science 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.

If you have a disability and experience difficulty accessing this content, please submit an email to [email protected] for assistance.

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Project management tips for researchers.

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7 Essential Project Management Skills for Scientists

Industry Advice Science & Mathematics

Project management skills are important for anyone working in an environment where there are many tasks to be done, deadlines to be met, and teams to engage with. In fields such as biotechnology and pharmaceuticals, project management skills allow scientists to balance the rigors of experimentation with product development’s formal processes.

Scientists who develop their soft skills in addition to honing their research skills can become especially effective contributors to multidisciplinary teams. In particular, scientists with project management acumen enable their teams to take products to market faster, bring clinical benefits to physicians and patients, and deliver financial benefits to companies and their shareholders.

Here’s a look at seven key project management skills that help scientists advance in organizations ranging from startups to large, multinational firms. 

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Project Management Skills for Scientists 

1. leadership.

Proficiency in “the art and science of getting things done” is the most important skill for a project manager, says Christa Dhimo, professor of informatics and biotechnology at Northeastern University’s College of Science . Leadership abilities in this industry are essential because project management roles at a biotechnology or pharmaceutical company present two unique sets of challenges. 

The first is that the scientific method and the corporate world often do not mesh. “Scientists are trained to think about their research question, the theory and hypothesis, and the gaps in their research. Curiosity is embedded in what they study,” Dhimo says. 

Commercial environments, however, are often dictated by standard operating procedures that don’t always lend themselves to that level of critical thinking. A project manager may need to tell a team of scientists to write up their findings but put them aside and shift their focus back to the project scope at hand.

The second challenge is that many founders and CEOs in biotech and pharma have a scientific or medical background themselves. “They have a full appreciation of what it takes to build something that will go into a living being,” Dhimo says, and thus they may take a more hands-on approach than executive leadership in other industries. “You need temperament and perspective to know what to push and what to pull,” she adds.

In industries such as pharma and biotech, the products that companies sell must meet rigorous scientific and regulatory guidelines. To be approved, these medications or treatments must demonstrate safety and efficacy while also addressing an unmet need in the market. That is, they need to show that they do something that no other product currently does.

For project managers, that means starting with the end in mind, Dhimo says—even if “the end” may be years away. According to Pharmaceutical Research and Manufacturers of America , or PhRMA, it typically takes at least 10 years for a medicine to progress from initial discovery to release on the marketplace. 

“You need to focus on your regulatory pathway from the very beginning of the project,” Dhimo adds. Without a clear path through product development, clinical trials, and approval, the process becomes scientific research, which is not profitable for a company. 

3. Collaboration

Successful drug development depends on a collaborative culture, which can be quite different from individual work done in an academic lab. 

As the Project Management Institute (PMI) points out , effective collaboration depends on defining roles and priorities clearly, managing relationships with senior leaders and research teams, and working across departments to develop a timeline. PMI notes that larger companies may have a more formal structure for managing resources and projects than smaller companies, where everyone may be working on a single experiment.

Along with the leadership skills Dhimo highlights, communication skills are a critical part of supporting collaboration. A commentary in Science magazine notes that clear communication on all project elements—from the big-picture objectives and vision to the day-to-day tasks and requirements—keeps the entire team on track. This is especially true when research teams have external collaborators who may not be part of regularly scheduled meetings.

4. Resource Allocation

Managing spending and personnel is an important project management skill for scientists. In biotech and pharma, the burn rate—or negative cash flow as a company is spending money without generating revenue—can exceed $4 million per month. 

These resources must be managed carefully in order to carry a company through the drug development process, Dhimo says. “You need to be smart about where the spending goes, and you need to manage expectations.”

Project managers must strike a balance. Hiring too many people and running too many experiments could cause a company to run out of money too quickly, she notes. On the other hand, under-assigning resources and waiting until additional funding arrives could slow down the research process and take away a company’s competitive advantage. 

An American Society for Microbiology article notes that resource allocation for a project must also account for other experiments that a company may be doing. Supplies and equipment are expensive, and lab time and personnel not unlimited. An effective project manager should understand the needs of their project in the context of other work and be able to communicate the importance of collaboration to the research team. 

5. Documentation

The process of getting a drug approved requires detailed filings with regulatory bodies such as the U.S. Food and Drug Administration (FDA), the European Medicines Agency, and the Pharmaceuticals and Medical Devices Agency in Japan. At each stage of the process, companies can expect to thoroughly document their research findings. For project managers, this requires careful attention to detail.

“It’s like the biggest dissertation you’ll ever do in your life,” Dhimo says. “You need to be able to explain your product in a way that non-scientific people can understand, to put it in a template that the agency provides, and show that it will be safe and effective and address an unmet need.”

Thorough documentation will help project managers shorten the timeline and improve the budget for future experiments, whether in their current roles or potentially at a new company. In particular, keeping track of mistakes made and obstacles encountered during one project will offer lessons learned for the next one. 

6. Priority Setting

Effectively setting priorities for product development will set a company on a course to deliver the right products to the right markets. P riority setting ensures that a project as a whole aligns with the company’s business strategy. This way, executive leadership sees that a project will bring value to a company and is a worthwhile investment of money, resources, and time. 

When comparing several projects, PMI recommends evaluating each project’s value based on a number of financial, quality, and regulatory objectives to create a prioritized list. From there, a company can apply available resources to each project based on its priorities. This ensures, for example, that a project likely to encounter a bottleneck due to insufficient resources is not deemed a high priority, as work would ultimately stop when resources run out.

7. Ability to Pivot

The uncertainty of the drug discovery process makes it difficult to develop precise timelines, allocate resources, and predict success. As a result, while the project management process is conceptually the same in pharma and biotech compared to industries such as IT or manufacturing, the complexities of the drug development process need to be taken into account.

Sometimes a drug or medical treatment will fail to receive regulatory approval or otherwise meet expectations through no fault of the product team’s careful work. A treatment may be developed on time, on budget, and within the parameters of the project scope, for example, but patients may not respond to the treatment as expected. In these cases, the ability to pivot is a valuable project management skill for a scientist, Dhimo says.

When this occurs, a common next step is to work with the FDA to redesign a clinical trial, perhaps to focus on a different disease or different group of patients (referred to as a cohort). Another option is to undergo a supplemental study using a subset of the data collected in the initial trial and a subset of the initial research team. In some cases, another company may acquire the assets and intellectual property from a trial, believing that the trial has value even if it wasn’t approved.

How to Build Your PM Skills as a Scientist

With the right skills and knowledge, a career in biotechnology or pharma can be lucrative, stable, and rewarding. Earning a graduate degree in a relevant field, such as biotechnology, is an excellent means of developing the skills and expertise required to work and excel in the field.

The Master of Science in Biotechnology program at Northeastern University combines interdisciplinary training in science with an emphasis on business skills to prepare students for research, managerial, or technical professional roles.

Download our free guide below to learn more about the skills and experience you need to advance your biotechnology career.

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How to Manage Your Research Project

You hear it everywhere: the project was delayed and over-budget. Whether it be bridges, space shuttle launches, or federally funded science projects, poor research project management can lead to abysmal consequences. Consider the example of the BepiColombo mission, a project to send an orbiter and lander to Mercury. While originally budgeted at 450 million euro for a 2008 mission, only the orbiter was developed in 2013 and at a cost of over 1 billion euro!

In academic sciences, project management for scientists is important. While a contract might be extended for a federal grant, the funders will remember who could not manage a project correctly. Meanwhile, if working on a degree, such as a PhD,  managing a large research project is critical for sticking to a timeline. Given that the typical constraints of a project include the science goals, time, risks, and costs, research project management becomes critical to the success.

Why Project Management Matters in Science

Successfully obtaining and completing a research grant requires intellectual and scientific self-awareness. In this honesty, a scientist can better predict the true costs and requisite time to complete his or her projects. Furthermore, having a detailed and well-designed project plan is foundational to convince a funder that you can do a project efficiently.

Managing a scientific research project often requires a variety of organizational and leadership skills. Scientists naturally acquire these skills over time due to experiences with failed experiments, planning a research question, and writing manuscripts. Yet, a principal investigator also needs to be aware of the more abstract caveats of a research laboratory and its personnel. Research teams are affected by internal politics, including the resources of their institution and the skills and motivation of the team members, The practical demands of any project can be easily over-looked; therefore, management techniques are needed to overcome the constraining factors.

So, what can a scientist do? Being a skilled and creative scientist isn’t enough – they must also manage projects carefully.

Key Areas to Manage

When managing a research project, there are several concerns that an investigator might have. There are various important considerations for conducting science team meetings, which include preparation, participation, conduct, and follow-up. Furthermore, information flow should be carefully maintained to provide sufficient information so that all team members understand the process and the project. Finally, a researcher must be ever vigilant of the boundaries of the project (i.e., the scope of the research). Without careful monitoring, the scope of the research may creep into a new field.  A project might thus lose its focus and the planned goals may never be achieved. It is important to remember the desired outcome and plan tasks, while regularly reviewing the progress and modifying the next steps accordingly.

Management Tools

Several tools exist to help manage a research project, including Evernote for science , citation management software programs (e.g., Endnote or Mendeley) , calendars that can be synced across devices or shared with collaborators (e.g., Google Calendar) , and drives that can help researchers share or backup data for collaborations or protection from cyber attacks that can result in data loss.

In addition to these ubiquitous tools, there are additional management tools that can help a researcher be both a scientist and a project manager. One such tool is LabGuru , which offers a collaborative project planning platform and allows for document storage. Meanwhile, a schedule developer that allows for timelines, flow charts and activities/responsibilities to be communicated, such as Microsoft Project and GANTT charts provide more centralized tools.

  As scientists prepare to manage large research projects, one should make several considerations within the project constraints. Furthermore, many research project management tools are available to facilitate the requisite tasks. As academic sciences become more competitive due to limitations in funding and resources, a well-managed and realistic project plan can be one way to guarantee research success.

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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.

Related Courses

Project management: Recent developments and research opportunities

• Published: 16 June 2012
• Volume 21 , pages 129–143, ( 2012 )

Cite this article

• Nicholas G. Hall 1  

4234 Accesses

45 Citations

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This paper studies the business process known as project management. This process has exhibited a remarkable growth in business interest over the last 15 years, as demonstrated by a 1000% increase in membership in the Project Management Institute since 1996. This growth is largely attributable to the emergence of many new diverse business applications that can be successfully managed as projects. The new applications for project management include IT implementations, research and development, new product and service development, corporate change management, and software development. The characteristics of modern projects are typically very different from those of traditional projects such as construction and engineering, which necessitates the development of new project management techniques. We discuss these recent practical developments. The history of project management methodology is reviewed, from CPM and PERT to the influential modern directions of critical chain project management and agile methods. We identify one important application area for future methodological change as new product and service development. A list of specific research topics within project management is discussed. The conclusions suggest the existence of significant research opportunities within project management.

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Open Source vs Proprietary Project Management Tools

Closing Thoughts

Further Research Opportunities in Project Management

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Nicholas G. Hall is Professor of Management Sciences in the Fisher College of Business at The Ohio State University. He holds B.A., M.A. degrees in economics from the University of Cambridge, a professional qualification in accounting, and a Ph.D. (1986) from the University of California at Berkeley. His main research interests are in tactical operations issues, especially project management, scheduling and pricing, public policy and sports management problems. He is the author of over 70 refereed publications, and has given over 260 academic presentations, including 88 invited presentations in 20 countries, 6 conference keynote presentations and 6 INFORMS tutorials. A 2008 citation study ranked him 13th among 1,376 scholars in the operations management field. He is a Fellow of the Institute for Decision Making under Uncertainty of the Chinese Academy of Sciences. He won the Faculty Outstanding Research Award of the Fisher College of Business in 1998 and 2005.

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Thirty-four project managers of life-science research projects were interviewed to investigate the characteristics of their projects, the challenges they faced and their training requirements. A set of ten discriminating parameters were identified based on four project categories: contract research, development, discovery and call-based projects--projects set up to address research questions defined in a call for proposals. The major challenges these project managers are faced with relate to project members, leadership without authority and a lack of commitment from the respective organization. Two-thirds of the project managers indicated that they would be interested in receiving additional training, mostly on people-oriented, soft skills. The training programs that are currently on offer, however, do not meet their needs.

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Citizen science has been studied intensively in recent years. Nonetheless, the voice of citizen scientists is often lost despite their altruistic and indispensable role. To remedy this deficiency, a survey on the overall experiences of citizen scientists was undertaken. Dimensions investigated include activities, open science concepts, and data practices. However, the study prioritizes knowledge and practices of data and data management. When a broad understanding of data is lacking, the ability to make informed decisions about consent and data sharing, for example, is compromised. Furthermore, the potential and impact of individual endeavors and collaborative projects are reduced. Findings indicate that understanding of data management principles is limited. Furthermore, an unawareness of common data and open science concepts was observed. It is concluded that appropriate training and a raised awareness of Responsible Research and Innovation concepts would benefit individual citizen scientists, their projects, and society.

Introduction

Citizen Science (CS) is increasingly viewed as a viable methodology for scientific research, either as a bottom-up initiative or as a collaboration with the professional scientific community, NGOs, or government organizations. Its importance is acknowledged in legislative contexts, for example, in the EU Open Science policy (European Commission, 2019 ) and the Crowdsourcing and Citizen Science Act in the USA (US Government, 2017 ). The importance of CS throughout history is undisputed—many famous scientists depended on alternative sources of income. The era of professional science is very much a modern phenomenon. Traditionally, CS was often perceived as an exercise in data collection. However, citizen scientists have increasingly undertaken epistemic roles such as analysis and interpretation, with the online Zooniverse platform being an exemplary model. Thus, while CS is synonymous with pursuing orthodox scientific knowledge, it is also interesting to recall that there is a countercultural dimension to CS (McQuillan, 2014 ). Indeed, CS is often seen as a vehicle for democratizing science, for which an effective data stewardship process is vital (de Sherbinin et al., 2021 ). However, the core concept of democratization of science has been challenged, e.g., Strähle and Urban ( 2022 ).

One viable but generally unexploded objective for CS communities is collaborating with national and local government agencies to influence policy. Conversely, CS offers a tool for diverse governmental agencies to engage with local communities (Cvitanovic et al., 2018 ). Indeed, all three courses of democratic action—monitorial, deliberative, and participatory, differ significantly and require context-sensitive, open data platforms (Ruijer et al., 2017 ). Citizen scientists can usefully contribute to each model—collecting, analyzing, and interpreting data to inform evidence-based policy formation. However, the data informing such policies must be of adequate quality and quantity.

CS initiatives face many challenges. Prominent among these are trust and quality assurance of data—topics well documented by the professional science community. However, professional scientists also grapple with diverse data-related issues. Other challenges include inclusivity and polarization (Cooper et al., 2021 ). Unlike the professional science community, CS projects often know very little about their participants, possibly due to a reluctance to collect and steward personal data (Moczek et al., 2021 ). However, a thorough understanding of their demographics, experiences, and understanding of data is essential if meaningful outcomes in orthodox science and policy formation are desired. Thus, this paper reports on a survey to obtain such an understanding.

Currently, there is no universally agreed definition of CS. One study identified 35 definitions (Haklay et al., 2021 ). Such ambiguity is problematic from a policy perspective, but a narrow definition risks excluding valid activities. A need for a standardized international definition has been highlighted by Heigl et al. ( 2019 ). For this discussion, CS is considered the pursuit of scientific knowledge undertaken or contributed to by those with no direct or indirect scientific role in their professional lives. While acknowledging efforts to rebrand citizen science as community science (Lin Hunter et al., 2023 ), this discussion adopts a holistic approach by not willfully excluding any initiative or participant that could be reasonably categorized under the above definition.

A cursory examination of the literature confirms the popularity of CS. Thus, there has been increased interest in both citizen science practice and the contributing volunteers. A Greek study by Galanos and Vogiatzakis ( 2022 ) is probably archetypical of critical stakeholders’ attitudes toward CS, such as NGOs and government agencies. Here, awareness of the term "Citizen Science” is relatively low, but familiarity with the concept is high (65%). The proliferation of definitions probably contributes to this situation. While the CS concept is viewed positively, various concerns were noted, including some concerning data quality. Motivations for participating in CS initiatives have been studied in the UK (West et al., 2021 ), while a literature survey identified an urgent need for participant diversity if initiatives were to maximize their impact (Pateman and West, 2023 ). While CS is sometimes portrayed as empowering marginalized communities, it may risk reinforcing inequality unless specific contexts are carefully considered (Lewenstein, 2022 ). Moczek et al. ( 2021 ) surveyed the citizen science landscape in Germany and found that the level of knowledge in projects regarding contributing volunteers was shallow. As Germany is unlikely to be an outlier, this finding has profound implications for project impact. The impact of a CS project cannot be decoupled from, amongst others, the quality of the collected data.

The potential of CS to inform evidence-based decision-making and enable primary research is compromised without practical, verifiable data collection and management practices. Thus, data management within CS projects has been studied extensively in the literature. Bowser et al. ( 2020 ) surveyed CS projects, examining the entire data lifecycle and developing recommendations concerning data access and quality. Shwe ( 2020 ) considered data management in CS through the lens of the DataONE lifecycle framework, concluding that this framework only partially fulfills CS requirements. Other researchers explored data processes within CS from the perspective of data justice, concluding that citizen scientists do not benefit as much as the professional science community and governments (Christine and Thinyane, 2021 ). Such a conclusion highlights a power balance concern in CS projects, demanding that additional ethical decisions relating to open data practices, including data governance, be implemented (Cooper et al., 2021 ).

Skepticism of CS-derived data permeates the professional scientific community, compromising the potential of CS for sustainable impact. For example, the potential of CS as a complementary but non-traditional approach to helping measure the Sustainable Development Goals (SDGs) has been acknowledged. Still, data quality is highlighted as a significant obstacle (Fritz et al., 2019 ). While data quality may be viewed exclusively through the lens of methodology, relatively minor issues can also contribute. For example, a water quality assessment found that emotional attachment to a site contributed to overestimating water quality (Gunko et al., 2022 ).

One approach to reducing concerns about data quality in CS initiatives is through explicitly communicating data management practices (Stevenson et al., 2021 ). Alternatively, Downs et al. ( 2021 ) advocate the need for quality control and assurance throughout the entire data lifecycle, from project conception to its conclusion. The critical role of reviewers of curated data in ensuring trust by both the professional science and society is highlighted by Gilfedder et al. ( 2019 ). In the view of Balázs et al. ( 2021 ), data quality is a methodological question that is particularly challenging due to the ambiguity of the term. This debate has been ongoing for several years; see, e.g., Cruickshank et al. ( 2019 ), Ratnieks et al. ( 2016 ), Lewandowski and Specht ( 2015 ), and Bird et al. ( 2014 ).

It is increasingly acknowledged that data quality is multifaceted, and the idea that data collected by professional scientists represent the gold standard is no longer tenable. Indeed, it is argued by Binley and Bennett ( 2023 ) that there is a double standard in operation in how professional scientists view data collected by citizen scientists as biases and limitations exist in all datasets. Other research by Mandeville et al. ( 2023 ) suggests a complementarity between data collected by professionals and participatory scientists, especially in globally protected areas. Diverse solutions have been proposed. A permissioned blockchain network could potentially manage data ownership and provenance (Lewis et al. 2022 ). The use of AI on mobile Apps has been demonstrated to improve quality in a birdsong CS initiative (Jäckel et al., 2023 ). Nonetheless, there remains a need, especially for those driving CS initiatives, to further understand participants’ expectations and aspirations regarding data, especially open data (Fox et al., 2019 ), and Intellectual Property Rights (IPR) (Hansen et al., 2021 ).

In conclusion, a study by Groom et al. ( 2017 ) identified CS data as being the most restrictive due to its licensing conditions. This means that its reuse by academia, research institutes, and government agencies is limited, thus significantly reducing its potential impact. This singular instance succinctly illustrates the need for increased awareness of data issues and the adoption of robust and transparent data management policies in future CS initiatives.

Contribution

One of the earliest surveys of CS regarding data quality and approaches to validation was that of Wiggins et al. ( 2011 ), who surveyed CS projects from the Cornell Lab of Ornithology’s CS email list. Contributors to this survey were mainly documented contacts for individual CS initiatives, but some were identified from an online community directory. A more recent survey was completed in 2016 by the EU Joint Research Centre (JRC). This survey adopted an online methodology and explored data management practices among citizen scientists (Schade and Tsinaraki, 2016 ).

The research described here both complements and differs from that described above. It is a continuation of research documented over a decade ago. It is also a response to the invitation of Schade and Tsinaraki ( 2016 ) to undertake further complementary research on data practices in CS. The scope of this survey is broader as more demographic and project-specific details are requested. However, it goes deeper into participants’ understanding of data, how data is managed in their respective projects, the degree to which their projects align with the open science model (Vicente-Saez and Martinez-Fuentes, 2018 ), and awareness of the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles (Jacobsen et al., 2020 ). The survey also explores any training participants may have received in their engagement with CS initiatives.

Methodology

A survey comprising two distinct but overlapping questionnaires was designed. The survey was administered in two phases. In Phase 1, the CS community was targeted, while Phase 2 focused on the general public.

For Phase 1, a questionnaire was constructed to elicit the CS community’s broad understanding and experience of CS. Questions followed seven themes—demography, their CS project, experience as a citizen scientist, data collection, data management, data dissemination, open research, including Responsible Research and Innovation (RRI), training received, etc. The design of the questionnaire was influenced by findings from the literature, including O’Grady and Mangina ( 2022 ) and Schade and Tsinaraki ( 2016 ). Structurally, the research design is descriptive, comprising a survey of 47 questions designed for quantitative analysis. Practically all questions could be answered by selecting pre-formulated answers, for example, “Yes”, “No”, or “I do not know”. A combination of single and multiple options for answers was used. Most questions were compulsory.

An online data collection approach was implemented. The survey was constructed in Google Forms and subsequently translated into several languages—French, German, Greek, Italian, Polish, Portuguese, Spanish, and Turkish. Appropriate background information on the project and its motivations was provided, enabling potential participants to decide whether to complete the survey. It was emphasized that no identifiable or personal information would be requested or should be provided. Likewise, no identifiable details of projects were requested. Participants were informed that the data would be harnessed for scientific publications and reports. Only when participants had consented could they access the core survey. Data was only stored when the final submit button was pressed. The survey was demanding time-wise; thus, potential participants were forewarned that it would take almost 30 min to complete.

Various communication channels were harnessed to advertise the survey; these included fora for citizen scientists, including those of the European Citizen Science Association (ECSA) and Zooniverse. As the survey was anonymous, remuneration was impossible. As a token of our gratitude, a donation was made to UNICEF.

Phase 2 focused on the general public. Here, the intention is to establish a baseline for comparison. The questionnaire harnessed in Phase 1 was adopted, focusing on data concepts and training while excluding the core questions about CS. Again, the questionnaire was constructed using Google Forms. In this case, however, the services of Prolific Academic Ltd. were availed of to recruit participants. Thus, specific population characteristics could be controlled for, restricting participants geographically and ensuring gender balance. Participants were generally multilingual but listed English as a second language. The survey was relatively short, taking about six minutes on average. Again, participants were given a description of the study and how the data would be utilized and shared, and they were asked for their consent before commencing the survey.

One-hundred and twenty citizen scientists completed the survey. After a rigorous quality check, 100 submissions were deemed consistent. The resultant dataset was then encoded and analyzed using MS Excel.

The gender profile constituted 53% of participants identifying as female, 45% as male, and 2% preferred not to say. The age profile ranged from 18 to 65+. The largest subgroup, 31%, was within the 35 to 44 age group. Interestingly, 13% were in the 65+ category. Participants came from 15 different European countries, with 5% from outside Europe. Over 50% of participants defined themselves as active citizen scientists, with 40% identifying as project leaders.

Biodiversity, earth science, and environmental science accounted for almost 80% of the CS projects. The geographic scope of projects ranged from neighborhood to continental, with regional (32%) and country (23%) being the most popular. Project timescales ranged from 1–4 years (44%) to more than four years (35%). Funding was generally sourced internationally (32%) and nationally (19%). Thirteen percent (13%) were unaware of how their project was funded. Academic institutions (46%) led projects, while NGOs initiated 19%. Only 55% of participants collaborated with the project leader, while 38% collaborated with people they knew only through their CS activities. Over 63% of participants contributed to project management or decision-making.

Conservation and nature protection were the primary motivations for engagement in CS (66%), followed by education and learning (62%). Most participants were active in CS for less than five years; however, two claimed they had been active for up to 50 years. Participants contributed to all the standard CS activities, from problem definition to analysis to interpretation. Predictably, data collection was the predominant activity for 85% of participants (Fig. 1 ).

How citizen scientists contribute to initiatives.

Mobile Apps were used by 45% of participants to collect data, while 19% used a traditional paper-based approach. Participants were well-informed as to whether their data included personal and location information. However, 20% of participants could not recall how informed consent was obtained concerning the potential use of the collected data.

Almost a quarter (26%) of participants were unaware of the existence of a data management plan. Similarly, 24% were unaware of quality control processes, while 26% were unaware that metadata or documentation was available. Notably, 43% of participants were unsure of what kind of license governed their project’s data. However, 73% of participants knew there was a dedicated contact person for queries on the data collected by their projects.

In the case of data dissemination, 37% claimed that data was made publicly available as datasets, mainly in a post-processed format (34%). However, 22% of participants indicated that project data was not available to the public.

When asked about their understanding of open research, a good awareness of its principles was reported, especially concerning access and, to a lesser degree, data. Awareness of open science was surprisingly average at 54%. With the apparent exception of open innovation, participants had, in the main, encountered these terms as part of their CS activities (Fig. 2 ).

Awareness of open research pillars.

A good awareness of GDPR was observed. Understanding of the FAIR data principles (37%) and RRI (30%) were relatively low. As can be seen from Fig. 3 , participation in CS contributed to participants’ knowledge of these concepts, including GDPR.

Awareness of relevant concepts and terminology.

To understand their experiences, participants were asked about the training they had received as part of their CS activities. Figure 4 illustrates the breakdown between formal and informal training.

Training received by participants.

Participants received training on core activities relating to CS, especially on data collection protocols, analysis, and protection. Encouragingly, training was obtained on all aspects of RRI. Apart from data collection protocols, the predominant approach to training was informal. Despite the range of training, the depth was relatively shallow in specific essential topics, including ethics, gender, and general legal issues.

A good awareness of open data repositories was demonstrated (Fig. 5 ). Moreover, such repositories were accessed outside CS (55%) and as part of CS initiatives (38%).

Awareness and use of data repositories.

Finally, participants were asked about their views on sharing their collected data. With the notable exception of for-profit organizations, attitudes were very positive (Fig. 6 ).

Attitudes to data sharing.

The second phase of the survey, that of the general public, was completed by 115 participants. After the quality check, 108 participants were retained. Some corresponding results from the citizen scientists survey are included for comparative purposes. A good gender balance was observed—52% female, 47% male, with 1% preferring not to say. The age profile was dominated by the 25–34 group (38%). Participants represented 21 European countries. This survey was completed in English. All participants claimed proficiency in English, usually as a second language.

In the case of democratic models, a greater awareness of participative democracy was reported (Fig. 7 ), with citizen scientists being more aware of the concept (59%) than the general public (42%).

Awareness of democratic models by the public and citizen scientists.

Perhaps the most striking result was that almost 88% of the European public claimed they had not encountered the term "Citizen Science” . Moreover, 56% of the public stated they had not encountered any alternative models, or synonyms, of CS (Fig. 8 ). Over half of the citizen scientist population was familiar with the terms "community science” (53%) and "participatory science” (51%).

Familiarity with alternative models of citizen science.

When offered a diverse selection of definitions of CS, the most popular was that of National Geographic for both the public and citizen scientists (Fig. 9 ), namely, Citizen science is the practice of public participation and collaboration in scientific research to increase scientific knowledge. Through citizen science, people share and contribute to data monitoring and collection programs. Usually, this participation is done as an unpaid volunteer .

Preferred definition of citizen science.

Over half the general public is aware of open access (63%) and open data (56%). However, awareness of all four pillars of open research is greater amongst the CS community (Fig. 10 ).

Knowledge of open research pillars.

The general public reported a very good awareness of GDPR (68%); however, for other concepts and terms, their knowledge was less than that of the CS population (Fig. 11 ).

Familiarity with some common open science concepts.

Formal training received by the general population in data protection, ethics, and legal issues was noticeably larger than that reported by citizen scientists (Fig. 12 ). This pattern was replicated in the case of informal training (Fig. 13 ).

Formal training received by participants.

Informal training received by participants.

In each case, citizen scientists received more training in public engagement, open science, and governance—three RRI keys. In the case of gender and ethics, the general population received more training, both formal and informal.

The implications of these results are now considered from the perspective of CS as an orthodox science methodology and a vehicle for local communities to further engage in democratic processes.

A recurring theme in the literature is the profile of the average citizen scientist—caucasian, middle-aged, college education, good socio-economic income, and male. In this survey, most participants were female and in the 35–54 age category. The results neither confirm nor challenge the stereotype of the average citizen scientist. Thus, for the credibility and integrity of arbitrary CS initiatives, inclusion remains both an objective and a priority.

CS is not a homogeneous construct. Other models, for example, community science, are broadly similar, but subtle differences and priorities may exist between them. For those seeking to harness CS, it is essential to remember that diverse communities exist. Moreover, people may not brand themselves as citizen scientists, even though their activities are archetypical of CS. It should also be noted that awareness of these terms, as well as CS amongst the general public, seems relatively low. This would suggest that projects are poor at communicating their objectives, motivations, and activities. When promoting projects with policy objectives, an awareness of the inherent diversity within the broad CS field and its communities is essential.

While many might assume open research is essential to citizen scientists, awareness of its founding principles is mixed. Most surprisingly, "Open Science” is not especially well-known. Open Access and, to a lesser extent, Open Data are better known. However, it could be the case that these terms are understood to be synonymous with Open Science. Considering other data concepts, GDPR is well-known possibly due to the recent emphasis on data protection. Other key concepts, such as the FAIR principles and RRI, are not well-known. However, in all cases, the general terminology is known more to the CS community than the general public.

Data collection was the most common activity by far. However, a good awareness of the sensitivity attached to identifiable and location-aware data was observed. A good understanding of how projects managed data was also observed. However, 20%-25% of participants regularly reported that they did not know or had forgotten when asked. This observation suggests that greater awareness is needed of all aspects of the data management cycle within CS initiatives. Understanding of the data licensing was very poor, indicating that participants were unaware of how data could or could not be used going forward.

Citizen scientists received both formal and informal training as part of their preparation to engage in their respective initiatives. While the range of training received was good, the participation rate was disappointing, almost consistently less than 50%. As expected, training mainly focused on data collection and analysis protocols. However, training in other essential topics, such as ethics and open science, was limited.

Recommendations

Several recommendations have been distilled from the surveys and are applicable across initiatives, regardless of domain or motivation.

Responsible research and innovation

Many of the issues raised in this survey can be usefully considered under the broad umbrella of RRI.

Public Engagement—As well as the explicit goal of widening participation in projects by including diverse actors and societal stakeholders, inclusion should be interpreted broadly to include age and socio-economic profile, gender, and minorities.

Gender—As well as gender balance, the gender dimension should be integral to all activities.

Education—As well as training in diverse topics pertinent to the CS initiative under development, a more holistic approach should be followed, including the philosophy and practice of modern science. Where a policy outcome is sought, a clear understanding of how policy formation is manifested in practice should be promoted. The role of evidence, experiential and contextual, for example, in decision-making should likewise be considered.

Open Science—While citizen scientists are sympathetic to the objectives of Open Science, a deeper understanding of Open Research is essential to enable them to make more informed decisions and maximize the impact of their efforts.

Ethics—Aside from standard ethical issues covered in legislation, each project may create unique ethical issues. Additionally, ethical issues may arise during a project. It is essential that participants are aware of potential ethical issues and can recognize them as they arise. How to conduct ethical CS remains an open question (see, e.g., Rasmussen, 2021 for a treatment).

Governance—Inclusive of all the other RRI keys, governance remains problematic in its implementation. It should be emphasized that there is a crucial data dimension to governance (Cooper et al., 2023 ).

Data management

A better understanding of how data is managed within CS initiatives is needed. Training, as highlighted under RRI, is an obvious vehicle. Availability, licensing, access, sharing, and quality control should be a crucial part of any CS project briefing so that participants can make an informed decision about their contributions. Informed consent is increasingly important in the future due to the increased monetization of data (Quigley et al., 2021 ).

Diversity and inclusion

Citizen scientists are not a homogenous group and cannot be considered representative of an average population. Thus, inclusion is an omnipresent challenge that CS initiatives must continuously and proactively address. Crucially, any CS initiative seeking to inform policy formation must be demonstrably sensitive to the profile of its participants.

Awareness of CS

The general public lacks an understanding of CS and equivalent models. There is a need for all actors and stakeholders to promote and educate the public on the broad CS model, including its history, diverse forms, objectives, and potential. Such activities align with public engagement as considered under RRI but differ in scope and purposes.

Limitations

This study is constrained in terms of its population size. Thus, the findings cannot be regarded as definitive but rather indicative. However, the study is comparable with others in this area. For example, that of Schade and Tsinaraki ( 2016 ) attracted 121 projects. Likewise, the survey of Wiggins et al. ( 2011 ) attracted 128 project profiles but only 63 fully completed surveys. Thus, the survey reported here is typical. However, as an online survey, those who were not computer literate could not participate.

Future work

The experiences and perspectives of citizen scientists remain underexplored. This study contributed to a better understanding, but further research is needed. A complementary study but one that is deeper through replication at the country level across Europe would yield additional insights into local conditions. Such insights would provide a proper foundation for a European strategy for incorporating the CS model into policy definition and local governance.

Additional training for the CS community in diverse areas, including data management, would be beneficial. A competence framework akin to that proposed by the FabCitizen project (Pawlowski et al., 2021 ) could be explored further.

Finally, a deeper understanding of identity amongst citizen scientists in all their manifestations would be informative. A phenomenological study may well yield additional insights that complement the predominantly descriptive and quantitative approaches adopted to date.

CS is increasingly permeating the modern scientific culture. It offers intriguing possibilities to increase scientific literacy at a time when disinformation has become a widespread phenomenon. Moreover, harnessing CS and similar paradigms to inform policy and contribute to democracy is viable and intriguing. This work offers a snapshot of the experiences of citizen scientists on the ground and makes concrete recommendations as to how their contribution could be strengthened going forward. CS has had a noble history since the earliest times. With adequate support, this tradition can be continued to aid in confronting the myriad of challenges currently facing society.

Data availability

The datasets generated during this research are available from the authors upon reasonable request.

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Acknowledgements

The encouragement and support of the ISEED consortium are gratefully acknowledged. The research has been supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 960366, ISEED—Inclusive Science and European Democracies ( https://iseedeurope.eu/ ).

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MO researched and defined the methodology for the survey. EM organized the collection of the data. Both authors contributed to the data analysis and the interpretation of results.

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O’Grady, M., Mangina, E. Citizen scientists—practices, observations, and experience. Humanit Soc Sci Commun 11 , 469 (2024). https://doi.org/10.1057/s41599-024-02966-x

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Received : 22 December 2023

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DOI : https://doi.org/10.1057/s41599-024-02966-x

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3 PhD Scholarships Available

The University of Nairobi and the University of Copenhagen recently received a Grant from DANIDA (Ministry of Foreign Affairs of Denmark). The Grant has a provision for three (3) PhD scholarships and the successful candidates will register in various Departments of the University of Nairobi. The project research title is “Improving milk and chicken meat quality and safety in the Kenyan food sector (QUALIFOOD). The scholarships will cover payment of tuition fee for university of Nairobi, research cost, exchange programs and a living monthly stipend and are strictly tenable for three (3) years only. Antimicrobials are widely used in livestock production and consequently, Antimicrobial resistant (AMR) bacteria as well as antimicrobial residues are commonly found in food and milk, leading to unintended exposure of consumers to resistant microbes and antimicrobial residues. The current project will explore the context and practice of antimicrobial use in dairy and chicken farms, assess antimicrobial residues and presence of resistant microorganisms in milk and chicken meat in the production chain, and determine the health effects of unintended exposure of farmer and consumers to resistant bacteria and antimicrobial residues. Eligible candidates are invited to apply for the following available PhD scholarships.

One Position: PhD in Microbiology (Bacteriology Option) - Department of Veterinary Pathology, Microbiology and Parasitology, University of Nairobi Objective: To determine the level of bacterial contamination in milk and chicken meat along market chain in Kenya, towards respective improved quality and safety. Special interest will be on presence of antimicrobial resistant bacteria. Qualifications:  Candidates must have Bachelor’s degree that encompasses Bacteriology (Bachelor veterinary degree (BVM); BSc in Public health, Laboratory technology or other equivalents fields) and registered by relevant professional bodies.  Respective Master’s degree (Msc in Applied Microbiology, Veterinary Medicine, Public health, Laboratory technology or other equivalents fields)  Those who have worked and/or published in livestock-based field/laboratory studies may have an advantage

One Position: PhD in Pharmacology and Toxicology - Department of Public health, Pharmacology and Toxicology, University of Nairobi Objective: To establish analytical LCMS/MS facility for determining the level of antimicrobial residues in milk and chicken meat along market chain in Kenya, towards respective improved quality and safety.

Qualifications:  Candidates must have a Bachelor’s degree that encompasses Pharmacology, (Bachelor of Veterinary Medicine (BVM), Medical, Pharmacy, Chemistry, Laboratory technology, or equivalent fields) and registered by relevant professional bodies.  Respective Master’s degree (MSc in Pharmacology, Chemistry, Natural sciences, Medicine, Pharmacy, Veterinary, or equivalent fields)  Those who have worked and/or published in livestock-based field/ laboratory studies and/or have experience with chromatographic techniques such as HPLC, may have an advantage

One Position: PhD in Medical Microbiology – Department of Medical Microbiology & Immunology, University of Nairobi Objective: To determine the current health status of farmers and other actors along milk and chicken market chain in Kenya; including presence of antimicrobial resistant bacteria. Qualifications:  Candidates must have Bachelor degree in Medical, Pharmacy, Public Health or equivalent fields and registered with relevant professional bodies.  Master’s degree (Msc in Medicine, Pharmacy, Public health or equivalent) Interested candidates who meet the criteria above are encouraged to send in their applications. To Apply: Submit: • Cover letter highlighting your suitability for the advertised position above • Current Curriculum Vitae with contact information of 3 references (their name, email address, and phone number) • Certified copies of your bachelor’s and master’s degree certificates, and transcripts. By 30th April 2024 to [email protected] , [email protected] and copy [email protected] Incomplete applications will not be considered. Please note that only shortlisted candidates will be contacted.

There will be no fee charges during the recruitment process.

• Arlington, Wisconsin
• COLLEGE OF AGRICULTURAL & LIFE SCIENCES/ANIMAL AND DAIRY SCIENCES
• Animal Care Services
• Staff-Full Time
• Opening at: Apr 2 2024 at 09:55 CDT
• Closing at: Apr 16 2024 at 23:55 CDT

Job Summary:

The Emmons Blaine Arlington Dairy Research Center is part of the Integrated Dairy Facilities for the Department of Animal and Dairy Science at UW-Madison. This industry leading facility was built in 2008, and is home to 500 milking cows, roughly 100 dry cows, and 100 neo-natal calves. This sand bedded freestall dairy facility, in the heart of the Arlington Ag Research Station just 20 miles north of Madison, allows for the completion of pen-based nutrition work, mammary and reproductive physiology research, welfare and behavioral sciences studies, calf growth research, transition cow management projects, and individual animal intake information. This position is involved in all aspects of the research dairy operations, including but not limited to, milking, feeding, cleaning, animal handling, and animal health observations at the facility. The daily responsibilities will include all activities involved with milking cows, taking and processing composite milk samples, and observing and handling the lactating cows in the research herd. The incumbent in this position must function with close, progressing to limited supervision over time once a clear understanding of the Standard Operating Procedures is developed. This position requires a high level of attention to detail in order to meet compliance needs of the research facility as well as that of a facility selling a food grade product. The person filling this role will need to be skilled in following written and oral instructions as directed by management and research protocols. The College of Agricultural and Life Sciences (CALS) is committed to maintaining and growing a culture that embraces diversity, inclusion, and equity, believing that these values are foundational elements of our excellence and fundamental components of a positive and enriching learning and working environment for all students, faculty, and staff. At CALS, we acknowledge that bias, prejudice, racism, and hate have historically occurred in many forms that cause significant and lasting harm to members of our community. We commit to taking actions each day toward a college that is inclusive and welcoming to all.

Responsibilities:

• 10% Collects samples from animals and administers medications, injections, and treatments according to established protocols and procedures to be used for diagnostic tests and research
• 20% Inventories, cleans, and maintains organization of animal holding spaces and supplies
• 10% Inspects and reports equipment problems to ensure optimal and safe operation
• 10% Maintains organization of and updates records related to research procedures
• 50% Observes animal behavior, performs basic animal care, and reports concerns to the appropriate entities to contribute to the health and well-being of animals and promote research

Institutional Statement on Diversity:

Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals. The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background - people who as students, faculty, and staff serve Wisconsin and the world. For more information on diversity and inclusion on campus, please visit: Diversity and Inclusion

Preferred H.S. Diploma

Qualifications:

Required Knowledge & Skills: -All necessary skills and knowledge will be taught during the first year on the job -A positive attitude and commitment to learning about the care of dairy cattle -Willingness to work hard and contribute positively to the work team -Skills in communicating with fellow co-workers, the management team, faculty, and visitors in a professional manner Preferred Knowledge & Skills: -Cattle handling and restraining techniques -Basic techniques and principles involved in milking dairy cows -Ability to accurately collect and record experimental samples and measurements -Familiarity and ability to recognize the difference between normal and abnormal appearances and behavior in dairy cattle -Ability to make minor repairs and adjustments to farm equipment utilized routinely -Skills in operating equipment related to farm work, or a willingness to learn them -Ability to interpret, understand, and follow both written and oral instructions pertaining to research and the management protocols of the facility

Work Schedule:

Scheduling for this position will be based on operational need to include 40 hours per week, with five 8 hour days OR four 10 hours days that include a rotating every other weekend schedule. Shifts are scheduled from 12:30 PM - 9:00 PM or 11:30 AM - 10:00 PM. Some holiday assignments will also be required.

Full Time: 100% It is anticipated this position requires work be performed in-person, onsite, at a designated work location in ARLINGTON, WI (53911).

Appointment Type, Duration:

Ongoing/Renewable

Minimum $18.50 HOURLY Depending on Qualifications Actual pay will depend on experience and qualifications. Employees in this position can expect to receive benefits such as vacation and paid time off; competitive insurances and savings accounts; retirement benefits. Benefit information at: https://www.wisconsin.edu/ohrwd/benefits/download/univstaff.pdf  

Additional Information:

Must be able to lift 50 pounds. Position requires frequent bending and twisting, along with repetitive motions of hands, wrists, and arms. Must be able to respond quickly to actions of 300-1400 pound animals to prevent being stepped on and/or pinned against a gate.

How to Apply:

Please click "Apply Now" to start the application process. You will be required to submit a resume for consideration. If you would like, you may also upload a cover letter. Your resume or cover letter should highlight your experience and skills as they relate to the listed qualifications for maximum consideration.

Jessica Cederquist [email protected] 608-265-8957 Relay Access (WTRS): 7-1-1. See RELAY_SERVICE for further information.

Official Title:

Animal Care Technician II(AN009)

Department(s):

A07-COL OF AG & LIFE SCIENCES/ANIMAL & DAIRY/DAIRY HERD

Employment Class:

University Staff-Ongoing

Job Number:

The university of wisconsin-madison is an equal opportunity and affirmative action employer..

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