database system and is not as susceptible to data corruption or loss as many other methods.

A suggested strategy is as follows. Keep every single email, sent and received. Every month (set a timed reminder), have MailSteward Pro import them into the MySQL database. Once a year, prune them from the mail server (or let IT do it on their own schedule). This allows rapid search (and then reply) from inside a mail client for anything that is less than one year old (most searches), but anything older can be found in the very versatile MailStewardPro Boolean search function. Over time, in addition to finding specific emails, this allows some informative data mining. Results of searches via MailStewardPro can be imported into Excel to, for example, identify the people with whom you most frequently communicate or make histograms of the frequency of specific keywords as a function of time throughout your career.

With ideas, mind maps, and the necessary information in hand, one can consider what aspects of the current operations plan can be changed to incorporate plans for new, impactful activity.

Organizing Tasks and Planning

A very useful strategy involves breaking down everything according to the timescales of decision-making, such as in the Getting Things Done (GTD) philosophy ( Figure 4 ) ( Allen, 2015 ). Activities range from immediate (daily) tasks to intermediate goals all the way to career-scale (or life-long) mission statements. As with mind maps, being explicit about these categories not only force one to think hard about important aspects of their work, but also facilitate the transmission of this information to others on the team. The different categories are to be revisited and revised at different rates, according to their position on the hierarchy. This enables you to make sure that effort and resources are being spent according to priorities.

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Scales of Activity Planning

Activities should be assigned to a level of planning with a temporal scale, based on how often the goals of that level get re-evaluated. This ranges from core values, which can span an entire career or lifetime, all the way to tactics that guide day-to-day activities. Each level should be re-evaluated at a reasonable time frame to ensure that its goals are still consistent with the bigger picture of the level(s) above it and to help re-define the plans for the levels below it.

We also strongly recommend a yearly personal scientific retreat. This is not meant to be a vacation to “forget about work” but rather an opportunity for freedom from everyday minutiae to revisit, evaluate, and potentially revise future activity (priorities, action items) for the next few years. Every few years, take more time to re-map even higher levels on the pyramid hierarchy; consider what the group has been doing—do you like the intellectual space your group now occupies? Are your efforts having the kind of impact you realistically want to make? A formal diagram helps clarify the conceptual vision and identify gaps and opportunities. Once a correct level of activity has been identified, it is time to plan specific activities.

A very good tool for this purpose, which enables hierarchical storage of tasks and subtasks and their scheduling, is OmniFocus ( Figure 5 ). OmniFocus also enables inclusion of files (or links to files or links to Evernote notes of information) together with each Action. It additionally allows each action to be marked as “Done” once it is complete, providing not only a current action plan but a history of every past activity. Another interesting aspect is the fact that one can link individual actions with specific contexts: visualizing the database from the perspective of contexts enables efficient focus of attention on those tasks that are relevant in a specific scenario. OmniFocus allows setting reminders for specific actions and can be used for adding a time component to the activity.

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Project Planning

This figure shows a screenshot of the OmniFocus application, illustrating the nested hierarchy of projects and sub-projects, arranged into larger groups.

The best way to manage time relative to activity (and to manage the people responsible for each activity) is to construct Gantt charts ( Figure 6 ), which can be used to plan out project timelines and help keep grant and contract deliverables on time. A critical feature is that it makes dependencies explicit, so that it is clear which items have to be solved/done before something else can be accomplished. Gantt charts are essential for complex, multi-person, and/or multi-step projects with strict deadlines (such as grant deliverables and progress reports). Software such as OmniPlanner can also be used to link resources (equipment, consumables, living material, etc.) with specific actions and timelines. Updating and evaluation of a Gantt chart for a specific project should take place on a time frame appropriate to the length of the next immediate phase; weekly or biweekly is typical.

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Timeline Planning

This figure shows a screenshot of a typical Gantt chart, in OmniPlan software, illustrating the timelines of different project steps, their dependencies, and specific milestones (such as a due date for a site visit or grant submission). Note that Gantt software automatically moves the end date for each item if its subtasks' timing changes, enabling one to see a dynamically correct up-to-date temporal map of the project that adjusts for the real-world contingencies of research.

In addition to the comprehensive work plan in OmniFocus or similar, it is helpful to use a Calendar (which synchronizes to a server, such as Microsoft Office calendar with Exchange server). For yourself, make a task every day called “Monday tasks,” etc., which contains all the individual things to be accomplished (which do not warrant their own calendar reminder). First thing in the morning, one can take a look at the day's tasks to see what needs to be done. Whatever does not get done that day is to be copied onto another day's tasks. For each of the people on your team, make a timed reminder (weekly, for example, for those with whom you meet once a week) containing the immediate next steps for them to do and the next thing they are supposed to produce for your meeting. Have it with you when you meet, and give them a copy, updating the next occurrence as needed based on what was decided at the meeting to do next. This scheme makes it easy for you to remember precisely what needs to be covered in the discussion, serves as a record of the project and what you walked about with whom at any given day (which can be consulted years later, to reconstruct events if needed), and is useful to synchronize everyone on the same page (if the team member gets a copy of it after the meeting).

Writing: The Work Products

Writing, to disseminate results and analysis, is a central activity for scientists. One of the OmniFocus library's sections should contain lists of upcoming grants to write, primary papers that are being worked on, and reviews/hypothesis papers planned. Microsoft Word is the most popular tool for writing papers—its major advantage is compatibility with others, for collaborative manuscripts (its Track Changes feature is also very well implemented, enabling collaboration as a master document is passed from one co-author to another). But Scrivener should be seriously considered—it is an excellent tool that facilitates complex projects and documents because it enables WYSIWYG text editing in the context of a hierarchical structure, which allows you to simultaneously work on a detailed piece of text while seeing the whole outline of the project ( Figure 7 ).

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Writing Complex Materials

This figure shows a screenshot from the Scrivener software. The panel on the left facilitates logical and hierarchical organization of a complex writing project (by showing where in the overall structure any given text would fit), while the editing pane on the right allows the user to focus on writing a specific subsection without having to scroll through (but still being able to see) the major categories within which it must fit.

It is critical to learn to use a reference manager—there are numerous ones, including, for example, Endnote, which will make it much easier to collaborate with others on papers with many citations. One specific tip to make collaboration easier is to ask all of the co-authors to set the reference manager to use PMID Accession Number in the temporary citations in the text instead of the arbitrary record number it uses by default. That way, a document can have its bibliography formatted by any of the co-authors even if they have completely different libraries. Although some prefer collaborative editing of a Google Doc file, we have found a “master document” system useful, in which a file is passed around among collaborators by email but only one can make (Tracked) edits at a time (i.e., one person has the master doc and everyone makes edits on top of that).

One task most scientists regularly undertake is writing reviews of a specific subfield (or Whitepapers). It is often difficult, when one has an assignment to write, to remember all of the important papers that were seen in the last few years that bear on the topic. One method to remedy this is to keep standing document files, one for each topic that one might plausibly want to cover and update them regularly. Whenever a good paper is found, immediately enter it into the reference manager (with good keywords) and put a sentence or two about its main point (with the citation) into the relevant document. Whenever you decide to write the review, you will already have a file with the necessary material that only remains to be organized, allowing you to focus on conceptual integration and not combing through literature.

The life cycle of research can be viewed through the lens of the tools used at different stages. First there are the conceptual ideas; many are interconnected, and a mind mapper is used to flesh out the structure of ideas, topics, and concepts; make it explicit; and share it within the team and with external collaborators. Then there is the knowledge—facts, data, documents, protocols, pieces of information that relate to the various concepts. Kept in a combination of Endnote (for papers), Evernote (for information fragments and lists), and file system files (for documents), everything is linked and cross-referenced to facilitate the projects. Activities are action items, based on the mind map, of what to do, who is doing what, and for which purpose/grant. OmniFocus stores the subtasks within tasks within goals for the PI and everyone in the laboratory. During meetings with team members, these lists and calendar entries are used to synchronize objectives with everyone and keep the activity optimized toward the next step goals. The product—discovery and synthesis—is embodied in publications via a word processor and reference manager. A calendar structure is used to manage the trajectory from idea to publication or grant.

The tools are currently good enough to enable individual components in this pipeline. Because new tools are continuously developed and improved, we recommend a yearly overview and analysis of how well the tools are working (e.g., which component of the management plan takes the most time or is the most difficult to make invisible relative to the actual thinking and writing), coupled to a web search for new software and updated versions of existing programs within each of the categories discussed earlier.

A major opportunity exists for software companies in the creation of integrated new tools that provide all the tools in a single integrated system. In future years, a single platform will surely appear that will enable the user to visualize the same research structure from the perspective of an idea mind map, a schedule, a list of action items, or a knowledge system to be queried. Subsequent development may even include Artificial Intelligence tools for knowledge mining, to help the researcher extract novel relationships among the content. These will also need to dovetail with ELN platforms, to enable a more seamless integration of project management with primary data. These may eventually become part of the suite of tools being developed for improving larger group dynamics (e.g., Microsoft Teams). One challenge in such endeavors is ensuring the compatibility of formats and management procedures across groups and collaborators, which can be mitigated by explicitly discussing choice of software and process, at the beginning of any serious collaboration.

Regardless of the specific software products used, a researcher needs to put systems in place for managing information, plans, schedules, and work products. These digital objects need to be maximally accessible and backed up, to optimize productivity. A core principle is to have these systems be so robust and lightweight as to serve as an “external brain” ( Menary, 2010 )—to maximize creativity and deep thought by making sure all the details are recorded and available when needed. Although the above discussion focused on the needs of a single researcher (perhaps running a team), future work will address the unique needs of collaborative projects with more lateral interactions by significant numbers of participants.

Acknowledgments

We thank Joshua Finkelstein for helpful comments on a draft of the manuscript. M.L. gratefully acknowledges support by an Allen Discovery Center award from the Paul G. Allen Frontiers Group (12171) and the Barton Family Foundation.

  • Allen D. Revised edition. Penguin Books; 2015. Getting Things Done: The Art of Stress-free Productivity. [ Google Scholar ]
  • Altshuller G.S. Gordon and Breach Science Publishers; 1984. Creativity as an Exact Science: The Theory of the Solution of Inventive Problems. [ Google Scholar ]
  • Menary R. MIT Press; 2010. The Extended Mind. [ Google Scholar ]

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  • Published: 18 April 2024

A method for identifying different types of university research teams

  • Zhe Cheng   ORCID: orcid.org/0009-0002-5120-6124 1 ,
  • Yihuan Zou 1 &
  • Yueyang Zheng   ORCID: orcid.org/0000-0001-7751-2619 2  

Humanities and Social Sciences Communications volume  11 , Article number:  523 ( 2024 ) Cite this article

Metrics details

Identifying research teams constitutes a fundamental step in team science research, and universities harbor diverse types of such teams. This study introduces a method and proposes algorithms for team identification, encompassing the project-based research team (Pbrt), the individual-based research team (Ibrt), the backbone-based research group (Bbrg), and the representative research group (Rrg), scrutinizing aspects such as project, contribution, collaboration, and similarity. Drawing on two top universities in Materials Science and Engineering as case studies, this research reveals that university research teams predominantly manifest as backbone-based research groups. The distribution of members within these groups adheres to Price’s Law, indicating a concentration of research funding among a minority of research groups. Furthermore, the representative research groups in universities exhibit interdisciplinary characteristics. Notably, significant differences exist in collaboration mode and member structures among high-level backbone-based research groups across diverse cultural backgrounds.

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Introduction

Team science has emerged as a burgeoning field of inquiry, attracting the attention of numerous scholars (e.g., Stokols et al., 2008 ; Bozeman & Youtie, 2018 ; Coles et al., 2022 ; Deng et al., 2022 ; Forscher et al., 2023 ), who endeavor to explore and try to summarize strategies for fostering effective research teams. Conducting team science research would help improve team efficacy. The National Institutes of Health in the USA pointed out that team science is a new interdisciplinary field that empirically examines the processes by which scientific teams, research centers, and institutes, both large and small, are structured (National Research Council, 2015 ). In accordance with this conceptualization, research teams can be delineated into various types based on their size and organizational form. Existing research also takes diverse teams as focal points when probing issues such as team construction and team performance. For example, Wu et al. ( 2019 ) and Abramo et al. ( 2017 ) regard the co-authors of a single paper as a team, discussing issues of research team innovation and benefits. Meanwhile, Zhao et al. ( 2014 ) and Lungeanu et al. ( 2014 ) consider the project members as a research team, exploring issues such as internal interest distribution and team performance. Boardman and Ponomariov ( 2014 ), Lee et al. ( 2008 ), and Okamoto and Centers for Population Health and Health Disparities Evaluation Working Group ( 2015 ) view the university’s research center as a research group, investigating themes about member collaboration, management, and knowledge management portals.

Regarding the definition of research teams, some researchers believe that a research team is a collection of people who work together to achieve a common goal and discover new phenomena through research by sharing information, resources, and professional expertise (Liu et al., 2020 ). Conversely, others argue that groups operating across distinct temporal and spatial contexts, such as virtual teams, do not meet the criteria for teams, as they engage solely in collaborative activities between teams. According to this perspective, Research teams should be individuals collaborating over an extended period (typically exceeding six months) (Barjak & Robinson, 2008 ). Contemporary discourse on team science tends to embrace a broad conceptualization wherein research teams include both small-scale teams comprising 2–10 individuals and larger groups consisting of more than 10 members (National Research Council, 2015 ). These research teams are typically formed to conduct a project or finish research papers, while research groups are formed to solve complex problems, drawing members from diverse departments or geographical locations.

Obviously, different research inquiries are linked to different types of research teams. Micro-level investigations, such as those probing the impact of international collaboration on citations, often regard co-authors of research papers as research teams. Conversely, meso-level inquiries, including those exploring factors impacting team organization and management, often view center-based researchers as research groups. Although various approaches can be adopted to identify research teams, such as retrieving names from research centers’ websites or obtaining lists of project-funded members, when the study involves a large sample size and requires more data to measure the performance of research teams, it becomes necessary to use bibliometric methods for team identification.

Existing literature on team identification uses social network analysis (Zhang et al., 2019 ), cohesive subgroup (Dino et al., 2020 ), faction algorithm (Imran et al., 2018 ), FP algorithm (Liao, 2018 ), etc. However, these identification methods often target a singular type of research team or fail to categorize the identified research teams. Moreover, existing studies mostly explore the evolution of specific disciplines (Wang et al., 2017 ), with limited attention devoted to identifying university research teams and the influencing factors of team effectiveness. Therefore, this study tries to develop algorithms to identify diverse university research teams, drawing insights from two universities characterized by different cultural backgrounds. It aims to address two research questions:

How can we identify different types of university research teams?

What are the characteristics of research groups within universities?

Literature review

Why is it necessary to identify research teams? The research focuses on scientific research teams, mostly first identifying the members of research teams through their names on the list of funding projects or institutions’ websites and then conducting research through questionnaires or interviews. However, this methodology may compromise research validity for several reasons. Firstly, the mere inclusion of individuals on funding project lists does not guarantee genuine research team membership or substantive collaboration among members. Secondly, the institutional website generally announces important research team members, potentially overlooking auxiliary personnel or important members from external institutions. Thirdly, reliance solely on lists of research team members fails to capture nuanced information about the team, such as their research ability or communication intensity, thus hindering the exploration of team science-related issues.

Consequently, researchers have turned to co-authorship and citation to identify research teams using established software tools and customized algorithms. For example, Li and Tan ( 2012 ) applied UCINET and social network analysis to identify university research teams, while Hu et al. ( 2019 ) used Citespace to analyze research communities of four disciplines in China, the UK, and the US. Similarly, some researchers also identify the members and leaders of research teams by using and optimizing existing algorithms. For example, Liao ( 2018 ) applied the Fast-Unfolding algorithm to identify research teams in the field of solar cells, while Yu et al. ( 2020 ) and Li et al. ( 2017 ) employed the Louvain community discovery algorithm to identify research teams in artificial intelligence. Lv et al. ( 2016 ) applied the FP-GROWTH algorithm to identify core R&D teams. Yu et al. ( 2018 ) used the faction algorithm to identify research teams in intelligence. Dino et al. ( 2020 ) developed the CL-leader algorithm to confirm research teams and their leaders. Boyack and Klavans ( 2014 ) regard researchers engaged in the same research topic as research teams based on citation information. Notably, these community detection algorithms complement each other, offering versatile tools for identifying research teams.

Despite the utility of these identification methods, they are not without limitations. For example, fixed software algorithms are constrained by predefined rules, posing challenges for researchers seeking to customize identification criteria. Moreover, for developed algorithms, although algorithms based on computer programming languages have high accuracy, they overemphasize the connection relationship between members and do not consider the definition of research teams. In addition, research based on co-authorship networks and community identification algorithms faces inherent problems: (1) Ensuring temporal consistency in co-authorship networks is challenging due to variations in publication timelines, potentially undermining the temporal alignment of team member collaborations; (2) The lack of stability in team identification result means that different identification standards would produce different outcomes; (3) Team members only belong to one research team, but in the actual process, researchers often participate in multiple research teams with different identities, or the same members conduct research in different team combinations.

In summary, research teams in a specific field can be identified using co-authorship information, designing or introducing identification algorithms. However, achieving more accurate identification necessitates consideration of the nuanced definition of research teams. Therefore, this study focuses on university research teams, addressing temporal and spatial collaboration issues among team members by incorporating project information and first-author information. Furthermore, it tackles the issue of classifying research team members by introducing Price’s Law and Everett’s Rule. Additionally, it tackles the issue of team members’ multiple affiliations through the Jaccard Similarity Coefficient and the Louvain Algorithm. Ultimately, this study aims to achieve the classification recognition of university research teams.

Team identification method

An effective team identification method requires both consideration of the definition of research teams and the ability to transform this definition into operable programming languages. University research teams, by definition, comprise researchers collaborating towards a shared objective. As a typical form of the output of a research team, the co-authorship of a scientific research paper implies information exchange and interaction among team members. Thus, this study uses co-authorship relationships within papers to reflect the collaborative relationships among research team members. In this section, novel algorithms for identifying research teams are proposed to address deficiencies observed in prior research.

Classification of research team members

A researcher might be part of multiple research teams, with varying roles within each. Members of the research team can be categorized according to how the research team is defined.

The original idea of team member classification

The prevailing notion of teams underscores the collaborative efforts between individual team members and their contributions toward achieving research objectives. This study similarly classifies team members based on these dual dimensions.

In terms of overall contributions, members who make substantial contributions are typically seen as pivotal figures within the research team, providing the primary impetus for the team’s productivity. Conversely, those with lesser input only contribute to specific facets of the team’s goals and engage in limited research activities, thus being regarded as standard team members.

In terms of collaboration, it is essential to recognize that high levels of contribution do not inherently denote a core position within a team. The collaboration among team members serves as an important indicator of their identity characteristics within the research team. Based on the collaboration between members, this study believes that researchers who have high contributions and collaborate with many high-contribution team members assume the core members of the research team. Conversely, members who have high contributions but only collaborate with a limited number of high-contribution team members are identified as backbone members. Similarly, members displaying low levels of contributions but collaborating widely with high contributors are categorized as ordinary members. Conversely, those with low contributions and limited collaboration with high-contributing team members are regarded as marginal members of the research team.

Establishment of team member classification criteria

This study introduces Price’s Law and Everett’s Rule to realize the idea of team member classification.

In terms of overall contribution, the well-known bibliometrics Price, drawing from Lotka’s Law, deduced that the number of papers published by prolific scientists is 0.749 times the square root of the number of papers published by the most prolific scientist in a group. Existing research also used this law when analyzing prolific authors of an organization. This study believes that prolific authors who conform to Price’s Law are important members who contribute more to the research team.

In terms of collaboration, existing research mostly employs the concept of factions. Factions refer to a relationship where members reciprocate and cannot readily join new groups without altering the reciprocal nature of their factional ties. However, in real-world settings, relationships with overtly reciprocal characteristics are uncommon. Therefore, to ensure the applicability and stability of the faction, Seidman and Foster ( 1978 ) proposed the concept of K-plex, pointing out that in a group of size n, when the number of direct connections of any point in the group is not less than n-k, this group is called k-plex. For k-plex, as the number k increases, the stability of the entire faction will decrease. Addressing this concern, renowned sociologist Martin Everett ( 2002 ), based on the empirical rule of research, proposed specific values for k and corresponding minimum group sizes, stipulating that the overall team size should not fall below 2k-1 (Scott, 2017 ). The expression is:

In other words, for a K-plex, the most acceptable definition to qualify as a faction is when each member of the team is directly connected to at least ( n  − 1)/2 members of the team. Applied to research teams, this empirical guideline necessitates that team members maintain collaborative ties with at least half or more of the team.

Based on Price’s Law and Everett’s Empirical Rule, this study gives the criteria for distinguishing prolific authors, core members, backbone members, ordinary members, and marginal members of research teams. The specifics are shown in the following Table 1 .

Classification of research teams

Within universities, a diverse array of research teams exists, categorized by their scale, the characteristics of funded projects, and the platforms they rely upon. This study proposes the identification algorithms for project-based teams, individual-based teams, backbone-based groups, and representative groups.

Project-based research teams: identification based on research projects

Traditional methods for identifying research teams attribute co-authorship to collaboration among multiple authors without considering the time scope. However, in practice, collaborations vary in content and duration. Therefore, in the identification process, it is necessary to introduce appropriate standards to distinguish varying degrees of collaboration and content among scholars.

Research projects serve as evidence of researchers engaging in the same research topic, thereby indicating that the paper’s authors belong to the same research team. Upon formal acceptance of a research paper, authors typically append funding information to the paper. Therefore, papers sharing the same funding information can be aggregated into paper clusters to identify the research team members who completed the fund project. The specific steps proposed for identifying a single research project fund are as follows.

Firstly, extract the funding number and regard all papers attached with the same funding number as a paper cluster. Secondly, construct a co-authorship network based on the paper cluster. Thirdly, identify the research team using the team member classification criteria.

Individual-based research teams: team identification based on the first author

For research papers lacking project numbers, clustering can be performed based on the contribution and research experience of the authors. Each co-author of the research paper contributes differently to the paper’s content. In 2014, the Consortia Advancing Standards in Research Administration Information (CASRAI) proposed classification standards for paper contributions, including 14 types such as conceptualization, data processing, formal analysis, funding acquisition, investigation, methods, project management, resources, software, supervision, validation, visualization, paper writing, review, and editing.

In this study, the primary author of a paper lacking project funding is considered the initiator, while other authors are seen as contributors who advance and finalize the research. For papers not affiliated with any project, the first author and all their published papers form a paper group for team identification purposes. The procedure entails the following steps: Initially, gather the first author and all papers authored by them within the identification period to constitute a paper group. Subsequently, a co-authorship network will be constructed using the papers within the group. Lastly, the research team will be identified based on the criteria for classifying team members.

Backbone-based research group: merging based on project-based and individual-based research teams

Research teams can be identified either by a single project number or by individual researchers. Upon identification, it becomes evident that many research teams share similar members. This is because a research team may engage in multiple projects, and some members collaborate without funding support. While identification algorithms are suitable for evaluating the quality of a research article or funding, they may not suffice when assessing the research group, or they may not suffice when assessing the key factors affecting their performance. To address this, it is necessary to merge highly similar individual-based or project-based research teams according to specific criteria. The merged one should be termed a group, as it encompasses multiple project-based and individual-based research teams.

In the pursuit of building world-class universities, governments worldwide often emphasize the necessity of fostering research teams led by discipline backbones. In this vein, this study further develops a backbone-based research group identification algorithm, which considers project-based and individual-based research teams.

Identification of university discipline backbone members

Previous studies have summarized the characteristics of the university discipline backbones, revealing that these individuals often excel in indicators such as degree centrality, eigenvector centrality, and betweenness centrality. Each centrality indicator demonstrates a strong positive correlation with the author’s output volume, indicating that high-productive researchers with more collaborators are more inclined to be university discipline backbones. Based on these characteristics, Price’s law is applied, defining discipline backbone members as researchers whose publications count exceeds 0.749 times the square root of the highest publication count within the discipline.

Team identification with discipline backbone members as the Core

Following the identification of discipline backbones, this study consolidates paper groups wherein the discipline backbone serves as the core member of either individual-based or project-based research teams. Subsequently, backbone-based research groups are formed.

Merging based on similarity perspective

It should be noted that different discipline backbones may simultaneously participate as core members in the same individual-based or project-based research teams. Consequently, distinct backbone-based research groups may encompass duplicate project-based and individual-based research teams, necessitating the merging of backbone-based research groups.

To address this redundancy issue, this study introduces the concept of similarity in community identification. In the community identification process, existing algorithms often assess whether to incorporate members into the community based on their level of similarity. Among various algorithms for calculating similarity, the Jaccard coefficient is deemed to possess superior validity and robustness in merging nodes within network communities (Wang et al., 2020 ). Its calculation formula is as follows.

N i denotes the nodes within subset i , while N j represents the nodes within subset j ; N i  ∩ N j signifies the nodes present in both subsets, whereas N i ∪ N j encompasses all nodes in subsets i and j . Existing research shows that when the Jaccard coefficient equals or exceeds 0.5 (Guo et al., 2022 ), the community identification algorithm achieves optimal precision.

In the context of this study, N i represents the core and backbone members of research group i , while N j denotes the core and backbone members of research group j . If these two groups exhibit significant overlap in core and backbone members, the papers from both research groups are merged into a new set of papers to identify the research team.

Given the efficacy of the Jaccard similarity measure in identifying community networks and merging, this study employs this principle to merge backbone-based research groups. Specifically, groups are merged if the Jaccard similarity coefficient between their core and backbone members equals or exceeds 0.5. Subsequently, new research groups are formed based on the merged set of papers.

It’s important to note that during the merging process, certain research teams within a backbone-based group may be utilized multiple times. Initially, the merging occurs based on the core and backbone members of the backbone-based research group, adhering to the Jaccard coefficient criterion. However, since project or individual-based research teams within a backbone-based research group may be reused, resulting in the similarity of research papers across different groups, the study further tested the team duplication of the merged papers of various groups. During the research process, it was found that the research papers within groups often exhibit similarity due to their association with multiple funding projects. Therefore, a principle of “if connected, then merged” was adopted among groups with highly similar research papers to ensure the heterogeneity of papers within the final merged research groups.

The generation process of the backbone-based research groups is illustrated in Fig. 1 below. Initially, university discipline backbones α, β, γ, θ, δ, and ε are each designated as core members within project-based or individual-based research teams A, B, C, D, E, and F, among which αβγ, γθ, θδ, δε ‘s core and backbone members’ Jaccard coefficient meet the merging standard and generate lines. After the first merging, the Jaccard coefficient of the papers of the αβγ, γθ, θδ, δε are calculated, and the lines are generated because of a high duplicated papers between γθ, θδ, and θδ, δε. Finally, αβγ and γθδε are retained based on the rule.

figure 1

The α, β, γ, θ, δ, and ε are core members within project-based or individual-based research teams. The A, B, C, D, E, and F are project-based or individual-based research teams. From step 1 to step 2, research groups are merged according to the Jaccard coefficient between research team members. From step 2 to step 3, research groups are merged according to the Jaccard coefficient between research group papers.

In summary, the process of identifying a backbone-based research group involves the following steps: (1) Identify prolific authors within the university’s discipline by analyzing all papers published in the field, considering them as the discipline’s backbones members; (2) Merge the project-based and individual-based research teams wherein university discipline backbones are core member, thereby forming backbone-based research groups; (3) Merge the backbone-based research group identified in step (2) based on the Jaccard coefficient between their core and backbone members; (4) Calculate the Jaccard coefficient of the papers of the merged groups in step (3), merge the groups with significant paper overlap, and generate new backbone-based research groups.

The research groups identified through the above steps offer two advantages: Firstly, they integrate similar project-based and individual-based research teams, avoiding redundancy in team identification outcomes. Secondly, the same member may participate in different research teams, assuming distinct roles within each, thus better reflecting the complexity of scientific research practices.

Representative team: consolidation via backbone-based research group

When universities introduce their research groups to external parties, they typically highlight the most significant research members within the institution. Although the backbone-based research group has condensed the project-based and individual-based research teams, there may still be some overlap among members from different backbone-based research groups.

In order to create condensed and representative research groups that accurately reflect the development of the university’s discipline, this study extracts the core and backbone members identified in the backbone-based research group. It then identifies the representative group using the widely utilized Louvain algorithm (Blondel et al., 2008 ) commonly employed in research group identification. This algorithm facilitates the integration of important members from different backbone-based research groups while ensuring there is no redundancy among group members. The merging process is shown in Fig. 2 .

figure 2

Each pass is made of two phases: one where modularity is optimized by allowing only local changes of communities, and one where the communities found are aggregated in order to build a new network of communities. The passes are repeated iteratively until no increase in modularity is possible.

Research team identification process and its pros and cons

Overall, the method of identifying university research teams proposed in this research encompasses four stages: Initially, research teams are categorized into project-based research teams and individual-based research teams based on information provided with research papers, distinguishing between those supported by funding projects and those not. Subsequently, the prolific authors of universities are identified to combine individual-based and project-based research teams, and backbone-based research groups are generated. Finally, representative research groups are established utilizing the Louvain algorithm and the interrelations among members within the backbone-based research groups. The entire process is depicted in Fig. 3 below.

figure 3

Different university research teams are identified at different stage.

Each type of research team or group has its advantages and disadvantages, as shown in Table 2 below.

Validation of identification results

In order to verify the accuracy of the identification results, the method proposed by Boyack and Klavans ( 2014 ), which relies on citation analysis, is utilized. This method calculates the level of consistency regarding the main research areas of the core and backbone members, thereby verifying the validity of the identification method.

In the SCIVAL database, all research papers are clustered into relevant topic groups, providing insights into the research area of individual authors. By examining the research topic clusters of team papers in the SCIVAL database, the predominant research areas of prolific authors can be determined. Authors sharing common research areas within a university are regarded as constituting a research team. Given that authors often conduct research in various research areas, this study focuses solely on the top three research areas for each author.

As demonstrated in Table 3 below, for the prolific authors A, B, C, D, and E of the research team, their top three research areas collectively span five distinct fields. By calculating the highest value of the consistency among these research areas, it can be judged whether these researchers can be classified as members of the same research group. As depicted in Table 3 , the main research areas of all prolific authors include Research Area 3, indicating that this field is one of the three most important research areas for all prolific authors. This consistency validates that the main research areas of the five authors align, affirming their classification within the same research team.

Data collection and preprocessing

In order to present the distinct characteristics of various types of scientific research teams as intuitively as possible, this study focuses on the field of material science, with Tsinghua University and Nanyang Technological University selected for analysis. The selection of these two institutions is driven by several considerations: (1) both universities boast exceptional performance in the field of material science on a global scale, consistently ranking within the top 10 worldwide for numerous years; (2) The scientific research systems in the respective countries where these universities are situated differ significantly. China’s scientific research system operates under a government-led funding model, whereas Singapore’s system involves a multi-party funding approach with contributions from the government, enterprises, and societies. By examining universities from these distinct scientific research cultures, this study aims to validate the proposed methods and highlight disparities in the characteristics of their scientific research teams. (3) Material science is inherently interdisciplinary, with contributions from researchers across various domains. Although the selected papers focus on material science, they may also intersect with other disciplines. Therefore, investigating research teams in material science could somewhat represent the interdisciplinary research teams.

The data utilized in this study is sourced from the Clarivate Analytics database, which categorizes scientific research papers based on the subject classification catalogs. In order to ensure the consistency and reliability of scientific research paper identification, this study focuses on the papers published in the field of material science by the two selected universities between 2017 and 2021. Additionally, considering the duration of funded projects, papers associated with projects that have appeared in 2017–2021 within ten years (2011–2022) are also included for analysis to enhance the precision of identification. In order to ensure the affiliation of a research team with the respective universities, this study exclusively considers papers authored by the first author or the corresponding author affiliated with the university as the subject of analysis.

Throughout this process, it should be noted that the name problem in identifying scientific research. Abbreviations, orders, and other name-related information are cleaned and verified. Given that this study exports data utilizing the Author’s Full name and restricts it to specific universities and disciplines, the cleaning process targets the rectification of identification discrepancies arising from a minority of abbreviations and similar names. The specific cleaning procedures entail the following steps.

First, all occurrences of “-” are replaced with null values, and names are standardized by capitalization. Second, the Python dedupe module is employed to mitigate ambiguity in author names, facilitating the differentiation or unification of authors sharing the same surname, name, and initials. List and output all personnel names of each university in this discipline and observe in ascending order. Third, a comparison of names and abbreviations is conducted in reverse order, alongside their respective affiliations and replacements in the identification data. For example, names such as “LONG, W.H” “LONG, WEN, HUI” and “LONG, WENHUI” are uniformly replaced with “LONG, WENHUI.” Fourth, identify and compare similar names in both abbreviations and full forms and confirm whether they are consistent by scrutinizing their affiliations and collaborators. Names exhibiting consistency are replaced accordingly, while those lacking uniformity remain unchanged. For example, “LI, W.D” and “LI, WEIDE” lacking common affiliations and collaborators, are not considered the same person and thus remain distinct.

The publication of the two universities in the field of Materials Science and Engineering across two distinct time periods is shown in Table 4 below.

Based on the publication count of papers authored by the first author or corresponding author from both universities, Tsinghua University demonstrates a significantly higher publication output than Nanyang Technological University, indicating a substantial disparity between the two institutions.

Subsequent to data preprocessing, this study uses the Python tool to develop algorithms in accordance with the proposed principles, thereby facilitating the identification of research teams and groups.

This study has identified several research teams through the sorting and analysis of original data. In order to provide a comprehensive overview of the identification results, this study begins by outlining the characteristics of the identification results and then analyzes the research teams affiliated with both universities, focusing on three aspects: scale, structure, and output.

Identification results of university research teams

The results reveal that both Tsinghua University and Nanyang Technological University boast a considerable number of Pbrts, indicating that most of the researchers from both universities have received funding support. Additionally, a small number of teams have not received funding support, although their overall proportion is relatively low. The Bbrgs predominantly encompass the majority of the Ibrts and Pbrts, underscoring the significant influence of the discipline backbone members within both universities. Notably, the total count of Rrg across the two universities stands at 39, reflecting that many research groups are supporting the construction of material disciplines in the two universities (Table 5 ).

In order to validate the accuracy of the developed method, this study verifies the effectiveness of the identification algorithm. Given that the method emphasizes the main research area of its members, it is appropriate to apply it to the verification of the Bbrgs, which encompass the majority of the individual-based and project-based teams.

The analysis reveals that the consistency level of the most concentrated research area within the identified Bbrgs is 0.93. This signifies that within a Bbrg comprising 10 core or backbone members, a minimum of 9.3 individuals share the same main research area. Moreover, across Bbrgs of varying sizes, the average consistency level of the most concentrated research area also reached 0.90, indicating that the algorithm proposed in this study is valid (Table 6 ).

Analysis of the characteristics of Bbrg in universities

The findings of the analysis show that the Bbrgs encompass the vast majority of Pbrts and Ibrts within universities. Consequently, this study further analyzes the scale, structure, and output of the Bbrgs to present the characteristics of university research teams.

Group scale

Upon scrutinizing the distribution of Bbrgs across the two universities, it is observed that the number of core members is similar. Bbrg with a core member scale of 6–10 individuals are the most prevalent, followed by those with a scale of 0–5 members. Additionally, there are Bbrgs comprising 11–15 members, with relatively fewer Bbrgs consisting of 15 members or more. On average, the number of core members in Bbrgs stands at 7.08. Tsinghua University has more Bbrgs than Nanyang Technological University, while the average number of core members is relatively less. Notably, the proportion of core and backbone members amounts to nearly 12%, ranging from 11.22% to 13.88% (Table 7 ).

Group structure

The structural attributes of the research groups could be assessed through network density among core members, core and backbone members, and all team members. Additionally, departmental distribution can be depicted based on the identification of core members and their organizational affiliations. The formula for network density calculation is as follows:

Note : R is the number of relationships, and N is the number of members.

Overall, the network density characteristics exhibit consistency across both universities. Specifically, the network density among research group members tends to decrease as the group size expands. The network density among core members is the highest, while that among all members records the lowest. Comparatively, the average amount of various types of network density at Tsinghua University is relatively lower than that at Nanyang Technological University, indicating a lesser degree of connectivity among members within Tsinghua University’s research group. However, the network density levels among core members and core and backbone members of research teams in both institutions remain relatively high. Notably, the network density of backbone-based research groups exceeds 0.5, indicating a close collaboration among the core and backbone members of these university research groups (Table 8 ).

The T-test analysis reveals no significant difference in the network density among core members between Tsinghua University and Nanyang Technological University. This suggests that core members of research groups from universities with high-level discipline often maintain close communication. However, concerning the network density among core and backbone members and all members, the average amount of Tsinghua University’s research groups is significantly lower than those of Nanyang Technological University. This implies less direct collaboration among prolific authors at Tsinghua University, with backbone members relying more on different core members of the group to carry out research.

To present the cooperative relationship among the core and backbone members of the Bbrgs, the prolific authors associated with the backbone-based research groups are extracted. Subsequently, the representative research groups affiliated with Nanyang Technological University and Tsinghua University are identified using the fast-unfolding algorithm. The resultant collaboration network diagram among prolific authors is depicted in Fig. 4 , wherein each node color corresponds to different representative research groups of the respective universities.

figure 4

Nodes (author) and links (relation between different authors) with the same color could be seen as the same representative research group.

The network connection diagram of Nanyang Technological University illustrates the presence of 39 Rrgs, including Rrgs from the School of Materials Science and Engineering and the Singapore Centre for 3D Printing. Owing to the inherently interdisciplinary characteristics of the materials discipline, its research groups are not only distributed in the School of Materials Science and Engineering; other academic units also have research groups engaged in materials science research.

Further insights into the distribution of research groups can be gleaned by examining the departments to which the primary members belong. Counting the departmental affiliations of the members with the highest centrality in each representative team reveals that, among the 39 Rrgs, the School of Materials Science and Engineering and the College of Engineering boast the highest number of affiliations, with nine core members of the research groups coming from these two departments, Following closely is the School of Physical and Mathematical Sciences. Notably, entities external to the university, such as the National Institute of Education and the Singapore Institute of Manufacturing Technology, also host important representative groups, underscoring the interdisciplinarity nature of material science. The distribution of Rrgs affiliations is delineated in Table 9 .

Similar to Nanyang Technological University, Tsinghua University also exhibits tightly woven connections within its backbone-based research group in Materials Science and Engineering, comprising a total of 39 Rrgs. Compared with Nanyang Technological University, Tsinghua University boasts a larger cohort of core and backbone members. The collaboration network diagram of representative groups is shown below (Fig. 5 ).

figure 5

Similar to Nanyang Technological University, representative research groups at Tsinghua University are distributed in different schools within the institution, with the School of Materials being the directly related department. In addition, the School of Medicine and the Center for Brain-like Computing also conduct research related to materials science (Table 10 ).

By summarizing the departmental affiliations of the research groups, it becomes evident that the Rrgs in Materials Science and Engineering at these universities span various academic departments, reflecting the interdisciplinary characteristics of the field. The network density of the research groups is also calculated, with Nanyang Technological University exhibiting a higher density (0.028) compared to Tsinghua University (0.022), indicating tighter connections within the representative research groups at Nanyang Technological University.

Group output

In order to control the impact of scale, this study compares several metrics, including publication, publication per capita of core and backbone members, capita of the most prolific author within the groups, field-weighted citation impact, and citations per publication of Bbrgs at these two top universities.

Regarding publications, the average number and the T-test results show that Tsinghua University significantly outperforms Nanyang Technological University, suggesting that the Bbrgs and prolific authors affiliated with Tsinghua University are more productive in terms of research output.

However, in terms of field-weighted citation impact and citations per publication of the Bbrgs, the average number and the T-test results show that Tsinghua University is significantly lower than that of Nanyang Technological University, which indicates the research papers originating from the Bbrgs at Nanyang Technological University have a greater academic influence (see Table 11 ).

Typical cases

To intuitively present the research groups identified, this study has selected the two Bbrgs with the highest number of published papers at Tsinghua University and Nanyang Technological University for analysis, aiming to offer insights for constructing research teams.

Basic Information of the Bbrgs

Examining the basic information of the Bbrgs reveals that although Kang Feiyu’s group at Tsinghua University comprises fewer researchers than Liu Zheng’s group at Nanyang Technological University, Kang Feiyu’s group has a higher total number of published papers. In order to measure the performance of the research results of these two Bbrgs, the field-weighted citation impact of their research papers was queried using SCIVAL. The results showed that the field-weighted citation impact of Kang Feiyu’s group at Tsinghua University was higher, indicating a greater influence in the field of Materials Science and Engineering. Furthermore, the identity information of the two group leaders was compared. It was found that Kang Feiyu, in addition to being a professor at Tsinghua University, holds administrative positions as the dean of the Shenzhen Graduate School of Tsinghua University. Meanwhile, LIU, Zheng, mainly serves as the chairman of the Singapore Materials Society alongside his role as a professor (see Table 12 ).

Characteristics of team member network structure

In order to reflect the collaboration characteristics of research groups, this study calculates the network density of the two groups and utilizes VOSviewer to present the collaboration network diagrams of their members.

In terms of network density, both groups exhibit a density of 1 among core members, indicating that the collaboration between core members is tight. However, regarding the network density of core and backbone members, as well as all members, Liu Zheng’s group at Nanyang Technological University demonstrates a higher density. This indicates a stronger interconnectedness between the backbone and other members within the group (refer to Table 13 ).

For the co-authorship network diagram of group members, distinctive characteristics are observed between the two Bbrgs. In Kang Feiyu’s team, the core members exhibit prominence, with sub-team structures under evident each team member (Fig. 6 ). Conversely, while Liu Zheng’s team also features different core members, the centrality within each member is not obvious (Fig. 7 ).

figure 6

Nodes (author) and links (relation between different authors) with the same color could be seen as the same sub-team.

figure 7

Discussion and conclusion

Distinguishing different research teams constitutes the foundational stage in conducting team science research. In this study, we employ Price’s Law, Everett’s Rule, Jaccard Similarity Coefficient, and Louvain Algorithm to identify different research teams and groups in two world-leading universities specializing in Materials Science and Engineering. Through this exploration, we aim to explore the characteristics of research teams. The main findings are discussed as follows.

First, based on the co-authorship and project data from scholarly articles, this study develops a methodology for identifying research teams that distinguishes between different types of research teams or groups. In contrast to the prior identification method, our algorithms could identify different types of research teams and realize the member classification within research teams. This affords greater clarity regarding collaboration time and content among team members. The validation of identification results, conducted using the methodology proposed by Boyack and Klavans ( 2014 ), demonstrates the consistency of the main research areas among identified research group members. This validation shows the accuracy and efficacy of the research team identification methodology proposed in this study.

Second, universities have different types of research teams or groups, encompassing both project-based research teams and individual-based research teams lacking project support. Among these, most research teams rely on projects to conduct research (Bloch & Sørensen, 2015 ). Concurrently, this research finds that university research groups predominantly coalesce around eminent scholars, with backbone-based research groups comprising the majority of both project-based and individual-based research teams. This phenomenon shows the concentration of research resources within a select few research groups and institutions, a concept previously highlighted by Mongeon et al. ( 2016 ), who pointed out that research funding tends to be concentrated among a minority of researchers. In this research, we not only corroborate this assertion but also observe that researchers with abundant funding collaborate to form research groups, thereby mutually supporting each other. In addition, based on the structures of research groups at Nanyang Technological University and Tsinghua University, one could posit that these institutions resemble what might be termed a “rich club” (Ma et al., 2015 ). However, despite the heightened productivity of relatively concentrated research groups at Tsinghua University in terms of research output, their academic influence pales compared to that of Nanyang Technological University. To enhance research influence, it seems that the funding agency should curtail funding allocations to these “rich” research groups and instead allocate resources to support more financially challenged research teams. This approach would serve to alleviate the trend of concentration in research project funding, as suggested by Aagaard et al. ( 2020 ).

Thirdly, research groups in Material Science and Engineering exhibit obvious interdisciplinary characteristics. Despite all research papers being classified under the Material Science and Engineering discipline, the distribution of research groups across various academic departments suggests a pervasive interdisciplinary nature. This phenomenon underscores the interconnectedness of Materials Science and Engineering with other disciplines and serves as evidence that members from diverse departments within high-caliber universities actively engage in collaborative efforts. Previous research conducted in the United Kingdom has revealed that interdisciplinary researchers from arts and humanities, biology, economics, engineering and physics, medicine, environmental sciences, and astronomy occupy a pivotal position in academic collaboration and can obtain more funding (Sun et al., 2021 ). In this research, similar conclusions are also found in Material Science and Engineering.

Fourth, the personnel structure distribution in university research groups adheres to Price’s Law, wherein prolific authors are a small part of the group members, with approximately 20% of individuals contributing to 80% of the work. Backbone-based research groups, comprising predominantly project-based and individual-based research teams in universities, typically exhibit a core and backbone members ratio of approximately 10%–15%, aligning with Price’s Law. Peterson ( 2018 ) also pointed out that Price’s Law is almost universally present in all creative work. Scientific research relies more on innovative thinking and collaboration among researchers, and the phenomenon was first confirmed within university research groups. Besides, systematic research activities require many researchers to participate, but few people make important intellectual support and contributions. In practical research endeavors, principal researchers, such as professors and associate professors, often exhibit higher levels of innovation and stability, while graduate students and external support staff tend to be more transient, engaging in foundational research tasks.

Fifth, regarding the research group with the highest publication count of the two universities, Tsinghua University has more core members, highlighting the research model centered around a single scholar, while Nanyang Technological University exhibits a more dispersed distribution of researchers. This discrepancy may be attributed to differences in the university’s system. In China, valuable scientific research often unfolds under the leadership of authoritative scholars, typically holding multiple administrative roles, thus exhibiting hierarchical centralization within the group. This hierarchical structure aligns with Merton’s Sociology of Science ( 1973 ), positing that the higher the position of scientists, the higher their status in the hierarchy, facilitating increased funding acquisition and research impact. Conversely, Singapore’s research system is more like that of developed countries such as the UK and the US, fostering a more democratic culture where communication among members is more open. This relatively flat team culture is conducive to generating high-level research outcomes (Xu et al., 2022 ). However, concerning the field-weighted citation impact of research group papers, the Chinese backbone-based research group outperforms in both publication volume and academic influence, suggesting that this organizational characteristic is more suitable for China and is more conducive to doing research with stronger academic influence.

The research teams and groups in these top two universities offer insights for constructing science teams: Firstly, the university should prioritize individual-based research teams to enhance the academic influence of their research. Secondly, intra-university research teams should foster collaboration across different departments to promote interdisciplinary research, contributing to the advancement of the discipline. Thirdly, emphasis should be placed on supporting core and backbone members who often generate innovative ideas and contribute more to the academic community. Fourth, the research team should cultivate a suitable research atmosphere according to their cultural background, whether centralized or democratic, to harness researchers’ strengths effectively.

This research proposes a method for identifying university research teams and analyzing the characteristics of such teams at the top two universities. In the future, further exploration into the role of different team members and the development of more effective research team construction strategies are warranted.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request. The data about the information of research papers authored by the two universities and the identification results of the members of university research teams are shared.

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Zhe Cheng contributed to the study conception, research design, data collection, and data analysis. Zhe Cheng wrote the first draft of the manuscript. Yihuan Zou made the last revisions. Yihuan Zou and Yueyang Zheng supervised, proofread, and commented on previous versions of this manuscript. All authors read and approved the final manuscript.

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Cheng, Z., Zou, Y. & Zheng, Y. A method for identifying different types of university research teams. Humanit Soc Sci Commun 11 , 523 (2024). https://doi.org/10.1057/s41599-024-03014-4

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16 Types of Projects. Classification in Project Management

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The goal of every well-planned project is to create a product that would be satisfying to potential customers. Your success as a project manager depends on how you use available resources to complete your project on time.

Knowing the type of project (which we'll list below) helps you effectively pick the best project management tools and techniques.

For example, suppose you are handling a real estate project. In that case, you are best served using project management software for real estate than simple project management software .

In this list, you will learn about the 16 different types of projects based on the product and level of complexity.

Let’s get started.

Types of Projects Based on the Product of the Project

Here's a list of the 16 different project types:

  • Communication Projects
  • Stakeholder Management Projects
  • Task Assignation Projects
  • Construction Projects
  • IT Projects
  • Business Projects
  • Production Projects
  • Social Projects
  • Educational Projects
  • Community Projects
  • Research Projects
  • Manufacturing Projects
  • Management Projects
  • Maintenance Projects
  • Infrastructure Projects
  • Integration Projects

1. Communication Projects

Every project type requires effective communication as it serves as the backbone of every successful project. However, communication and its methods vary among different types of projects and are key to its overall success.

Members or stakeholders involved in various types of projects need to utilize various means of communication, which include verbal, non-verbal, writing, and visual.

There must be a well-detailed communication plan which documents all communication activities, methods, and intended audience. A detailed communication plan on the redevelopment of a website to support a client’s new brand is an example of a communication project.

Example of a project management communications plan

2. Stakeholder Management Projects

The variety experienced with projects can be largely attributed to the number of stakeholders involved in the project process. More often than not, the project stakeholders consist of only the project manager and the project team.

The more stakeholders involved in the project, the more complex the project will become. There is a need for better stakeholder management. The re-strategizing of an urban construction firm to meet new arising project needs is an example of a stakeholder management project.

Stakeholder Management Projects

3. Task Assignation Projects

Each type of project embarked on in project management comes with a variety of tasks and activities to be completed. Usually, these projects vary depending on the way and manner in which these tasks are assigned. It also depends on the appropriate responsibility sharing formula chosen.

Assigning project tasks is the sole responsibility of the project manager and the success of the project depends on the effective and proper distribution of such tasks.

The effective delegation of project stakeholders to perform various tasks as regards the construction of a new garden is a typical example of this project.

Project Assignment Plan

4. Construction Projects

These projects comprise engineering projects which bother around basic construction, be it civil or architectural. Construction projects involve rigorous planning and are draining both physically and mentally.

Paying keen attention to details during construction projects execution is crucial as errors are very costly and more often than not, impossible to manipulate.

Building constructions involving the construction of residential and commercial infrastructures is a typical example of a construction project.

Construction Project Timeline Template

5. IT Projects

Any project which falls under software development, web development, network configuration, database management, and IT recovery are categorized as an IT project.

In today’s world, information systems across project management are becoming seemingly common and fast searched for across critical sectors.

Most organizations have sewn into their structure specific IT goals in a bid to move with evolving times. Acquiring a modified data system is an example of an IT project.

Example of an IT project

6. Business Projects

Business projects have a commercial outlook as they involve the development of a business plan from the project initiation phase to the project closure phase. It is aimed at achieving a specific business objective pre-determined during the business planning and strategic phase.

An example of a business project is the design of a portfolio management office.

7. Production Projects

Production projects aim to deliver goods and services in a response to customers’ needs. The purpose of any production project embarked on is to create a unique product or service through innovative means.

Project Management vs Product Management

This product or service production process involves conceiving the idea and then carefully developing the idea till it reaches a marketable form.

Only when it reaches a marketable form is a service or product production project showcased to intending customers for which the product or service was uniquely designed.

The unique process of customizing your home construction is an example of a service or product production process.

New Product or Service Worksheet

8. Social Projects

Social projects are also called public service projects and they usually come at little or no cost. These projects are aimed at providing innovative solutions to societal ills and vices.

The idea behind social projects is to ensure the people at the receiving end up with a much greater standard of living. Under corporate social responsibility (CSR), many companies and organizations embark on this type of project.

Social projects include social security, social housing, and social services such as road repairs, building infrastructures, and others.

9. Educational Projects

Educational projects aim at improving the learning process for students by utilizing harnessed and gathered knowledge over time. These projects involve the real-life application of the gained knowledge to solve practical real-life challenges. Working on a biography about a notable individual is an example of an educational project.

10. Community Projects

These projects are quite similar to social projects with the sole exception that the direct beneficiaries are involved in the project process. The goal of community projects is to meet the welfare needs of community dwellers and may also include charities.

Economic community projects also exist in a bid to curtail the economic hardship faced by community members due to a variety of factors. These projects help to alleviate the overall welfare of community members by the organization of small but impacting projects. Local funding of orphanages is an example of a community project.

11. Research Projects

Research projects are specially designed to make use of innovative means and knowledge harnessed over time to improve the overall operation of an organization. This involves the extensive use of scientific means to find solutions to questions that arise as a result of extensive research work conducted.

These projects aim to improve and advance the development of knowledge. Research projects can either be scientific, social, economic, or technological. Extensive research work conducted on the prevention and treatment of chronic brain injury is an example of a research project.

Research Project Proposal Template

12. Manufacturing Projects

Manufacturing is the heartbeat of many organizations. Projects that need to undergo manufacturing of various forms need to be conducted with the highest level of concentration and oversight.

These manufacturing projects usually come with a high level of risk to the organization at large. The use of the appropriate and most efficient project management strategies helps to reduce the likely occurrence of such project risks.

Manufacturing projects can relate to the food, chemical, automobile, metal fabrication, and aerospace manufacturing industries. The processes involved in the production of candles are an example of a manufacturing project.

13. Management Projects

The use of special and specific knowledge or skill sets is essential for every successful management project. Every project requires unique management skills to meet its goals and objectives.

The process of successfully managing a project process is an entirely different project of its own and needs the best professionals on hand.

A large chunk of the project resources is utilized by management projects. The design and testing of new computer software for a much-improved business operation is a typical example of a management project.

14. Maintenance Projects

Maintenance projects are the projects involved in keeping organization facilities or structures in good and sound working conditions. The sole aim of these projects is to ensure facilities are kept in perfect operating condition even after usage for a long period.

Efficient and routine maintenance projects are essential for the success of ongoing and future projects. Maintenance plays a vital role in project management. This type of project falls under service products. The conduct of root-cause failure analysis is an example of a maintenance project.

15. Infrastructure Projects

These projects are plain structures and services put in place to enhance efficient operation. Infrastructure projects are the foundation building block on which most successful projects are built.

Major infrastructure projects include the building and development of roads, sewers, railways, and power lines. These projects are also called essential projects and focus primarily on the development of services and efficient working systems.

16. Integration Projects

Integration projects combine and coordinate all essential elements of a project process together. These processes include the coordination of tasks, resources, and key stakeholders. Integration projects can either be batch-oriented, event-oriented, or service-oriented.

They usually require the exchange of data and relevant information between two or more project management systems with the sole aim of improving the overall project quality.

Types of Projects in Project Management by Level of Complexity

You can classify projects by their level of complexity . A project can either be easy or complicated. Project managers and project teams aim to make complex projects look as simple as possible even with a large number of dependencies . What are the types of projects based on complexity?

  • Simple projects
  • Cross-functional
  • Inter-Company

1. Simple Projects

Simple projects involve only a handful of people working on the project. They comprise tasks and interdependencies that are clear and easy to execute.

A simple project uses the smallest of resources and needs little or no planning at all to implement. An example of a simple project is the coordination of the delivery of equipment to a workshop site.

2. Cross-Functional Projects

Cross-functional projects involve the entirety of processes and procedures needed to be put in place before the project is completed. They are workflow systems and are methodical in meeting up the project’s goals and objectives.

These projects usually require constituting cross-functional teams that comprise different functional sections of the organization such as sales, marketing, manufacturing, and finance aimed at ensuring the projects reach the set target.

Cross-functional projects involve a vast depth of expertise from various stakeholders involved as their ideas are hybrid and cut across various sectors.

3. Inter-Company Projects

Inter-company projects involve the merging of two separate and entirely distinct organizations with the sole aim of combining their resources to achieve a set-out goal.

The project is usually the sole responsibility of one of the organizations but the resources needed to pull the desired result are at the beck and call of the other organization to be merged.

Inter-company projects are organizations' teamwork and the profits of the arrangements are shared based on the pre-signed working agreement.

Best Project Management Software for Any Type of Project

Choosing a project management software can be tricky. Many project management software claim to have these essential project management software features. However, not all PM tools provide them to an acceptable standard.

Here are the best project management software tools that can be used in any of the mentioned types of projects.

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  • Understanding Enterprise Structures

Project Units

Project units are operational subsets of an enterprise that conducts business operations using projects and enforces consistent project planning, management, analysis, and reporting. Project units often represent lines of business, such as Consulting Services, Sales, and Research and Development.

You must set up at least one project unit to use in Oracle Project Portfolio Management.

Maintain independent setup data for each project unit while sharing a common approach to financial management across all project units. The following graphic shows two project units that share a common approach to financial management and data. Each project unit maintains separate reference data for managing projects.

This diagram illustrates project units that share a business unit.

General Properties

General property options include the default reference data set the application uses for any new reference data object associated with the project unit. You can override the default set for each reference data object. The method of project number creation, either manual or automatic, and daily or weekly full time equivalent hours for reporting purposes, are also included in general properties.

Set Assignments

Assign sets to project units to determine how the application shares reference data across different lines of business in a company. A project unit is a set determinant for the following objects.

Project Definition: Includes set-enabled reference data for the project definition including:

Financial plan type

Project Transaction Types: Includes set-enabled reference data for project transactions including:

Project expenditure type

Project work type

Set assignment configuration includes the following options for each project unit.

Reference Data Object: For the project definition and project transaction types.

Reference Data Set Code: By default, the set for each reference data object is from the default set specified for the project unit.

Related Business Units

You associate business units with a project unit to identify the business units that are accountable for financial transactions of projects in each project unit. You can change the project unit and business unit association if you haven't used the combination on a project or project template. If a business unit isn't associated with any project unit, then the business unit is valid for all project units.

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  • western everglades restoration project update

Western Everglades Restoration Project Update

DATE AND TIME:   Wednesday, April 17, 2024, 5:00 PM

LOCATION:   University of Florida/Institute of Food and Agricultural Sciences (IFAS) Station Southwest Florida Research & Education Center 2685 State Road 29 North Immokalee, FL 34142

CONTACT INFORMATION:   For more information, you may contact   Jeff Prater with the U.S. Army Corps of Engineers   via phone at 561-801-5734 or via email at  [email protected] .

GENERAL SUBJECT MATTER TO BE DISCUSSED:   The U.S. Army Corps of Engineers, Jacksonville District (USACE) will be hosting an update on the Western Everglades Restoration Project (WERP) on April 17, 2024, from 5:00-7:00 pm at the University of Florida/Institute of Food and Agricultural Sciences (IFAS) station, Southwest Florida Research & Education Center, 2685 State Road 29 North in Immokalee, FL. The USACE / South Florida Water Management District (SFWMD) team will provide an update to the project since the last public meeting on January 17, 2024. This will be an “Open House” style question/answer period where USACE and SFWMD staff will be able to interact with attendees.

The purpose of the WERP is to improve the quantity, quality, timing, and distribution of water needed to restore and reconnect the western Everglades ecosystem. Public engagement is encouraged.

WERP will reestablish ecological connectivity from the northwest portion of the study area, across the Seminole Tribe of Florida’s Big Cypress Reservation and into Big Cypress National Preserve, while maintaining flood protection and ensuring that inflows meet applicable water quality standards. The proposed action is located within Hendry, Broward, Miami-Dade, and Collier counties, Florida. 

For more information please visit: http://www.saj.usace.army.mil/Missions/Environmental/Ecosystem-Restoration/Western-Everglades-Restoration-Project/

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  27. Western Everglades Restoration Project Update

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