what is research project management

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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• 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

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.

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.

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.

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.

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.

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.

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 ).

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: 22 April 2024

A method for managing scientific research project resource conflicts and predicting risks using BP neural networks

• Xuying Dong 1 &
• Wanlin Qiu 1  

Scientific Reports volume  14 , Article number:  9238 ( 2024 ) Cite this article

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This study begins by considering the resource-sharing characteristics of scientific research projects to address the issues of resource misalignment and conflict in scientific research project management. It comprehensively evaluates the tangible and intangible resources required during project execution and establishes a resource conflict risk index system. Subsequently, a resource conflict risk management model for scientific research projects is developed using Back Propagation (BP) neural networks. This model incorporates the Dropout regularization technique to enhance the generalization capacity of the BP neural network. Leveraging the BP neural network’s non-linear fitting capabilities, it captures the intricate relationship between project resource demand and supply. Additionally, the model employs self-learning to continuously adapt to new scenarios based on historical data, enabling more precise resource conflict risk assessments. Finally, the model’s performance is analyzed. The results reveal that risks in scientific research project management primarily fall into six categories: material, equipment, personnel, financial, time, and organizational factors. This study’s model algorithm exhibits the highest accuracy in predicting time-related risks, achieving 97.21%, surpassing convolutional neural network algorithms. Furthermore, the Root Mean Squared Error of the model algorithm remains stable at approximately 0.03, regardless of the number of hidden layer neurons, demonstrating excellent fitting capabilities. The developed BP neural network risk prediction framework in this study, while not directly influencing resource utilization efficiency or mitigating resource conflicts, aims to offer robust data support for research project managers when making decisions on resource allocation. The framework provides valuable insights through sensitivity analysis of organizational risks and other factors, with their relative importance reaching up to 20%. Further research should focus on defining specific strategies for various risk factors to effectively enhance resource utilization efficiency and manage resource conflicts.

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Introduction.

In the twenty-first century, driven by rapid technological innovation and a substantial increase in research funding, the number of scientific research projects has experienced exponential growth. These projects, serving as pivotal drivers of scientific and technological advancement, encompass a wide array of domains, including natural sciences, engineering, medicine, and social sciences, among others. This extensive spectrum attracts participation from diverse researchers and institutions 1 , 2 . However, this burgeoning landscape of scientific research projects brings forth a set of accompanying challenges and predicaments. Foremost among these challenges is the persistent issue of resource scarcity and the diversity of project requirements. This quandary poses a formidable obstacle to the management and execution of scientific research initiatives. It not only impacts the project’s quality and efficiency but can also cast a shadow on an organization’s reputation and the output of its research endeavors 3 , 4 , 5 . For instance, when two university research projects concurrently require the use of a specific instrument with limited availability or in need of maintenance, it may result in both projects being unable to proceed as planned, leading to resource conflicts. Similarly, competition for research funding from the same source can introduce conflicts in resource allocation decisions by the approval authority. These issues are widespread in research projects, and surveys indicate that project delays or budget overruns due to improper resource allocation are common in scientific research. For example, a study on research projects funded by the National Institutes of Health in the United States revealed that approximately 30% of projects faced delays due to improper resource allocation. In Europe, statistics from the European Union’s Framework Programme for Science and Innovation indicate that resource conflicts have impeded about 20% of transnational collaborative research projects from achieving their established research objectives on time. Furthermore, scientific research projects encompass a spectrum of resource requirements essential for their seamless progression, including but not limited to materials, equipment, skilled personnel, adequate funding, and time 6 . The predicament arises when multiple research initiatives necessitate identical or analogous resources simultaneously, creating a challenge for organizations to provide equitable support during peak demand periods. To mitigate the risks associated with resource conflicts, organizations must continually administer their resource allocation and strike a harmonious equilibrium between resource requisites and their availability 7 .

The Back Propagation (BP) neural network, as a prominent deep learning algorithm, boasts exceptional data processing capabilities. Notably, neural networks possess the capacity to swiftly process extensive datasets and extract intricate mapping relationships within data, rendering them versatile tools employed across various domains, including project evaluation, risk assessment, and cost prediction 8 , 9 . Scientific research project management constitutes a dynamic process. As projects advance and environmental factors evolve, the risk landscape may undergo continuous transformation 10 . The BP neural network’s inherent self-learning ability empowers it to iteratively update its model based on fresh data, enabling seamless adaptation to new circumstances and changes, thereby preserving the model’s real-time relevance 11 . In conclusion, this approach is poised to enhance project management efficiency and quality, mitigate risks, and foster the potential for the successful realization of scientific research projects.

The primary objective of this study is to formulate an evaluation and risk prediction framework for scientific research project management utilizing the BP neural network. This framework aims to address the issues associated with resource discrepancies and conflicts within the realm of scientific research project management. This study addresses the primary inquiry: What types of resource conflict risks exist in scientific research project management? An extensive literature review and empirical data analysis are conducted to answer this question, identifying six main risk categories: materials, equipment, personnel, finance, time, and organizational factors. A comprehensive resource conflict risk index system is constructed based on these categories. To quantitatively assess the importance of different resource conflict risk factors, the Analytic Hierarchy Process (AHP) is employed. This method allowed for the quantification of the influence of each risk factor objectively and accurately by constructing judgment matrices and calculating the weights of each factor. Subsequently, exploration is conducted into the utilization of BP neural networks to construct a resource conflict risk management model for scientific research projects. A BP neural network model is developed incorporating Dropout technology to capture complex correlations between project resource demand and supply. This model self-learns to adapt to new scenarios in historical data, thereby improving prediction accuracy. Research project data is collected from several universities in Xi’an from September 2021 to March 2023 to validate the effectiveness and accuracy of the proposed model. This data is utilized to train and test the model, and its performance is compared with other advanced algorithms such as CNN and BiLSTM. The evaluation is based on two key metrics: accuracy and root mean square error (RMSE), demonstrating excellent fitting ability and prediction accuracy.

The innovation introduced in this study is rooted in the recognition that the proliferation of scientific research initiatives can precipitate resource conflicts and competition, potentially leading to adverse outcomes such as project failure or resource inefficiency. This study harnesses a multi-layer BP neural network as its central computational tool, concomitantly incorporating the establishment of a resource conflict risk index system. This comprehensive model for evaluating and predicting the risks in scientific research project management takes into account both the resource conflict risk index system and the intrinsic characteristics of the BP neural network. This combined approach serves to enhance the efficiency of managing scientific research projects, curtail resource wastage, mitigate the risk of resource conflicts, and ultimately furnish robust support for the enduring success of scientific research endeavors.

Related work

Current research landscape in scientific research project management.

Scientific research projects hold a pivotal role in advancing scientific and technological frontiers, fostering knowledge generation, and driving innovation. Effective project management in this context ensures the timely delivery, adherence to budgetary constraints, and attainment of predefined quality standards. Numerous scholars have contributed to the body of knowledge concerning scientific research project management. Significant risks in scientific research project management include improper resource allocation, time delays, budget overruns, and collaboration challenges. For instance, concerning time management, Khiat 12 illustrated that insufficient project planning or external factors often hinder project deadlines. Regarding financial management, Gao 13 highlighted the lack of transparency in fund allocation and unreasonable budgeting, leading to unnecessary research cost overruns. Previous studies have predominantly concentrated on developing diverse methodologies and tools to identify and assess potential risks in scientific research projects. For instance, quantitative models have been employed by researchers like Jeong et al. 14 to evaluate project failure probabilities and devise corresponding risk mitigation strategies. Concurrently, Matel et al. 15 utilized artificial intelligence (AI), including neural networks and machine learning, to conduct comprehensive analyses of project data and predict potential issues throughout project progression.

The preceding studies offer essential groundwork and insights for the scientific research project management discussed in this study. They illuminate key risks encountered in scientific research project management, including inadequate resource allocation, time constraints, budgetary overruns, and collaboration hurdles. These risks are pervasive in scientific research project management, directly impacting project execution efficiency and outcomes. Moreover, these studies furnish empirical data and case studies, elucidating the underlying causes and mechanisms of these risks. For example, the research conducted by Khiat and Gao offers a nuanced understanding of risk factors, enriching the comprehension of the challenges in scientific research project management. Additionally, these studies introduce diverse methods and tools for identifying and evaluating potential risks in scientific research projects. For instance, the works of Jeong et al. and Matel et al. utilize quantitative models and artificial intelligence techniques to comprehensively analyze project data and forecast potential issues in project advancement. These methodologies and tools serve as valuable resources for constructing the research framework and methodologies in this study. Despite the commendable strides made in employing multidisciplinary approaches to address the challenges posed by scientific research project management, the issues related to resource allocation conflicts and quality assurance during project implementation remain fertile ground for future exploration and active investigation.

Application of BP neural network in project risk and resource management

BP neural networks are renowned for their non-linear fitting and self-learning capabilities, rendering them invaluable for discerning intricate relationships and patterns in project management. Their applications span diverse areas, including resource allocation, risk assessment, schedule forecasting, cost estimation, and more, culminating in heightened efficiency and precision within project management practices. Numerous scholars have ventured into the realm of BP neural network applications within project management. Zhang et al. 16 introduced a real-time network attack detection method underpinned by deep belief networks and support vector machines. Their findings underscore the method’s potential for bolstering network security risk management, extending novel data security safeguards to scientific research project management. Gong et al. 17 devised an AI-driven human resources management system. This system autonomously evaluates employee performance and needs, proffering intelligent managerial recommendations. Bai et al. 18 harnessed BP neural networks to tackle the intricate challenge of selecting service providers for project management portfolios. Leveraging neural networks, they prognosticate the performance of diverse service providers, lending support to project management decision-making. Sivakumar et al. 19 harnessed BP neural networks to prognosticate the prioritization of production facilities in the bus body manufacturing sector. Their work serves as an illustrative testament to the potential of neural networks in the production and resource allocation facets of scientific project management. Liu et al. 20 undertook an analysis of the influential factors and early warning signs pertaining to construction workers’ safety conditions. This investigation underscores the profound implications of neural networks in safety management within the context of engineering and construction project management. Li et al. 21 harnessed optimized BP neural networks to anticipate risks in the financial management arena of listed companies. Their outcomes underscore the utility of neural networks in financial management, providing an exemplar of a risk assessment tool for scientific research project management.

The comprehensive analysis of the aforementioned studies reveals that BP neural networks exhibit substantial capabilities in scrutinizing historical project data, discerning intricate resource demand–supply dynamics, and offering valuable insights for project management decisions and optimizations. These applications underscore the potential of BP neural networks as indispensable tools within the project management domain. Nonetheless, several challenges persist, particularly concerning the real-time adaptability of BP neural networks and their capacity to cater to dynamic project management requisites.

Research in the field of scientific research project resource management and risk prediction

Within the realm of scientific research project resource management and risk prediction, various studies by notable scholars warrant attention. Jehi et al. 22 employed statistical models for risk prediction but overlooked the intricate resource conflict relationships within scientific research projects. Efficient project resource management and accurate risk prediction are pivotal for ensuring smooth project execution and attaining desired outcomes. Asamoah et al. elucidated that scientific research projects necessitate both tangible and intangible resources 23 , encompassing materials, equipment, personnel, funding, and time. The judicious allocation and optimal utilization of these resources significantly influence project progress and outcomes. Misallocation of resources can lead to setbacks such as project delays and budget overruns. Meanwhile, Zwikael et al. identified organizational culture, awareness, support, rewards, and incentive programs as key drivers impacting the effective management of scientific research project benefits 24 . These risks can profoundly affect project advancement and outcomes, underscoring the importance of accurate prediction and adept management. Farooq et al. advocated for scientific project management, emphasizing the need for enhanced risk management strategies and management efficacy to foster sustainable enterprise development 25 .

In conclusion, studies on project resource management and risk prediction encompass diverse facets, including resource allocation, risk assessment, and model development. These efforts offer essential theoretical and methodological underpinnings for the effective execution of scientific research endeavors. Given the ongoing expansion and growing complexity of scientific projects, further research on resource management and risk prediction is imperative to navigate increasingly intricate circumstances.

A comprehensive review of methods employed in scientific research project management and risk assessment reveals a predominant focus on quantitative analysis, qualitative research, and the integration of AI techniques. In particular, the utilization of BP neural networks, as demonstrated in studies such as Sivakumar et al., Liu et al., and Li et al., underscores their capacity to furnish real-time data analysis and decision-making support for project managers. However, it remains evident that challenges persist in harnessing the full potential of BP neural networks in terms of real-time adaptability and resource allocation within the multifaceted landscape of dynamic project management. Hence, this study accentuates the existing methodological challenges associated with resource conflict resolution, risk management, and overall scientific research project management. Through the optimization and refinement of BP neural network applications in risk assessment, this study strives to furnish organizations with effective decision-making tools. Ultimately, the insights gleaned from this study aim to serve as a valuable reference for scientific research project managers as they navigate the complexities of project risk management.

Prediction method for scientific research project management risks based on the BP neural network

Analysis of the construction of a scientific research project management risk system.

Scientific research project management constitutes a specialized discipline encompassing the planning, organization, execution, and oversight of scientific research endeavors. Its primary objective is to facilitate the effective attainment of research objectives and anticipated outcomes. The overarching aim of scientific research project management is to optimize resource allocation, schedule planning, and risk mitigation, thereby ensuring the successful culmination of research projects 26 , 27 . A visual representation of the fundamental task processes integral to scientific research project management is depicted in Fig.  1 .

Schematic representation of key scientific research project management tasks.

Scientific research project management, as illustrated in Fig.  1 , constitutes an essential framework to ensure the efficient and organized execution of scientific research endeavors. It encompasses four core phases: project planning and initiation, project execution and monitoring, project closure and summarization, and project communication and feedback 28 . The meticulous determination of project requisites is of particular significance, encompassing financial resources, personnel, equipment, materials, and more. Failure to ensure the effective utilization and judicious allocation of these resources during project management may introduce the risk of hindrances in the smooth progress and achievement of the research project’s envisioned objectives.

Ongoing scientific research projects necessitate an array of resources, encompassing both tangible assets such as materials, equipment, and funds, and intangible elements like time, personnel expertise, and organizational support 29 , 30 . These resources are intricately interwoven within scientific research projects and collectively influence project success. However, when confronted with limited total resources, resource conflicts can arise when multiple projects vie for the utilization of the same resources. Consequently, this study has devised a resource conflict risk index system tailored for the management of scientific research projects. This system stratifies risks according to the categories of resources implicated in the project implementation process, as depicted in Fig.  2 . In this study, ensuring the representativeness and comprehensiveness of risk assessment for resource conflicts in scientific research project management is pivotal. A multifaceted and systematic approach is adopted to define risk categories. A comprehensive literature review initially identifies common resource conflicts in scientific research project management. Subsequently, through interviews and surveys with industry research project managers, firsthand information on specific challenges and risk factors encountered during project execution is collected. Additionally, referencing international standards and best practices ensures the authority and applicability of risk classification. The outcome of these efforts is illustrated in Fig.  2 , showcasing a meticulously designed resource conflict risk index system. It encompasses six major categories: equipment risk, material risk, personnel risk, financial risk, time risk, and organizational risk, further subdivided into 17 specific sub-items. Acknowledging the complexity and diversity of research projects, it is recognized that, despite efforts made, other potential risks may not be included in the current model. A dynamic iterative approach is proposed to address this challenge, integrate additional risk factors, and continuously optimize the model. Specific steps are outlined to enhance the model’s capabilities. Firstly, establishing a monitoring system to regularly collect user feedback and industry updates allows the prompt discovery and incorporation of new risk factors. Simultaneously, closely monitoring the latest research findings in the domestic and international scientific research project management field ensures the continuous integration of new discoveries from academia. Additionally, a dedicated team conducts regular in-depth reviews of the existing risk index system, adding, deleting, or adjusting the weights of risk factors as needed based on actual requirements. This process enables the model to better adapt to the current project management environment and future trends. Secondly, utilizing the newly integrated dataset to cross-validate the model ensures that the newly added risk factors are appropriately assessed and predicted. By comparing the performance of different versions of the model, a more accurate measurement of the effects of optimization is achieved. Finally, research project managers are encouraged to provide real-time feedback, including the model’s performance in actual applications, overlooked risk points, and improvement suggestions, enhancing the model’s usability and reliability. These methods aim to construct a more refined, flexible, and adaptable scientific research project risk assessment model that continuously evolves to meet changing needs. Through continuous optimization and improvement, this model is believed to more effectively assist project managers in making risk-based decisions and promote the success rate of scientific research projects.

Resource conflict risk indicator system for scientific research project management.

As depicted in Fig.  2 , this risk system underscores the significance of material quality and timely supply in project execution. The establishment of this resource conflict risk indicator system forms a fundamental basis for subsequent model development and risk forecasting, empowering project managers to gain comprehensive insights into and effectively manage resource conflict risks.

Weight analysis process using APH for the risk indicator system

The AHP is primarily employed for the comprehensive analysis of multifaceted problem systems, involving the segmentation of interrelated factors into hierarchical levels. It subsequently facilitates objective assessments at each tier. This method typically deconstructs problems into a tripartite structure comprising the following levels: the objective layer (highest), the criteria layer (intermediate), and the indicator layer (fundamental) 31 , 32 . In this context, the objective layer pertains to the project’s resource conflict risk, which represents the core challenge addressed by this structural model. The criteria layer provides an initial decomposition of the objective layer and establishes the foundational logical framework for third-level indicators. The indicator layer encompasses risk factors, specifically, the potential triggers for resource conflict risks. The weight analysis process, employing the AHP for the risk indicator system, is delineated in Fig.  3 .

Weight analysis process of applying the hierarchical analysis method to the risk indicator system.

In Fig.  3 , the application of the AHP to the weight analysis of the scientific research project management risk indicator system follows a general procedure: sequentially defining individual problems, creating a hierarchical structural model, constructing pairwise comparison matrices, performing hierarchical ranking calculations and consistency tests, and finally, selecting evaluation criteria systematically for assessment.

The initial step involves breaking down the intricate problem into distinct components, creating a hierarchical structure model comprising the target layer, criterion layer, and indicator layer.

In this phase, the assessment of relative importance between elements leads to the formation of a pairwise comparison judgment matrix, denoted as matrix A , as depicted in Eq. ( 1 ).

In Eq. ( 1 ), \(a_{ij} > 0\) , \(a_{ji} = 1/a_{ij}\) , and \(a_{ii} = 1\) .

The AHP calculations are performed following the classic methodology proposed by Rehman 33 . The process begins by computing the product M i of the elements within each row, as illustrated in Eq. ( 2 ).

The next step involves calculating the n -th root of M i , as described in Eq. ( 3 ).

Next, the process involves normalizing \(W = \left[ {W_{1} ,W_{2} , \cdots ,W_{n} } \right]^{T}\) , as shown in Eq. ( 4 ).

Finally, the maximum eigenvalue \(\lambda_{\max }\) is calculated via Eq. ( 5 ).

The calculation of weights and the consistency test of the judgment matrix involve the use of the eigenvalue method to calculate the weight vector of the judgment matrix. This is demonstrated in Eq. ( 6 ).

In Eq. ( 6 ), \(\lambda_{\max }\) denotes the maximum characteristic root of A , Q signifies the eigenvector, and the weight vector is obtained by normalizing Q.

Continuing with the consistency testing, the weight vector must undergo evaluation for consistency. To initiate this evaluation, calculate the Consistency Index ( C.I. ) using Eq. ( 7 ).

Next, it is imperative to determine the corresponding average Random Consistency Index ( R.I. ). Subsequently, the Consistency Ratio ( C.R. ) is computed using the formula presented in Eq. ( 8 ).

If the calculated C.R. is less than 0.1, it indicates that the judgment matrix meets the prescribed consistency criteria, and the assigned weight values for each indicator are considered valid. However, if the calculated C.R. equals or exceeds 0.1, this signals the need for adjustments to the judgment matrix. To address this, the matrix is re-evaluated, and consistency checks are repeatedly performed until the matrix achieves the required level of consistency.

Analyzing the resource conflict risk management model for scientific research projects based on the BP neural network

This section focuses on predicting and evaluating the potential occurrence of various risk factors within scientific research projects. The objective is to facilitate the selection of appropriate response strategies aimed at minimizing losses stemming from risks associated with scientific research endeavors. Resource management within scientific research projects is a complex undertaking, with resource conflict risks influenced by a multitude of factors. Furthermore, as projects evolve, the risk landscape undergoes dynamic changes. In contrast to conventional statistical models, BP neural networks offer distinctive advantages. They employ a combination of forward signal propagation and reverse error-adjustment learning techniques, showcasing exceptional self-learning capabilities, distributed knowledge storage, and associative memory functions 34 . The BP neural network model, rooted in the backpropagation algorithm, evolved from the necessity to simulate biological neural systems and meet the demands of machine learning. Originating in the 1980s, it became a prominent deep learning model, continually iterating and adjusting connection weights to minimize the error between output and target. This learning mechanism allows the BP neural network to adapt to complex non-linear relationships, showcasing robust approximation and generalization capabilities. Over time, enhanced computer hardware and algorithm optimization led to widespread application and development of the BP neural network model. Algorithmically, various improvements, including the momentum method, adaptive learning rate, and regularization, were introduced to boost training speed and generalization ability, addressing challenges such as susceptibility to local minima in traditional BP algorithms. The advent of deep learning saw the integration of the BP neural network into deeper structures like ResNet and CNN, enabling it to handle more intricate tasks and data. The model’s applicability expanded across diverse domains, including image and speech recognition, natural language processing, financial forecasting, and medical diagnosis, yielding breakthrough results. Moreover, technological advancements like big data and cloud computing have enhanced the training and application efficiency of the BP neural network model, presenting new avenues for development. In conclusion, the evolution of the BP neural network model stems from algorithmic refinements, structural enhancements, and broadened applications, providing potent tools for addressing diverse practical challenges. The data transmission process of the BP neural network is illustrated in Fig.  4 .

Data transmission flow chart of the BP neural network.

Figure  4 illustrates the data transmission process in the BP neural network, highlighting forward propagation, which entails processing and transmitting received data information. This unidirectional propagation begins at the input layer, traverses through the hidden layers, and culminates in the output layer to yield the network’s overall output. Let the received input data be denoted as X  = ( x 1 , x 2 …, x n ), with ‘ n ’ signifying the number of neurons in the input layer. The connections between the input layer and the hidden layer initially possess randomized weight values. This citation is derived from Liu et al.’s recommendation 35 to prevent premature convergence to local minima during the training process. Representing the weight of the connection between the i -th neuron in the input layer and the j -th neuron in the hidden layer as W ij . The notation follows Narkhede et al.’s study 36 , which offers a comprehensive explanation of neural network fundamentals and operational principles. The information received by the hidden layer is expressed in Eq. ( 9 ).

In Eq. ( 9 ), i represents the number assigned to neurons in the input layer, ‘ j ’ pertains to the number of neurons in the hidden layer, and A  = ( a 1 , a 2 …, a m ) symbolizes the input variables received by the hidden layer. Upon receiving these variables, the hidden layer neuron transforms them into the output value of the hidden layer using the activation function. The methodology in this section draws from the research by Narengbam et al. 37 on activation functions in deep learning models. Specifically, the treatment of the output layer mirrors that of the hidden layers, and the computation of output layer neurons adheres to the methodology outlined in the cited literature.

In Eq. ( 10 ), Y  = ( y 1 , y 2 …, y m ) represents the output variables of the hidden layer. The computation method for the input and output values of the output layer parallels that of the hidden layer. The weight denoted as v jk signifies the connection between the j -th neuron in the hidden layer and the k -th neuron in the output layer. The information received by the output layer is described in Eq. ( 11 ).

The output value of the output layer neurons, once activated by the activation function, is expressed in Eq. ( 12 ).

At this juncture, the output value O denoted as \(O = \left( {o_{1} ,o_{2} , \cdots ,o_{z} } \right)\) is obtained, signifying the conclusion of the forward propagation process.

In the backpropagation process, the loss function J quantifies the error between the neural network’s output value and the true value (referring to the definition and application of the loss function in neural network optimization as articulated by Özden et al. 38 ), as illustrated in Eq. ( 13 ).

During the neural network’s training process, the weight, denoted as W , and the bias vector, denoted as b , play essential roles. The gradient descent method is employed to optimize the neural network (derived from Kumar et al.’s 39 analysis of the effectiveness of optimization algorithms in deep learning training). Each iteration within the gradient descent method updates the parameters W and b as per Eqs. ( 14 ) and ( 15 ).

where α represents the learning rate. The crucial step involves computing derivatives using backpropagation, employing the BP algorithm to calculate \(\frac{\partial }{{\partial W_{ij}^{\left( l \right)} }}J\left( {W,b;x,y} \right)\) and \(\frac{\partial }{{\partial b_{i}^{\left( l \right)} }}J\left( {W,b;x,y} \right)\) . These two components represent the derivatives of the cost function J ( W , b ; x , y ) for a single sample ( x , y ). Once this derivative is computed, deriving the derivatives of the overall cost function J ( W , b ; x , y ) becomes relatively straightforward. The calculated results are presented in Eqs. ( 16 ) and ( 17 ).

This study aims to develop a resource conflict risk management model tailored to predict and assess the resource conflict risks inherent in scientific research projects during execution. Resource conflicts arise from competition for limited resources like equipment, funding, and personnel among multiple projects. If unaddressed, these conflicts can significantly impede project progress and outcomes. The model’s specific objectives are to analyze project-related information (e.g., project scale, duration, funding, personnel allocation) to predict potential conflict points in resource allocation, enabling project managers to proactively mitigate or avoid conflicts and optimize resource utilization effectively. To achieve these objectives, we employ a BP neural network approach for model construction, chosen for its superior non-linear mapping capability and self-learning characteristics, enabling it to learn from extensive historical project data and identify complex resource conflict risk patterns. The model construction entails key steps: Data preprocessing involves cleaning and normalizing collected project data to meet model input requirements. Feature selection entails choosing highly correlated feature variables associated with resource conflict risks as model inputs based on expert knowledge and data analysis results. Model training and validation involve training the BP neural network with labeled historical project data and evaluating and optimizing model performance through techniques like cross-validation. Through these methods, the developed model accurately predicts resource conflict risks in scientific research project management, providing decision support for project managers to enhance resource utilization efficiency and foster successful project completion.

While the BP neural network possesses robust learning and non-linear fitting capabilities, inadequate training data can lead to suboptimal fitting. In some cases, the network may only excel at learning from a limited dataset, generating a mapping function (typically represented as a weight vector) that closely matches the training dataset. Consequently, it may struggle to generalize well to new data, exhibiting insufficient generalization abilities. This scenario is known as overfitting. To mitigate overfitting, this study introduces the Dropout regularization method 40 when applying the BP neural network to scientific research project risk management. The Dropout method involves freezing nodes within the input and hidden layers. It is particularly useful when specific neuron correlations in the input layer hinder continuous error convergence during training. The node freezing rate should strike a balance—not too low, as it would have an insignificant impact on the neural network, and not too high, which could lead to underfitting. Therefore, this study sets the node freezing rate for the Dropout regularization method at 50%. By incorporating the Dropout method into the BP neural network, the network topology used for managing resource conflict risks in scientific research projects, based on the BP neural network, is depicted in Fig.  5 .

Network topology based on the BP neural network applied to the resource conflict risk management model for scientific research projects.

As depicted in Fig.  5 , this model incorporates a novel approach. During each training iteration, a randomly selected set of neurons, encompassing those associated with equipment, materials, and organizational risk factors, is temporarily frozen. These frozen neurons do not participate in either the forward propagation calculations or the subsequent backpropagation error adjustments within the current training cycle. The weights connecting these neurons to others retain their previous states or revert to their initial values from the last training update. As the next training iteration commences, the neurons previously frozen are unfrozen, and a new batch of neurons is randomly chosen for freezing. This iterative process effectively bolsters the BP neural network’s ability to generalize from limited data, particularly when addressing resource conflict risk management in research projects.

The integration of the Dropout method into the BP neural network introduces further opportunities for optimization. Adjustments to the network’s depth, the number of neurons, and the choice of activation functions within the risk prediction model can be made. The specific optimization procedure for the BP neural network is outlined in Fig.  6 .

Flowchart presenting the pseudocode algorithm for optimizing the BP neural network.

Experimental evaluation

To assess the performance of the resource conflict risk management model developed in this study, a BP neural network was constructed utilizing the ‘newff’ function within MATLAB. Python was employed for data preprocessing and algorithm implementation. The training of the BP neural network involved configuring parameters for net.trainFcn and net. trainParam following network initialization. Training iterations continued until the error met the predefined performance criterion. The dataset utilized in this study consisted of research project information spanning all universities in Xi’an, China, from September 2021 to March 2023. In comprehensively evaluating the performance of the resource conflict risk management model developed in this study, the scope and objectives of data collection are first determined, focusing primarily on scientific research projects at major universities in the Xi’an area. Data sources included publicly available project records, official website information, and pertinent research project databases. The utilization of web scraping techniques facilitates automated data collection, encompassing details such as project names, principal investigators, start and completion dates, funding particulars, research areas, and participating personnel. Rigorous anonymization and encryption measures are implemented to uphold information security. Subsequently, to enhance understanding of the data characteristics, exploratory data analysis is conducted on the cleaned dataset. This involves calculating descriptive statistics, conducting distribution tests, and performing correlation analysis. Such steps aid in identifying the most influential feature variables for the predictive model. Given that raw data often contain missing values, outliers, or inconsistencies, comprehensive data cleaning is executed, which includes imputation of missing values, removal of outlier data, and standardization of data formats. To safeguard individual privacy, sensitive information such as project leader names undergoes anonymization and encryption. Concerning the application of the AHP in this study, this method is employed to ascertain the relative weights of various risk factors (including materials, equipment, funding, time, personnel skills, and organizational support). The operational process involves establishing a pairwise comparison judgment matrix based on expert assessments and historical data analysis. Each element in the matrix reflects the importance of one risk factor relative to another. The weights of each risk factor are determined by calculating the maximum eigenvalue of the judgment matrix and its corresponding eigenvector. Consistency indices and random consistency ratios are used to verify the consistency of the judgment matrix, deeming the derived weights acceptable only when the random consistency ratio is below 0.1. Using these meticulously assigned weighted risk factors throughout the model evaluation process, resource conflict risk prediction is conducted via the BP neural network using data collected from actual scientific research projects.

Subsequently, rigorous data anonymization procedures were applied, including de-identification, data anonymization, and encryption of sensitive information. The data preprocessing workflow encompassed comprehensive data cleaning to rectify missing or outlier data points. Ultimately, data from 8,175 research projects were amassed and segregated into training and testing subsets, with an 80% to 20% partition ratio.

To assess the performance of the model developed in this study, an initial step involved employing the AHP to evaluate the weights assigned to each factor, including materials, equipment, funds, time, personnel skills, and organization. Subsequently, the algorithm presented in this study was combined with the Convolutional Neural Network (CNN) 41 , Bidirectional Long Short-Term Memory (BiLSTM) 42 , and comparative experiments were conducted in alignment with recent studies conducted by Liu et al. and Li et al. The evaluation primarily relied on accuracy and RMSE as key metrics, precisely measuring model prediction accuracy. Additionally, the Garson sensitivity analysis method was employed to assess the sensitivity of risk factors across various algorithms.

Results and discussions

Analysis of weights and sensitivity results of different factors.

The analysis of weights and sensitivities for various factors is depicted in Figs.  7 and 8 .

Weight results of different factors.

Sensitivity results of different factors.

Figure  7 highlights the various risk factors present in scientific research project management, including materials, equipment, funds, time, personnel skills, and organization. A more in-depth examination of the weight of sub-indicators within each factor reveals that A 21 holds the highest weight value, at 0.705, while A 63 carries the smallest weight value. Consequently, the application of the AHP in this study enables a clear representation of the significance of each influencing factor. This, in turn, facilitates a more targeted and informed decision-making process, allowing for decisions that align better with the actual circumstances and desired outcomes.

Figure  8 reveals notable variations in the sensitivity of each risk factor to the model’s output variables. Organizational risk emerges as the most influential factor on the comprehensive risk value, accounting for a relative importance of 20.31%. Following closely are financial risk at 18.84%, personnel risk at 18.30%, material risk at 17.04%, equipment risk at 16.29%, and time risk at 9.24%. A more detailed scrutiny of the sensitivity of individual sub-indicators within each factor uncovers that A52 exhibits the lowest sensitivity, standing at 4.28%, while A63 records the highest sensitivity, reaching 7.84%.

Model performance comparison results under different algorithms

In-depth analysis encompassed evaluating the accuracy and RMSE outcomes of distinct algorithms across diverse indicators, as depicted in Figs.  9 and 10 .

Visual representation of accuracy results achieved by different algorithms across various factors.

RMSE comparison results of each algorithm under different numbers of neurons.

Figure  9 illustrates that the accuracy of various algorithms remains relatively stable across different index factors. Notably, the risk prediction accuracy achieved by the algorithm proposed in this study outperforms other model algorithms across various factors. The highest risk prediction accuracy is observed in the time factor, reaching an impressive 97.21%, while the equipment factor yields the lowest prediction accuracy, hovering around 80%. Upon further comparison of risk prediction accuracy across algorithms, it becomes evident that the model algorithms proposed in this study outperform Li et al.’s model algorithm and Liu et al.’s model algorithm. Additionally, the proposed model algorithm surpasses BiLSTM and CNN. Consequently, this study’s model algorithm effectively identifies risk factors in the management of scientific research projects.

Figure  10 presents the RMSE results of each algorithm, and it is evident that increasing the number of hidden layer neurons does not significantly alter the RMSE values. Specifically, the RMSE of the model algorithm introduced in this study consistently remains around 0.03. In contrast, other model algorithms yield RMSE values exceeding 0.031, indicating higher errors compared to the model proposed in this study. When arranging the RMSE results in ascending order, it becomes apparent that the order is as follows: the model algorithm introduced in this study has the lowest RMSE, followed by Li et al.’s proposed model algorithm, Liu et al.’s proposed model algorithm, BiLSTM, and CNN. Therefore, the research model demonstrates effective risk prediction in scientific research project management, characterized by lower identification errors and superior fitting capabilities.

This study established a resource conflict risk index system for scientific research project management and introduced a BP neural network as a risk prediction model. Leveraging its non-linear fitting and self-learning capabilities, the model effectively captured intricate resource demand and supply dynamics, enabling a more precise assessment of resource conflict risks. The performance evaluation revealed the model’s strength in predicting time-related risks, achieving an accuracy rate of 97.21% with an RMSE consistently around 0.03, indicating strong fitting capabilities. The developed BP neural network model in this study effectively predicts resource conflict risks in scientific research project management, serving as a valuable decision support tool for risk assessment. However, certain limitations are acknowledged in this research. Firstly, the dataset is derived from universities in a specific region (Xi’an), and although sizable, it may not comprehensively represent all types of scientific research projects. Future endeavors could involve incorporating more diverse and extensive data sources to enhance the model’s universality and robustness. Secondly, despite the notable advantages of BP neural networks in addressing non-linear problems, the selection of appropriate network structures and parameter settings remains a challenge. Subsequent work could focus on further enhancing the network’s performance through the exploration of additional optimization algorithms. In terms of future research directions, the following points are proposed: Firstly, considering the integration of various machine learning and deep learning technologies to obtain more comprehensive risk prediction results. Secondly, exploring the application of the model in scientific research projects of different scales and types to validate and broaden its applicability. Lastly, investigating the integration of the model into a real-time project management system can provide project managers with dynamic risk monitoring and warning services.

Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

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Dong Xuying and Qiu Wanlin studied the specific situation of BP neural network, and combined with the experience of scientific research project management, Dong Xuying designed a scientific research project management evaluation and risk prediction method based on BP neural network. At the same time, Qiu Wanlin collected and analyzed the experimental data in this paper according to the actual situation. Dong Xuying and Qiu Wanlin wrote the first draft together.

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Dong, X., Qiu, W. A method for managing scientific research project resource conflicts and predicting risks using BP neural networks. Sci Rep 14 , 9238 (2024). https://doi.org/10.1038/s41598-024-59911-w

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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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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.

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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

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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.

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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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21 Research project management

• Published: February 2016
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What is a project? - Stage 1: Definition: defining and agreeing what the project is about - Stage 2: Planning: planning how the project will be conducted - Stage 3: Implementation and control: running the project - Stage 4: Close out: delivery and the end of the project

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Project Management Strategies for Research Team Members

Webinar series on the principles of project management

For more information:

• Understand the foundational principles of project management.
• Explore how project management principles and strategies can influence your work with colleagues and stakeholders on various projects.

Managing projects is a detailed and systematic process. Yet, the applications of this process vary across disciplines and teams. This webinar series will introduce how to troubleshoot, forecast, and problem solve using project management in various contexts while considering how these elements impact the work of teams. Each of the four independent sessions will be led by David Vincenti, PMP, a certified project management professional. This series will identify the principles of project management and how to apply templates and skills to your work and experiences in team settings. The last session will feature a panel of guest speakers who utilize successful project management strategies in their respective roles and professions. Those without official training in this area will gain skills and confidence in project management during this series.

Boundary-Crossing Skills for Research Careers

This session explores approaches to developing a broad range of competencies integral to establishing and maintaining a successful research career. The series delves into the following competencies: team science, mentorship, project management, communication, leadership, and funding research. For more information and to access other resources and webinars in the series, please visit  Boundary-Crossing Skills for Research Careers.

Meet the Presenter

Vincenti has presented to academic and professional audiences on project management, professional development, and other topics, and has been recognized for his work with career planning for early-career technical professionals. He holds degrees in materials engineering and technology management from Stevens Institute of Technology.

Meet the Panelists

Sarita Patil, MD:  Assistant Professor of Medicine, Harvard Medical School and Assistant Physician, Massachusetts General Hospital

Jane Shim, BA : Clinical Research Coordinator, Food Allergy Center, Massachusetts General Hospital

Neal Smith, MSc : Senior Computational Biologist, Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital

Yamini Virkud, MD, MA, MPH : Pediatric Allergist/Immunologist and Assistant Professor, University of North Carolina at Chapel Hill

Session dates

Session 1: Defining the Work November 1, 2022 | 12:00pm ET This session introduces basic project management principles. You will learn the definition of a project, how to manage project scope, and how to draft the baseline of a project while considering how projects can be connected.

Session 2: Creating the Plan November 3, 2022 | 12:00pm ET In this session, you will learn to apply project planning terms and understand how to break a project into manageable parts, sequence tasks, and manage time while considering how these components affect your work and the work of your team members.

Session 3: Finalizing the Plan November 8, 2022 | 12:00pm ET In this session, you will explore project management principles further by calculating risks, managing a process, reviewing a project plan, and forecasting the execution and completion of a project while considering how these elements impact your work and the work of your team members.

Session 4: Panel Discussion November 10, 2022 | 12:00pm ET This culminating session features a panel discussion with four successful project management practitioners. The panelists will share their experiences in their respective roles and professions, and discuss how they engage in project management work within team settings.

Time commitment

50-minute sessions on Zoom

This series is designed for team members in the clinical and translational (c/t ) workforce who are familiar with project management but have no formal training. Attendees are welcome to attend on their own or with their team members.

We believe that the research community is strengthened by understanding how a number of factors including gender identity, sexual orientation, race and ethnicity, socioeconomic status, culture, religion, national origin, language, disability, and age shape the environment in which we live and work, affect each of our personal identities, and impacts all areas of human health.

Eligibility

There are no eligibility requirements. Prior session attendees have included: PhD, MD, postdocs, junior faculty, and medical students.

Registration is currently closed. Please check back for future opportunities.

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Examples of Project Management

In order to better conceptualize what project management is, it’s helpful to understand how project management plays out in real-life applications. Here are a few examples of how project management is used across various industries every day: 

Example One: Project Management in Construction

In 2005, BAA Airports Ltd. was presented with an enormous task: remodeling Terminal 1 within Heathrow Airport, the busiest international airport in the world while keeping the terminal open to the 20 million annual travelers that pass through the airport. The project was extremely complex, and made even more challenging by a strict deadline and significant public health concerns, given the construction project was taking place within an active terminal. 

Throughout the project lifecycle, David Buisson, PMP, and the project manager in charge of the Heathrow renovation project, encountered many unexpected obstacles, including asbestos in the ceiling and inconsistencies with the floor level. Buisson and his team were able to properly navigate project challenges, operational risk, and communication management with key stakeholders. They successfully delivered the project on time and on budget—without any major mishaps—utilizing the PMBOK Guide from the Project Management Institute, the standard guide for project management professionals.

The 2005 renovation of Heathrow Airport Terminal 1 is widely considered one of the most successful case studies in construction project management to date. 

Example Two: Project Management in Healthcare

During the Covid-19 pandemic, pharmaceutical and biotechnology company, AstraZeneca partnered with the University of Oxford to address the international need for a vaccine. The research partners at Oxford University had begun showing promising research around an early vaccine option. Paired with AstraZeneca’s manufacturing capabilities and global supply chain experience, it was a no-brainer for the two entities to pair up to address the pandemic. 

However, the partnership would face numerous challenges throughout the project lifecycle, including, most notably, a highly unpredictable and rapidly evolving public health crisis. Adaptability had to be the name of the game, and the structured guidelines of project management provided a baseline for the team to work from. Ultimately, the project was an overwhelming success, with over 1 billion doses of the vaccine delivered to over 170 countries.

Example Three: Project Management in Aerospace Technology

The Mars Pathfinder Mission began in 1996 as a result of budget cuts within NASA, shifting the organization’s focus to projects that could be completed “faster, better, and cheaper.” The goal was to spend less than $150 million dollars on the project in total and implement it within 36 months. Based on the initial goals established by NASA, just getting the spacecraft to Mars and landing it in one piece would have been a success. 

Instead, by the time the project reached completion in September 1997, the Mars Pathfinder returned 2.3 billion bits of information, including more than 16,500 images from the lander and 550 images from the rover, as well as more than 15 chemical analyses of rocks and soil and extensive data on winds and other weather taking place on Mars. Ultimately, the project was such an exemplary example of project management at work that it won the Project Management Institute’s coveted ‘‘Project of the Year Award’’ for 1998. 

What is a Project Manager?

Project managers take ownership over the entirety of the project lifecycle from start to finish, from directing team efforts to navigating day-to-day challenges, implementing project management strategies, and more. Ultimately, they are responsible for the successful competition of the project and the distribution of key deliverables and project outcomes. 

Responsibilities of a Project Manager

Project managers are responsible for a wide range of project-related duties, including but not limited to:

• Establishing and managing the project timeline  
• Assigning project tasks and delegating responsibilities to team members
• Communicating with key stakeholders
• Executing each phase of the project
• Facilitating team adaptation of project management aids and tools (such as project management software, and Gantt charts)
• Monitoring the project budget and project scope, preventing cost or scope overruns
• Troubleshoot and mitigate potential roadblocks and issues 
• Establishing set meeting schedules and facilitating team discussions
• Monitoring ongoing project progress
• Concluding the project lifecycle with relevant end-of-project responsibilities, including facilitating project reviews , and turning over deliverables

Essential Project Manager Skills

Project managers handle a wide variety of project-related responsibilities and duties, and understandably, need to wield a broad and flexible skillset. Some of the essential skills a project manager should possess include the following:

Soft Skills

Hard skills, phases of project management .

Check out the video below for an in-depth walkthrough of the five phases of the project management lifecycle. 

Read more: 5 Phases of Project Management Life Cycle You Need to Know

1. project initiation.

The project initiation phase focuses on establishing a high-level vision for the project while securing approvals from sanctioning stakeholders. This phase is not meant to dive into excessive detail, but rather to get the ball rolling and get the team thinking about what is to come.  Read more about the initiation phase .

2. Project Planning

During the project planning phase , teams build upon the vision established in the initiation phase in much more detail. First, teams must answer a few essential questions surrounding what the project will aim to accomplish, how the project will be carried out, when it will begin, on what timeline, and how project success will be measured. Once those initial questions have been answered, teams can dive into building out project infrastructure, covering essential topics such as:

• Project scope
• Deliverables
• Key stakeholders
• Goals and milestones
• Resources needed (internally and externally)
• Project timeline
• Potential risks or roadblocks
• Dependencies
• End of project outcomes

3. Project Execution

The project execution phase is the starring act of the project, and where most of the deliverables come from. During this phase, the project manager coaches and guides the team to present essential project deliverables while keeping stakeholders in the loop and monitoring progress against key milestones and KPIs. Throughout the project execution phase, project management systems, such as project management software, can make life easier by keeping track of deadlines and deliverables, serving as a platform for team member collaboration, and more.  Learn more abou t project execution.

4. Project Monitoring 

During the monitoring phase, the project manager(s) keep tabs on the progress of the project overall and the status of the team. Whether teams are on track and delivering stellar results or struggling with roadblocks and challenges, the project manager can help eliminate stressors, solve problems, and communicate updates with key stakeholders. Read more about project monitoring .

5. Project Closure

The closing phase of the project lifecycle is a time for wrapping up project activities, delivering project deliverables and outcomes, and reflecting on the wins and losses of the project overall. Communication is key within this final phase, where team members have an opportunity to reflect and celebrate. Learn more about the project closure phase .

Download Our FREE Project Lifecycle Guide

Project management methodologies.

Project management methodologies establish a guiding set of rules and principles that teams can implement in order to achieve greater efficiency while maximizing positive project outcomes. Each methodology approaches project management through a slightly different lens, providing teams with a specific set of repeatable steps to follow throughout the project lifecycle. Methodologies are rigid and cannot be used in combination with other methodologies.

Project management frameworks can exist within methodologies, providing a more focused view of how a methodologies guidelines can be applied and implemented. While the structure and rules follow the teachings of the methodology, frameworks can color in detail how and when those rules are applied in a project setting. 

Agile project management focuses on an iterative and highly flexible approach to project management that focuses on delivering the project in pieces throughout the project lifecycle, rather than all at once at the project’s conclusion. In Agile project management, teams have more flexibility to adapt to challenges and redirections than in more structured methodologies, such as Waterfall.

Best for: 

• Software development teams
• Teams dealing with high levels of uncertainty
• Teams who are creating prototypes that need multiple levels of edits and changes
• Teams working closely with external parties and stakeholders

Waterfall project management is a traditional approach to project management that involves rigid, sequential project phases. In the waterfall model, each phase of the project must be fully completed before the next phase can begin, and project deliverables are turned over only at the conclusion of the project.

• Projects with a well-defined goal
• Projects with concrete timelines
• Teams who need to define rigid project requirements early on

Project Management Frameworks

Scrum project management, as the name suggests, is inspired by the camaraderie and teamwork of a Rugby team within the Agile methodology. Led by a Scrum master, Scrum teams are encouraged to learn through their experiences, self-organize as they problem-solve, and progress throughout the project lifecycle. 

• Smaller teams tackling numerous unknowns and ever-changing variables

The Kanban framework is a subset of the Agile methodology that emphasizes continuous improvement and flexible task management. In the Kanban framework, teams utilize Kanban boards, vertical boards that separate individual task cards into categories based on their status in the project lifecycle (for example: “not started,” “in progress,” and “completed”). 

• Teams who are new to project management and looking for a simple, organized framework
• Projects with numerous individual tasks and assignments
• Teams who need quick access to a high-level view of task overviews and completion status

Critical Path Method (CPM)

Critical Path Method is a project management framework within the Waterfall methodology that identifies critical and non-critical tasks, prioritizing them based on their importance—eliminating bottlenecks and roadblocks. The CPM method emphasizes the importance of calling out relationships between tasks and task dependencies. 

• Teams managing large, complex projects
• Projects that require a large number of tasks with subtasks and dependencies
• Teams who want to maximize efficiency and prevent roadblocks from the start of the project (especially for projects that have a high likelihood of complication) 

PRojects IN Controlled Environments, or PRINCE2, is a framework within the Waterfall project management methodology that emphasizes organization and control. Frequently used in the UK and internationally, The PRINCE2 model breaks down projects into smaller, more manageable chunks in order to manage risk and resources while clearly defining team roles and responsibilities. 

• Teams who have less experience in project management (PRINCE2 follows clearly defined, easy-to-understand steps)
• Teams who need more clarity around specific role-based responsibilities
• Compartmentalizing project steps and actions

Project Management Tools

Project management software.

Project management software helps teams organize all project essentials in one place, while streamlining and simplifying the project management process overall. At every phase of the project lifecycle, project management software supports teams’ ability to assign tasks, manage deadlines, view task dependencies, track team progress against goals, access data insights, and much more. 

Read more: 10 Best Project Management Software for 2023

Project management charts , gantt charts.

Gantt charts are one of the most common planning tools in project management. In a timeline-inspired format, Gantt charts highlight tasks against the project timeline, task dependencies, and designated assignees. Gantt charts are useful for teams who want to visualize projects at a high-level view while avoiding resource overload. 

Best for: Visualizing project timelines and task dependencies

Burn-Up/Burn-Down Charts

Burn-up and burn-down charts visually represent how project tasks have been completed across a predetermined timeframe. This type of chart is popular with Scrum teams for tracking work across sprints, as it can easily reveal the total scope of work against items that have been completed or left unfinished. 

Best for: Tracking project progress

Read more: Best Project Planning Software & Tools

Collaboration tools.

Slack is a communication-focused collaboration software that enables teams to communicate asynchronously through messaging, audio calling, and video conferencing. While many project management software offerings include collaboration features, Slack is a faster solution for teams who need to communicate efficiently as project updates come up.

Miro is a collaborative mind-mapping software that can help teams brainstorm throughout the project lifecycle in real-time. The application functions as a virtual whiteboard for teams to map ideas, add digital sticky notes, and plan out timelines.

Read more: Best Collaboration Software & Tools in 2023

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A project is a temporary, time-bound sequence of tasks that aim to achieve a particular goal. Projects bring together the talents of multiple team members in order to deliver a tangible result or outcome over a predetermined span of time. Often, projects involve the work of multiple individuals, across numerous areas of expertise, requiring an upfront investment in time and resources. 

Project management provides structure and accountability to the project process while providing ongoing support to your team by way of a project manager. Here are just a few of the top benefits of project management: 

Project Management Keeps Projects on Track

According to data from Wellingtone, only 43% of projects are finished on time and within budget, and only 29% are on time. Project management structures a plan for teams to stay on time and budget ahead of time, so projects are more likely to go as planned. 

Eliminate Scope Creep

Scope creep occurs when project requirements and frame of work expand over time—and it’s one of the most significant threats to project success, with the Project Management Institute reporting that half of all projects experience scope creep. Projects that adhere to best practices in project management are more likely to stay focused on the initial objectives of the project and, ultimately, experience success. 

Enhanced Resource Management

Project management involves planning and accountability—and that can make resource management much easier. During the initial phases of project planning, teams clearly outline team roles and responsibilities while monitoring individual workloads as work progresses, ensuring that resources are allocated appropriately.

Team Coaching and Coordination

Project management efforts are traditionally led by a project manager, or at the very least, a dedicated team member who oversees team efforts while providing support throughout the project lifecycle. Having a dedicated individual who can monitor project progress, troubleshoot problems, and promote team accountability can help the project process move much more smoothly. 

While every organization’s approach to project management is different, taking stock of your goals can help guide your next move. Take time to reflect on the projects your team has completed previously. What went well? What could have been improved? 

If you don’t have the budget to hire a dedicated project manager, implementing smaller steps, such as taking advantage of a project management software solution, can help your team make big strides toward a strong project management strategy. 

Most teams will require a toolbox of project management aids, rather than a single solution by itself. In order to determine which tools are the best fit for your team, testing is key. A majority of project management software solutions offer free trials and plans, making it easy to test out a variety of options. Other tools, such as charts, planning aids, and mind maps, are free tools that can easily be tested and explored. 

Interested in learning more? Check out our FREE guide on how to choose project management software. 

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

What is the Difference Between Research and Project

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

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

Key Areas Covered

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

Research, Project

What is Research

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

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

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

What is a Project

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

Difference Between Research and Project

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

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

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

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

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

Image Courtesy:

1. “ Research ” by Nick Youngson (CC BY-SA 3.0) via The Blue Diamond Gallery 2. “ Project-group-team-feedback ” (CC0) via Pixabay

About the Author: Hasa

Hasanthi is a seasoned content writer and editor with over 8 years of experience. Armed with a BA degree in English and a knack for digital marketing, she explores her passions for literature, history, culture, and food through her engaging and informative writing.

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Bitter Creek National Wildlife Refuge

Management and Conservation

Wildlife and habitat management.

Since 1995, the refuge has served as a release site for the California Condor Recovery Program to release condors into the wild. Condor management activities include:

• Condor population monitoring; very high frequency (30–300MHz) (VHF), global positioning system (GPS), and visually providing sites for the Recovery Program to trap and process condors (assess body condition, attach transmitters).
• Twice yearly (minimum) trapping and sampling all southern California condors; monitoring contaminants in released condors (analyzing blood and feather samples).
• Providing sites to vaccinate condors for West Nile Virus and sites for supplemental feedings to maximize survivorship.
•  Maintaining temporary quarters for Service biologists performing Recovery Program activities and researchers, volunteers, and partners supporting Recovery Program or refuge goals.
• Releasing up to 15 tagged condors into the wild per year (as needed and as determined by the Recovery Program).
• Coordinating with ranches to allow condors to feed on natural livestock mortalities.

The Service also manages grassland, mixed scrub, oak and juniper woodlands, riparian riparian Definition of riparian habitat or riparian areas. Learn more about riparian , and wetland habitats that support other plants and wildlife, as well as the condor.

Wildlife Surveys

To date, plant and wildlife data collected to inform refuge management decisions include surveys for burrowing owl (Athene cunicularia) (2006), rare and endangered reptiles and amphibians (1994), small mammals (2006–2007), tricolored blackbird (Agelaius tricolor) (2006–2011), and plant surveys of Bitter Creek NWR (1997, 2009–2011). Sightings of wildlife have also been documented for San Joaquin kit fox (Vulpes macrotis mutica) (1982–2009), tule elk (Cervus elaphus ssp. nannodes) (2008–present), and other species (periodically between 1991 and 2008).

See Appendix D of the CCP for a list of surveys conducted.

Fire Management

Fire preparedness is an important aspect of refuge management. The Service suppresses all wildfires and implements fire prevention and mitigation measures (such as fuel breaks) at the wildland-urban interface (WUI) and roads. The approved update to the Fire Management Plan for Bitter Creek NWR allows prescribed burning in the form of pile burning (USFWS 2001). Pile burning is a low risk use of fire, used primarily in winter, when air quality is less likely to be adversely affected. The Service obtains the required permits to burn from the regional air quality district. Department of the Interior and Service policy require that the Service comply with all air quality regulations and obtain permits for all planned burning on the refuge.

For more information on the FWS Fire crew that is responsible for this refuge, please visit the Fire Management Page.

Cultural Resources Management

Previous cultural resource inventories have recorded sites associated with Native American use of the refuge area along with historic-period resources. To date, approximately 7.5% (1,886 acres) of the 14,096-acre refuge has been systematically surveyed as a result of 13 archaeological research projects conducted on the refuge. It is highly probable that additional archaeological sites will be exposed by human actions or natural causes in the future. Previous archaeological research includes the following. In 1982 and 1983, three land parcels were surveyed for cultural resources in anticipation of development for housing within or immediately adjacent to what later became the refuge boundary. As a result, seven prehistoric archaeological resources and three isolated artifacts were recorded within the current refuge boundaries.

Archaeological fieldwork on the refuge since its establishment in 1985 has primarily focused on compliance with Section 106 of the NHPA for a variety of undertakings proposed either by right-of way holders or by the refuge.

Conservation

Current status.

The planning process for Hopper Mountain, Bitter Creek, and Blue Ridge National Wildlife Refuges’ Comprehensive Conservation Plan and Environmental Assessment (CCP/EA) is now complete. The Final CCP/EA is available below. The Final CCP outlines the management direction and strategies for the three refuges for the next 15 years. 

Refuge Planning 

National Wildlife Refuge planning sets the broad vision for refuge management and the goals, objectives, strategies, and actions required to achieve it. Planning ensures that each refuge meets its individual purposes, contributes to the Refuge System’s mission and priorities, is consistent with other applicable laws and policies, and enhances conservation benefits beyond refuge boundaries. 

Comprehensive Conservation Plans 

Comprehensive Conservation Plans (CCPs) are the primary planning documents for National Wildlife Refuges. As outlined in the National Wildlife Refuge System Administration Act, as amended, the U.S. Fish and Wildlife Service (Service) is required to develop CCPs that guide refuge management for the next 15 years. CCPs articulate the Service’s contributions to meeting refuge purposes and the National Wildlife Refuge System mission. CCPs serve as a bridge between broad, landscape-level plans developed by other agencies and stakeholders and the more detailed step-downs that stem from Refuge CCPs.  

The 2013 Final Comprehensive Conservation Plan for Hopper Mountain, Bitter Creek, and Blue Ridge National Wildlife Refuges can be found here: https://ecos.fws.gov/ServCat/Reference/Profile/43995  

Step-down Plans 

CCP step-down plans guide refuge-level programs for: (1) conserving natural resources (e.g., fish, wildlife, plants, and the ecosystems they depend on for habitat); (2) stewarding other special values of the refuge (e.g., cultural or archeological resources, wilderness, wild and scenic rivers, etc.); and (3) engaging visitors and the community in conservation, including providing opportunities for wildlife-dependent recreation. Like CCPs, step-down plans contribute to the implementation of relevant landscape plans by developing SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) objectives, strategies, implementation schedules, and decision support tools to fulfill refuge visions and goals. This ensures that refuges are managed in a landscape context and that conservation benefits extend beyond refuge boundaries.  

Project Contacts:  

Refuge Manager

Hopper Mountain National Wildlife Refuge Complex

2493 Portola Rd, Suite A, Ventura, CA 93005

Phone: (805) 644-5185 

Fax: (805) 644-1732 

[email protected]    

Annual Field Report for Hopper Mountain National Wildlife Refuge Complex, Hopper Mountain, Blue Ridge, Bitter Creek, Guadalupe-Nipomo Dunes NWRs, California Condor Recovery Program. 2019.

CCP for Hopper Mountain, Bitter Creek, and Blue Ridge National Wildlife Refuges. HMNWR, BCNWR, BRNWR.

Law Enforcement

The National Wildlife Refuge System's Law Enforcement Division is responsible for physical security and emergency management on FWS lands. Refuge law enforcement officers have federal jurisdiction to enforce all federal laws throughout the United States.

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Support for Existing Expertise: Community-focused training initiatives to improve the safety and health of Tribal buffalo herd workers

American bison, also known as buffalo, are the largest land mammal in North America and are perfectly adapted to the harsh landscape of the high plains, capable of surviving extreme winters, vast changes in temperature, drought conditions, high humidity, and many diseases that impact other hoofed mammals. In recent decades, indigenous communities across North America and organizations such as the Intertribal Buffalo Council (ITBC) have led efforts to bring the buffalo home to Tribal lands. This work is done with many goals in mind including ecological restoration, cultural and spiritual revitalization, economic growth, and food sovereignty.

However, buffalo are not domesticated and have not been bred for docility. They still exhibit their innate defensive strategies, including aggression and heightened vigilance in comparison with domesticated livestock like cattle. In flight or pursuit, buffalo can reach speeds up to 35 mph and are surprisingly agile given their large size. For these reasons, people working around buffalo are at risk for injury and exposure to zoonotic diseases, which are infections that can spread between animals and people. These risks and the growth of buffalo herding led to initial research which documented both hazards and health and safety best practices in buffalo herding . Now, a new research project seeks to build on past work and partnerships to create relevant and culturally appropriate safety and health training for buffalo herd workers.

The Central States Center for Agriculture Safety and Health (CS-CASH) at the College of Public Health in the University of Nebraska Medical Center is one of 11 regional Centers for Agricultural Safety and Health funded by the National Institute for Occupational Safety and Health. CS-CASH’s new project, “ Establishing a Community-Based Training Network to Enhance Bison Herd Workers Safety on Tribal Lands ” aims to support the people who do the hands-on work of managing Tribal buffalo herds by employing what was learned in previous work to:

 1. Continue to monitor and understand workplace injuries, working conditions, and worker safety hazards for buffalo herd workers on Tribal lands;

2. Work with indigenous buffalo herd managers to ensure educational materials and training strategies are culturally relevant and appropriate; and

3. Help develop and support an indigenous-led training and mentorship program focused on worker and herd health.

The existing community of Tribal buffalo herd managers and workers contains the world’s foremost experts in buffalo herd management, harvesting, and processing. Tribes who are establishing their own herds may need trusted guidance and support as they work to establish their own programs. What has been lacking, however, is support for expert mentorship and training for these up-and-coming programs. This project intends to help provide this support and foster collaboration between experienced and less experienced tribal groups.

Improving Health and Safety Through Collaboration and Community

Health and safety hazards for buffalo herd workers include working with aging and repurposed equipment (often designed for cattle); working in remote locations during winter months; slip, trip and fall hazards; and high stress handling techniques. For the past five years, CS-CASH and the ITBC have worked together to hold an annual roundtable event which brings together experts and learners to discuss creative solutions, facilitate resource sharing, and document concerns regarding new and existing hazards to worker safety within the community.

Tribal communities have a strong interest in the safety and logistics surrounding cultural harvests and processing. Community events serve as an opportunity for the exchange of cultural knowledge, spiritual practice, as well as supporting community food sovereignty initiatives. This is also an opportunity to refine safety practices surrounding food preparation, to sample organs for disease and parasite monitoring, and to establish practices aimed at supporting the health and safety of the herd, the herd workers, and the broader community.

As the movement to bring bison back to Tribal lands continues to grow, we continue to work with Tribal herd workers and managers, ITBC, and other collaborators to enhance training materials, training opportunities, and support for community-led mentorship. Existing materials are available on the Central States Center for Agricultural Safety and Health (CS-CASH) website . Including annual reports summarizing discussions resulting from our annual roundtable events.

Mystera Samuelson, PhD, Assistant Professor, University of Nebraska Medical Center, College of Public Health, Department of Environmental, Agricultural, and Occupational Health

Arlo Iron Cloud Sr., Porcupine, SD Community Member

Lisa Iron Cloud, Porcupine, SD Community Member

KC Elliott, MA, MPH, Epidemiologist in the NIOSH Office of Agriculture Safety and Health.

Jessica Post, University of Nebraska Medical Center, Central States Center for Agricultural Safety and Health

Risto Rautiainen, PhD, MS, Professor, University of Nebraska Medical Center, College of Public Health, Director of Central States Center for Agricultural Safety and Health

Ellen Duysen, MPH, COHC, Assistant Research Professor, University of Nebraska, College of Public Health, Central States Center for Agricultural Safety and Health

John Gibbins, DVM, MPH, Senior Veterinary Advisor, NIOSH Office of Agriculture Safety and Health

This research is done under research cooperative agreement award U54OH010162 supported by the Centers for Disease Control and Prevention National Institute for Occupational Safety and Health (CDC/NIOSH) under CDC funding opportunity RFA-OH-22-002. The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement, by CDC/HHS, or the U.S. Government.

One comment on “Support for Existing Expertise: Community-focused training initiatives to improve the safety and health of Tribal buffalo herd workers”

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I am truly amazed that this work with buffalo and the Indian nation exists. I had no idea studies and help was there to help the Indian reservations re-populate the Bison herd and take care of them. I am sure the help with managing the care and keeping the workers safe is a big step forward. I applaud DR. John Gibbins for his work on this subject.

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