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Week 1 Laboratory Introduction and Scientific Approaches Previous Next  Week 1 For Week 1, you have read and learned about the basic principles of scientific research, evaluating information sources, key features of a science laboratory, laboratory staff (personnel), and management/leadership skills. In this discussion, you will explore scenarios that bring these Week 1 concepts together in an attempt to gain a deeper appreciation for the inter-relatedness of these concepts.             STEP 1: Choose **one** of the scenarios described below. Title your Subject line with the scenario title.             STEP 2: Explain how specific concepts that you learned this week apply to the scenario.            • (a) Clearly explain how at least three distinct (3) concepts from the Week 1 readings and resources are relevant to the scenario. Be specific about the concepts. For example, specific concepts might be hypothesis testing, the peer review process, or time management. (Do not use these three in your answer.)          • (b) Clearly define and explain terms that you incorporate from the readings and resources. STEP 3: Summarize how the three concepts relate to each other in the chosen scenario.   IMPORTANT: There are many concepts addressed in the Week 1 readings and resources. There is no reason for you to choose to address the same three concepts as any one of your classmates. Duplicated content will not receive full credit for this Week 1 Discussion.   SCENARIO 1: Michael dropped a beaker of a hazardous liquid on the laboratory floor and toxic fumes immediately began emanating from the liquid. Nico, the laboratory manager yelled at Michael, using profanity and called him derogatory names.   SCENARIO 2: Tiana works at the Food and Drug Administration (FDA) and Larry works at Diagnostics Inc. They need to hire staff for both labs to conduct a set of planned experiments to ultimately develop a product that will prevent vector-borne illnesses due to insect bites.   SCENARIO 3: Kim made several field observations about the growth of bacterial cultures in New York Harbor. She developed a hypothesis about the factors that impact the growth of the bacteria, and she plans to test her hypothesis by conducting three separate experiments.   SCENARIO 4: Sherry argued that TopNewsScience.com provided her with the most insightful and current research news that anyone could possibly need. Pardeep doubted this and began to research the .com and to contrast it with what he knew about the quality of research published in peer-reviewed journals.   SCENARIO 5: Harper, a laboratory manager at Symbronic Technologies Inc,  checks on the progress for lab technician Laurie weekly and on the progress for lab technician David bi-weekly. Both Laurie and David had been collecting quality data and meeting their deadlines for more than two years. Week 1 Laboratory Introduction and Scientific Approaches   Previous  Next  Week 1 For Week 1, you have read and learned about the basic principles of scientific research, evaluating information sources, key features of a science laboratory, laboratory staff (personnel), and management/leadership skills. In this discussion, you will explore scenarios that bring these Week 1 concepts together in an attempt to gain a deeper appreciation for the inter-relatedness of these concepts.           STEP 1: Choose **one** of the scenarios described below. Title your Subject line with the scenario title.           STEP 2: Explain how specific concepts that you learned this week apply to the scenario.          • (a) Clearly explain how at least three distinct (3) concepts from the Week 1 readings and resources are relevant to the scenario. Be specific about the concepts. For example, specific concepts might be hypothesis testing, the peer review process, or time management. (Do not use these three in your answer.)        • (b) Clearly define and explain terms that you incorporate from the readings and resources. STEP 3: Summarize how the three concepts relate to each other in the chosen scenario. IMPORTANT: There are many concepts addressed in the Week 1 readings and resources. There is no reason for you to choose to address the same three concepts as any one of your classmates. Duplicated content will not receive full credit for this Week 1 Discussion. SCENARIO 1: Michael dropped a beaker of a hazardous liquid on the laboratory floor and toxic fumes immediately began emanating from the liquid. Nico, the laboratory manager yelled at Michael, using profanity and called him derogatory names. SCENARIO 2: Tiana works at the Food and Drug Administration (FDA) and Larry works at Diagnostics Inc. They need to hire staff for both labs to conduct a set of planned experiments to ultimately develop a product that will prevent vector-borne illnesses due to insect bites. SCENARIO 3: Kim made several field observations about the growth of bacterial cultures in New York Harbor. She developed a hypothesis about the factors that impact the growth of the bacteria, and she plans to test her hypothesis by conducting three separate experiments. SCENARIO 4: Sherry argued that TopNewsScience.com provided her with the most insightful and current research news that anyone could possibly need. Pardeep doubted this and began to research the .com and to contrast it with what he knew about the quality of research published in peer-reviewed journals. SCENARIO 5: Harper, a laboratory manager at Symbronic Technologies Inc,  checks on the progress for lab technician Laurie weekly and on the progress for lab technician David bi-weekly. Both Laurie and David had been collecting quality data and meeting their deadlines for more than two years. Module 1: Introduction to Laboratory Management The roles and responsibilities of the lab manager are diverse and vary for each place of employment. As lab manager, you may be responsible for ordering supplies, maintaining a budget, tracking materials or samples, supervising staff, and presenting research results. In the three course modules, we will review many of these aspects of laboratory management. Below, we will discuss the roles of the various personnel in the lab setting. Keep in mind that, while different labs may have different titles for their employees, the functional goals of the lab members remain the same. In keeping the lab running efficiently, personnel should follow guidelines for safety, standards for recordkeeping, and operating protocols for research. The lab manager or lead scientist may be responsible for all of these aspects of lab management in addition to maintaining inventory records and a budget. Outcomes and Objectives Course Learning Outcomes Addressed in this Module · execute sound management practices in a laboratory setting for the creation and maintenance of records, timelines, and deliverables Module 1 Learning Objectives After completing this module, you should be able to · define the roles and responsibilities of personnel in a laboratory setting · identify management styles and understand their function in the laboratory · develop project and time management systems for the workplace Commentary Topics Section 1. Introduction to the Lab Environment Section 2. Personnel Management and Leadership Skills Section 3. Inventory Tracking and Management Section 4. Procurement Section 5. Summary Section 1. Introduction to the Lab Environment Laboratory managers are often found working in government, industrial/commercial, or academic lab settings. Even though these types of workplaces function according to different guidelines, the equipment, job responsibilities, and daily operations remain the same. As lab manager, you will assume a leadership role and have responsibilities beyond those of a lab technician. The research conducted at your workplace will be categorized as either basic  or  applied science (Barker, 2005). In a lab context, basic science is research performed for the sake of knowledge. This type of research is often conducted in an academic lab funded by grant money or faculty salary. Applied science is research conducted to develop a product, and has historically been associated with industrial labs. Another type of science practiced in labs is clinical research, in which scientists investigate a disease or disorder of humans. The lab in which you will work will be part of a larger system or organization (Brett, 2007; Barker, 2005). As an employee of the organization, you will share common goals with your peers. The success of the organization will be rooted in the performance and capabilities of the individuals in the system. The organization in which you will work may have an organizational chart illustrating the staff hierarchy and outlining individual roles (see figure 1.1), which will depend on the structure of the organization. Functionally, the dynamics of a laboratory environment are such that staff usually work independently to address specific questions related to the labs main focus. Staff members may all be held responsible for their actions, and should all be treated with respect. In a research team, the project leader or manager supervises technicians and students; however, subordinates should be treated as skilled staff essential to the team. Figure 1.1 Sample Organizational Structure of a Research Facility Roles and Responsibilities The personnel and responsibilities listed below are not inclusive, but should give you a sense of the staff and their tasks in a standard lab setting (Barker, 2005; HHMI, 2006). Depending on your work environment, your responsibilities and accountability may fall into one or more of the categories below. · The principal investigator (PI), lead scientist, or lab director is responsible for guiding most of the research in the lab, supervising the labs overall accomplishments, ensuring the completion of administrative tasks for the organization, and securing funding. The PIs administrative duties can consume most of his or her day, leaving little time for benchwork. · The lab manager or scientist is responsible for maintaining daily operations in the lab as well as for managing inventory and lab safety, conducting experiments, and supervising assistants or student assistants. The lab manager is often a highly skilled researcher knowledgeable about all work conducted in the lab. He or she is typically responsible for providing orientation or training to new members of the lab. · The postdoctoral associate is responsible for independent research and sometimes for supervising assistants. This position is usually filled by a recent doctorate recipient completing additional training for two to five years in a university or industrial lab prior to becoming a PI. · The research assistant or lab technician is responsible for a variety of tasks, which may include ordering supplies, managing equipment maintenance, preparing media, and assisting others in the lab with experiments. · Visiting researchers or faculty may be present in the lab on occasion to learn new techniques or to participate in collaborative research with the PI. · Graduate students or rotational students or residents or research fellows are responsible for completing research and academic credits toward an advanced degree (masters, doctoral, or medical). They may be engaged in independent research related to the PIs research focus. Graduate students work on their own projects for two or more years, becoming increasingly independent. Rotational students or residents are typically visiting scientists who stay in the lab for a short time—several weeks to several months—conducting short projects. · The student research assistant or intern is responsible for conducting research under the supervision of more experienced researchers in the lab. Students may be completing research for their senior project or independent study, or in order to obtain additional experience prior to graduate school or employment. They may be undergraduate students or high school students working during the summer or academic year. Use Your Knowledge 1.1 Read the following scenario. Use it in the activity below to match members of a lab with their titles. Dr. V. Wang, from Beijing University, has joined Dr. P. Smiths lab in order to learn how to use mass spectrometry to identify proteins found in traditional medicinal plants. Dr. Wang is well-known for her studies in dosage and toxicity levels of traditional plants. Upon her arrival at Dr. Smiths lab, Dr. Wang is given a tour by Ella Ward. Ella has worked with Dr. Smith for five years and is responsible for providing staff members with the tools and resources they need to conduct research. Ella provides Dr. Wang with a desk and bench space for her research and shows her the chemical storage cabinets in the lab. Dr. Wang will work with Ella and Dr. Reginald Gomez to learn how to prepare samples for mass spectrometry and to use the equipment in the lab. Dr. Gomez recently completed his PhD and is collaborating with Dr. Smith on a project blending his graduate research with Dr. Smiths primary research. Dr. Gomez introduces Dr. Wang to Phillip and Aisha, who also work in the lab. Phillip is completing his masters degree and works closely with Ella on a project for Dr. Smith. Aisha works in the lab for a few hours three days a week in between classes at the university. She washes dishes and prepares chemical stock solutions as directed by Ella. Dr. Wang is not familiar with the job titles and responsibilities at this university lab, as the staffing arrangements at the lab differ from those in her facility in Beijing. In the following activity, identify the role of each member of Dr. Smiths lab. What are the roles of each member of Dr. Smiths lab? Section 2. Personnel Management and Leadership Skills Leadership is not domination, but the art of persuading people to work toward a common goal. —Daniel Goleman Assuming a leadership role in the laboratory means taking responsibility for personnel management—hiring staff, conducting performance evaluations, and making promotion or termination decisions—in addition to sample/inventory tracking, general lab maintenance, and research performance. These responsibilities can leave a lab manager feeling stressed (Lab Manager Magazine, 2011). Many lab managers would agree that they hardly have the time each day to complete all their tasks while ensuring consistently high-quality results. Effective time management can help to alleviate stress for you as well as for the other members of the lab. Establish an effective time management plan early on by setting a schedule, assigning priorities, and building teamwork. As your responsibilities expand, you will need to adjust your plan accordingly. The S.M.A.R.T. goals system for time management is one tool you can use to keep the lab and your job running smoothly. To keep the lab functioning as well as possible, encourage your personnel to develop time management plans of their own. This is especially useful when equipment or resources are shared throughout the lab for time-sensitive experiments. S.M.A.R.T. involves the following steps: · Specific—Adopt a specific and detailed schedule. · Measurable—Define your goals in order to assess your progress and effectiveness. Benchmarks or baselines may be included in your job description or may be set after discussions with your supervisor. · Achievable—Make your goals reasonable and achievable. If you are aggressive with your goals and set them too high, this can be counterproductive. Set achievable goals for the time allotted, taking into consideration the variety of tasks for which you are responsible. · Result-oriented—Focus on the targeted goal that you want to achieve. · Time-limited—When setting goals, identify specific deadlines and/or tasks you wish to complete by a deadline. As you are responsible for your staff, it is advisable that you meet with lab members regularly to support effective time management and lab etiquette for all members. For example, if a lab member reserves a piece of shared equipment for a four-hour time block and is late in starting the experiment, this can push back the time at which the next user can start his or her work on the equipment. If such tardy behavior is consistent, tensions can build in the lab and create conflict that will need to be addressed. In addition to ensuring good time management, the lab manager influences the attitude and work etiquette of the lab through his or her leadership style. Your leadership should go beyond managing the complicated system of employees and equipment in the lab (Kearns and Sun, 2007). You can take cues from the PI. You can guide employees by delegating authority and leading by example. Or you can order employees to perform tasks based on your authority and micromanage certain projects. Your approach to leading the lab will be, in part subconsciously, influenced by your personality traits, your behavior patterns, the interactions and dynamics of those in the lab, and the responses of the members of the lab to you. Use Your Knowledge 1.2 Read the following scenario. Use it in the activity below to analyze time management issues arising in a particular lab. Martha dreads coming to work on Mondays. Every Monday, the lab has a meeting to discuss problems encountered the previous week and the timeline for the work to be completed during the current week. Marthas coworker Jorge commits to using the high-performance liquid chromatography (HPLC) equipment each week at the time at which his samples are ready, usually by Wednesday. However, in the last few months, Jorge has not used the HPLC as planned. Jorge is completing his postdoctoral research in the lab, and has been distracted by the process of applying for a permanent position elsewhere. Each week, Martha and Weimin, a graduate student, wait for Jorge to finish with the HPLC equipment so that they can use it to meet their goal of analyzing samples received from Weimins collaborator in Europe. Each week in the past few months, Martha has had to communicate to their anxious collaborator that the work is not completed. Weimin blames Martha for the lack of results, and is unable to finish his manuscripts. Mark, the lab manager, does not stress to the research team the importance of following the schedule. Lately, he has been focusing his attention elsewhere—on two new projects the PI has given him. This Monday is especially bad for Martha. Her patience is exhausted, and she and Jorge argue during the lab meeting. Jorge states that his work is more important than Marthas and Weimins, and that the number of samples he needs to process with the HPLC is higher. Martha asserts that Jorge does not have his samples prepared for the HPLC on time, whereas those from the collaborator are always ready. Weimin feels uncomfortable with the argument and abruptly leaves the room. During this interaction, Mark continues to read protocols provided by the PI for an experiment to begin next month. He only comments once that Martha and Jorge need speak more quietly. Reflection Activity Use Your Knowledge 1.2- Please go to My Tools -> Self Assessments -> to complete this self assessment. Finding Your Own Leadership Style A leader is someone you choose to follow to a place you wouldnt go by yourself. —Joel Barker To be an effective leader, it will benefit you to do some self-evaluation to assess your personality and leadership style. The Keirsey Temperament Sorter is a self-scoring questionnaire designed to identify personality and temperament types (Kearns and Sun, 2007). Temperament is the combination of ones thought processing and ones actions, and includes factors such as communication style, values, skills, and personal perceptions of worth in the workplace and socially. There are four temperament types: idealist, rational, artisan, and guardian. Idealists aim to achieve balance and a stable environment through the use of teamwork and creativity, working to reach their goals without compromising their ideals. Rational personalities are pragmatists; they achieve balance through empowerment and through handling challenges efficiently, possibly working outside of set guidelines in order to address problems. Artisans concern themselves with current goals and strive for balance among empowered team members by encouraging creative solutions outside of set guidelines. Guardians are duty-oriented and strive to achieve a stable work environment by adhering to guidelines and using and fostering respect among team members (Keirsey.com). Figure 1.2 gives further information on the Keirsey temperament types: Figure 1.2 Keirsey Temperament Types Source: Information taken from https://www.mtso.edu/site/assets/files/1136/keirsey-temperament-character-intelligence.pdf Your leadership style will be reflected in the goals you establish for the lab and in your treatment of the staff. Individuals respond differently to different management styles; as the leader of the lab, you should be aware of the responses of your lab members and should work to overcome potential conflicts. In the Use Your Knowledge 1.2 scenario above, it is evident that the lab members temperaments contribute to their responses to the situation. Marthas temperament aligns most closely with the guardian—she has established a time management plan as directed and works to adhere to the lab rules for equipment use and project completion. Jorge, on the other hand, implies through his actions that he does not need to follow the rules. His temperament aligns more with the artisan, as he works toward his goal of HPLC analysis of numerous samples at his own pace. Weimin can be considered an idealist; he has worked with Martha to establish a timeframe for completing the sample analyses, but will not compromise his value of respecting authoritative figures, and therefore leaves during the lab meeting. Mark may fit with the rational temperament. He asks his staff to resolve conflicts on their own while he turns his attention to the new challenges faced by the lab. Mark does not enforce schedule guidelines with his lab members or require them to establish time management plans. Mark works independently on goals established by the PI and does little to manage the personnel in the lab. Many models of management and leadership behavior have been developed over the years. One model for understanding managerial behavior is the managerial grid developed by Robert R. Blake and Jane Mouton (see figure 1.3). According to this model, there are two fundamental drivers of managerial behavior: concern for productivity and concern for people. The grid shows four profiles of managers based on whether they rank high or low on concern for productivity and for people. If a manager is exclusively concerned with getting the job done at the expense of the needs of the workers, employee performance suffers. In Use Your Knowledge 1.2, Marks managerial style fits this description. Conversely, if one is overly concerned with building relationships and preventing conflict among employees, as in the accommodating profile, output suffers. Balancing the two factors is considered the best management approach. In the scenario above, Mark allows his time to be pulled away from his staff members as he strives to address the needs of his supervisor, the PI. With his energies directed elsewhere, Marks concern for the lab members and their individual needs falters. Based on the grid below, Marks style can be classified as indifferent. He is not overly occupied with his lab staff, and diverts his focus to new projects before completing current assignments. If Mark acknowledged the disagreement between Martha and Jorge and addressed their concerns with the goal of producing results in a timely manner, his management style would shift toward team-oriented. If he demanded the completion of all samples without addressing Jorges failure to follow a schedule, the goal would be achieved at the cost of employee morale, and, with his dictatorial style, Mark would possibly lose respect among his staff. Take this inventory tool to discover your style as a manager. The “Leadership Quiz” results number will help you understand your management or leadership style, so that you can maximize your team’s productivity. https://www.mindtools.com/pages/article/newLDR_73.htm Figure 1.3 Blake and Mouton Managerial Grid Source: Adapted from Brett, 2007 Use Your Knowledge 1.3 Perform the activity and reflect on the following questions. 1. Complete the Keirsey Temperament Sorter II (found at https://www.strategicaction.com.au/keirsey-temperament-sorter-questionnaire). Based on the survey results, what is your temperament type? Did any aspect of the results surprise you? 2. Describe your leadership style. How does it fit into the grid in figure 1.3 above? Section 3. Inventory Tracking and Management In addition to supervising employees, the lab manager is usually responsible for maintaining daily operations in the lab. These may include ordering supplies, maintaining and tracking inventory and expenditures, ensuring equipment maintenance, tracking samples, and monitoring environmental health and safety concerns. The tracking of specific tasks to be completed daily to ensure the achievement of personal or lab measurable goals within a designated timeframe can be accomplished through adherence to a time management plan, via S.M.A.R.T. or another method. When achievable goals are reached and results produced through laboratory operations, the time management plan should be updated and new timelines established. Laboratory information management system (LIMS) software is often used to track supplies for ordering, to provide the location of materials in the lab, to store safety information, to track progress of samples, and possibly to manage expenditures. The tracking system you use can be as simple as a centrally placed notebook containing lists and invoices, or it can be a database developed in standard office software (Microsoft Excel or Access) or a specialized LIMS package. The two figures below show examples of tracking systems, figure 1.4 for lab items, and figure 1.5 for lab samples. Directions: Scroll your mouse over the cells in the header of the spreadsheet for a description of each component tracked. Figure 1.4 Lab Item Tracking System Item Inventory Number Supplier Location Last Purchased Expiration Date SDS Chemical 1 10103 Sigma-Aldrich cabinet 1 09/05/2009 12/21/2010 file cabinet Reagent 2 2244-7 Promega -80C, shelf 2 02/02/2008 05/01/2008 online H3-isotope 546-00-12 Fisher Sci. -20C, box 07/01/2009 08/10/2009 online Directions: Scroll your mouse over the cells in the header of the spreadsheet for a description of each component tracked. Figure 1.5 Lab Sample Tracking System Sample Barcode Sample Name Prepared By Date Location Stage Last Accessed Project Name Notes 01012345 0809-JAD-Np01 John Doe 08/30/2000 -80: box 12 GS-MC completed 12/11/2009 MS thesis material collected at site 24, Calvert Co. 01012346 0809-JAD-Np02 John Doe 08/30/2001 -80: box 12 GS-MC completed 12/11/2009 MS thesis material collected at site 24, Calvert Co. 01012347 0809-JAD-Np03 John Doe 08/30/2002 -80: box 12 GS-MC completed 12/11/2009 MS thesis material collected at site 24, Calvert Co. 01012348 0809-KTS-EC24 Karen Smith 08/31/2009 -80: box 15 protein extracted 09/05/2009 DOE grant --none-- Several LIMS software programs are available, either open-source or for commercial purchase (see figures 1.6 and 1.7). Consider the LIMS options to select the best one for your workplace. The tracking system you choose should provide the user with a logical and accessible format for data storage and retrieval (Soto, 2009). It should be user-friendly, with minimal training required; be compatible with the other databases and spreadsheets you use; adequately protect stored information; and be reasonably priced (or free). Lastly, the system should be able to adapt to changing research and technology. There is no one best LIMS for all lab situations (Segalstad, 2009). A thorough evaluation of the labs needs and organizational requirements should be considered in setting up a tracking system. Figure 1.6 LIMS Freely Available Online, Example I Source: Labmatica LIMS Figure 1.7 LIMS Freely Available Online, Example II Source: Open-LIMS Recordkeeping For your supervisor or funding agency, you may need to obtain records for samples within a short time period. You will need to be able to search through a database or sort samples quickly (HHMI, 2006). To facilitate speedy retrieval, the PI or other members of the lab should establish a format for naming samples and should consistently use this protocol. Consistency is key to a well-maintained information system that includes information on samples and files as well as other laboratory records. In an academic setting with low security, non-hazardous research may not require strict adherence to a tracking system; however, consistency eases the preparation of research findings for presentations and publications as well as the transition of new graduate and undergraduate assistants. Private industry and government research facilities typically have specific standard operating procedures (SOPs) to follow for document and sample processing. Security of research information is also handled differently in different research environments. It is important for you to familiarize yourself with the protocols for recording and maintaining proprietary information in your workplace. In the box below, see some examples of naming standards: Sample Naming Standards Electronic File 090801_GelPhoto_RJones_xy22set.jpg Format: Date(YYMMDD)_FileType_User_SampleGroup.FileType   Specimen 090801_xy22Bats_hydroxylase001 Format: Date(YYMMDD)_ProjectID_UniqueSampleID LIMS are also used to store general laboratory information such as primary data and information on protocols, samples, reagents, and equipment (HHMI, 2006). This information may be stored according to security protocols, depending on its sensitivity. General laboratory protocols are often kept in a common file. Protocols need to be updated as they are modified by users and as technology changes. Reagent use warrants the same level of detail as does sample tracking. The laboratory setting is not static; staff and students may change every semester, and information needs to be clearly accessible and understandable. Before a lab member leaves, his or her records and samples should be identified and clearly labeled. Equipment tracking may be another responsibility of the lab manager (HHMI, 2006). While all members of the lab should ensure equipment maintenance, one person should be responsible for keeping records of purchase, repair, and calibration. Equipment history can be included in the collective LIMS for the group or recorded in a separate file by the individual responsible. Budget Management Many research centers (academic, industry, and government) have a chief financial officer (CFO) responsible for tracking funds. However, responsibility falls to each lab group to keep expenditures within the scope of the research budget. Several aspects of the laboratory budget are evaluated in the financial decision-making process (HHMI, 2006). First, direct and indirect costs for the lab are tracked (HHMI, 2006). Direct costs are those pertaining to the research conducted. These include costs for salaries and employee benefits, equipment, and consumable supplies. Indirect costs, often referred to as overhead, are those associated with funding the organization (university or business). Indirect costs include costs for administration, utilities, infrastructure, and facility maintenance. Secondly, the source of the funding is considered. The source exerts an influence on the scope and amount of research conducted. PIs and affiliated staff often apply for grants, or soft money, to fund projects and pay salaries. Project selection in government and private industry may be driven by shareholder, stakeholder, or public demands. The PI or administrative staff may assume responsibility for managing the labs budget, or this too may fall within the purview of the lab manager. Grants or government or organizational sources of funding may not take into account the day-to-day costs of lab operations (Brown, 1999). To make funds last over the course of a year, it is advisable to first budget basic necessities on a 12-month calendar. This will make it easier to determine whether the lab is overspending or underspending any given month. If you miscalculate and consistently overspend, then, at the end of the year, you will have insufficient funds for salaries, supplies, and so forth. If you consistently underspend, then you will be left with a surplus at the end of the year. This may lead to a rush of spending, committed in the fear that funds will not carry over to the next year. Section 4. Procurement In any laboratory having the correct supplies in the correct amount when needed keeps work uninterrupted and saves money. In order to do this, the laboratory manager and PI need to project in advance the supplies required and decide how to pay for them. The laboratory technicians, graduate students, etc. are also vital in noticing when items need re-ordering. The manner in which an item can be purchased depends on the item itself and its price. There are two systems that can be used: the internal purchase request system and the external credit card system, also called the purchase card or pcard, for short. See Procurement flowchart (Figure 1.8). The roles involved in the purchasing process include the laboratory manager, laboratory staff, principal investigator, Office of Sponsored Research (grant and contracts office), and maintenance and facilities (Purchasing office/Procurement). For more information about stakeholders involved in laboratory purchasing and other operations, see Module 3, Figure 3.4, Communication Flow Outside the Laboratory. INTERNAL PURCHASE REQUEST PROCESS: Once the need for an item is noticed, a laboratory staff member sends an email request to the laboratory manager, research center administrator (RCA), or equivalent. The laboratory staff member making the request also includes in the email the account number/cost center associated with a particular grant or project which the lab manager/RCA will use for billing. The lab manger/RCA log in to a secure purchasing system and inputs the information and the … E-Mail Us | 240-684-2020 (Hours) Tell us how were doing Featured Application for Admission Academic Calendar Catalogs: Undergraduate | Graduate | Prior Years Consumer Disclosures & Policies Help Center Library Schedule of Classes Title IX/Sexual Misconduct Quick Links Log In Events News Jobs U.S. Locations UMGC Asia UMGC Europe UMGC Gear Contact UMGC UMGC For Prospective Students Military & Veterans Current Students Administration Partners Alumni Donors Media Contact Us Contact UMGC 855-655-8682 Help Center Academic Center at Largo 1616 McCormick Drive, Largo, MD 20774 Directions to Academic Center at Largo Mailing Address No classes or services at this location 3501 University Blvd. East, Adelphi, MD 20783 Get Social UMGC is a proud member of the University System of Maryland. © 2021 University of Maryland Global Campus. All rights reserved. Accessibility Statement Terms & Conditions Privacy Policy Social Media Policy PB © 2012 The University of California Museum of Paleontology, Berkeley, and the Regents of the University of California • www.understandingscience.org How science works The Scientific Method is traditionally presented in the first chapter of science text- books as a simple recipe for performing scientific investigations. Though many use- ful points are embodied in this method, it can easily be misinterpreted as linear and “cookbook”: pull a problem off the shelf, throw in an observation, mix in a few ques- tions, sprinkle on a hypothesis, put the whole mixture into a 350° experiment—and voila, 50 minutes later you’ll be pulling a conclusion out of the oven! That might work if science were like Hamburger Helper®, but science is complex and cannot be re- duced to a single, prepackaged recipe. The linear, stepwise representation of the process of science is simplified, but it does get at least one thing right. It captures the core logic of science: testing ideas with evidence. However, this version of the scientific method is so simplified and rigid that it fails to accurately portray how real science works. It more accurately describes how science is summarized after the fact—in textbooks and journal articles—than how sci- ence is actually done. The simplified, linear scientific method implies that scientific studies follow an unvarying, linear recipe. But in reality, in their work, scientists engage in many different activities in many different sequences. Scientific investigations often involve repeating the same steps many times to account for new information and ideas. The simplified, linear scientific method implies that science is done by individual scientists working through these steps in isolation. But in reality, science depends on interactions within the scientific community. Dif- ferent parts of the process of science may be carried out by different people at differ- ent times. The simplified, linear scientific method implies that science has little room for creativity. But in reality, the process of science is exciting, dynamic, and unpredictable. Science relies on creative people thinking outside the box! The simplified, linear scientific method implies that science concludes. But in reality, scientific conclusions are always revisable if warranted by the evi- dence. Scientific investigations are often ongoing, raising new questions even as old ones are answered. Page 1 dmclaughlin Highlight dmclaughlin Highlight dmclaughlin Highlight dmclaughlin Highlight dmclaughlin Sticky Note Rejected set by dmclaughlin © 2012 The University of California Museum of Paleontology, Berkeley, and the Regents of the University of California • www.understandingscience.org A blueprint for scientific investigations The process of science involves many layers of complexity, but the key points of that process are straightforward: There are many routes into the process—from serendipity (e.g., being hit on the head by the proverbial apple), to concern over a practical problem (e.g., finding a new treatment for diabetes), to a technological development (e.g., the launch of a more advanced telescope)—and scientists often begin an investigation by plain old poking around: tinkering, brainstorming, trying to make some new observations, chatting with colleagues about an idea, or doing some reading. Scientific testing is at the heart of the process. In science, all ideas are tested with evidence from the natural world, which may take many different forms—from Antarctic ice cores, to particle accelerator experiments, to detailed descriptions of sed- imentary rock layers. You can’t move through the process of science without examin- ing how that evidence reflects on your ideas about how the world works—even if that means giving up a favorite hypothesis. Page 2 dmclaughlin Highlight 4 © 2012 The University of California Museum of Paleontology, Berkeley, and the Regents of the University of California • www.understandingscience.org The scientific community helps ensure science’s accuracy. Members of the sci- entific community (i.e., researchers, technicians, educators, and students, to name a few) play many roles in the process of science, but are especially important in gen- erating ideas, scrutinizing ideas, and weighing the evidence for and against them. Through the action of this community, science is self-correcting. For example, in the 1990s, John Christy and Roy Spencer reported that temperature measurements taken by satellite, instead of from the Earth’s surface, seemed to indicate that the Earth was cooling, not warming. However, other researchers soon pointed out that those mea- surements didn’t correct for the fact that satellites slowly lose altitude as they orbit and that once these corrections are made, the satellite measurements were much more consistent with the warming trend observed at the surface. Christy and Spencer immediately acknowledged the need for that correction. The process of science is intertwined with society. The process of science both influences society (e.g., investigations of X-rays leading to the development of CT scanners) and is influenced by society (e.g., a society’s concern about the spread of HIV leading to studies of the molecular interactions within the immune system). Page 3 dmclaughlin Highlight dmclaughlin Highlight © 2012 The University of California Museum of Paleontology, Berkeley, and the Regents of the University of California • www.understandingscience.org Exploration and discovery The early stages of a scientific investigation often rely on making observations, ask- ing questions, and initial experimentation—essentially poking around—but the routes to and from these stages are diverse. Intriguing observations sometimes arise in surprising ways, as in the discovery of radioactivity, which was inspired by the obser- vation that photographic plates (an early version of camera film) stored next to ura- nium salts were unexpectedly exposed. Sometimes interesting observations (and the investigations that follow) are suddenly made possible by the development of a new technology. For example, the launch of the Hubble Space Telescope in 1990 allowed astronomers to make deeper and more focused observations of our universe than were ever before possible. These observations ultimately led to breakthroughs in ar- eas as diverse as star and planet formation, the nature of black holes, and the expan- sion of the universe. Observations like this image from the Hubble Telescope can lead to further breakthroughs. Sometimes, observations are clarified and questions arise through discussions with colleagues and reading the work of other scientists—as demonstrated by the discovery of the role of chlorofluorocarbons (CFCs) in ozone depletion … Hubble image provided by NASA, ESA, and A. Nota (STScI/ESA) Page 4 6 © 2012 The University of California Museum of Paleontology, Berkeley, and the Regents of the University of California • www.understandingscience.org EXPLORING AEROSOLS In 1973, chemists had observed that CFCs were being re- leased into the environment from aerosol cans, air condi- tioners, and other sources, but it was discussions with his colleague and advisor, Sherwood Rowland, that led Mario Molina to ask what their ultimate fate was. Since CFCs were rapidly accumulating in the atmosphere, the question was intriguing, but before he could tackle the issue (which would ultimately lead to a Nobel Prize and an explanation for the hole in the ozone layer), Molina needed more infor- mation. He had to learn more about other scientists’ stud- ies of atmospheric chemistry, and what he learned pointed to the disturbing fate of CFCs. Furthermore, though observation and questioning are essential to the process of sci- ence, on their own, they are not enough to launch a scientific investigation; generally, scientists also need scientific background knowledge—all the information and under- standings they’ve picked up from their scientific training in school, supplemented by discussions with colleagues and reviews of the scientific literature. As in Mario Molina’s story, an understanding of what other scientists have already figured out about a par- ticular topic is critical to the process. This background knowledge allows scientists to recognize revealing observations for what they are, to make connections between ideas and observations, and to figure out which questions can be fruitfully tackled with avail- able tools. The importance of content knowledge to the process of science helps explain why science is often mischaracterized as a static set of facts contained in textbooks— science is a process, but one that relies on accumulated knowledge to move forward. THE SCIENTIFIC STATE OF MIND Some scientific discoveries are chalked up to the ser- endipity of being in the right place at the right time to make a key observation—but rarely does seren- dipity alone lead to a new discovery. The people who turn lucky breaks into breakthroughs are generally those with the background knowledge and scientific ways of thinking needed to make sense of the lucky observation. For example, in 1896, Henri Becquerel made a surprising observation. He found that pho- tographic plates stored next to uranium salts were spotted, as though they’d been exposed to light rays—even though they had been kept in a dark drawer. Someone else, with a less scientific state of mind and less background knowledge about physics, might have cursed their bad luck and thrown out the ruined plates. But Becquerel was intrigued by the ob- servation. He recognized it as something scientifically interesting, went on to perform follow-up experiments that traced the source of the exposure to the urani- um, and in the process, discovered radioactivity. The key to this story of discovery lies partly in Becquerel’s instigating observation, but also in his way of thinking. Along with the relevant background knowledge, Becquerel had a scientific state of mind. Sure, he made some key observations — but then he dug into them further, inquiring why the plates were exposed and trying to eliminate different potential causes of the ex- posure to get to the physical explanation behind the happy accident. Mario Molina The ruined photo plate that got Becquerel thinking Henri Becquerel Mario Molina photo by Donna Coveney/MIT Page 5 © 2012 The University of California Museum of Paleontology, Berkeley, and the Regents of the University of California • www.understandingscience.org Observation beyond our eyes We typically think of observations as having been seen “with our own eyes,” but in science, observations can take many forms. Of course, we can make observations di- rectly by seeing, feeling, hearing, and smelling, but we can also extend and refine our basic senses with tools: thermometers, microscopes, telescopes, radar, radiation sen- sors, X-ray crystallography, mass spectroscopy, etc. And these tools do a better job of observing than we can! Further, humans cannot directly sense many of the phenom- ena that science investigates (no amount of staring at this computer screen will ever let you see the atoms that make it up or the UV radiation that it emits), and in such cases, we must rely on indirect observations facilitated by tools. Through these tools, we can make many more observations much more precisely than those our basic senses are equipped to handle. Tools like the Hubble Space Telescope, microscopes and submersibles help us to observe the natural world. Observations yield what scientists call data. Whether the observation is an experimen- tal result, radiation measurements taken from an orbiting satellite, an infrared record- ing of a volcanic eruption, or just noticing that a certain bird species always thumps the ground with its foot while foraging — they’re all data. Scientists analyze and inter- pret data in order to figure out how those data inform their hypotheses and theories. Do they support one idea over others, help refute an idea, or suggest an entirely new explanation? Though data may seem complex and be represented by detailed graphs or complex statistical analyses, it’s important to remember that, at the most basic level, they are simply observations. Observations inspire, lend support to, and help refute scientific hypotheses and theo- ries. However, theories and hypotheses (the fundamental structures of scientific knowledge) cannot be directly read off of nature. A falling ball (no matter how detailed our observations of it may be) does not directly tell us how gravity works, and collect- ing observations of all the different finch species of the Galapagos Islands does not di- rectly tell us how their beaks evolved. Scientific knowledge is built as people come up with hypotheses and theories, repeatedly test them against observations of the natu- ral world, and continue to refine those explanations based on new ideas and observa- tions. Observation is essential to the process of science, but it is only half the picture. Hubble image provided by NASA; microscope photo from Scott Bauer/USDA; submersible photo from NOAA Ocean Explorer Page 6 8 © 2012 The University of California Museum of Paleontology, Berkeley, and the Regents of the University of California • www.understandingscience.org Testing scientific ideas Testing hypotheses and theories is at the core of the process of science. Any aspect of the natural world could be explained in many different ways. It is the job of science to collect all those plausible explanations and to use scientific testing to filter through them, retaining ideas that are supported by the evidence and discarding the others. You can think of scientific testing as occurring in two logical steps: (1) if the idea is correct, what would we expect to see, and (2) does that expectation match what we actually observe? Ideas are supported when actual observations (i.e., results) match expected observations and are contradicted when they do not match. Page 7 © 2012 The University of California Museum of Paleontology, Berkeley, and the Regents of the University of California • www.understandingscience.org The logic of scientific arguments Taken together, the expectations generated by a scientific idea and the actual observa- tions relevant to those expectations form what we’ll call a scientific argument. This is a bit like an argument in a court case—a logical description of what we think and why we think it. A scientific argument uses evidence to make a case for whether a scientif- ic idea is accurate or inaccurate. For example, the idea that illness in new mothers can be caused by doctors’ dirty hands generates the expectation that illness rates should go down when doctors are required to wash their hands before attending births. When this test was actually performed in the 1800s, the results matched the expectations, forming a strong scientific argument in support of the idea—and hand-washing! Though the elements of a scientific argu- ment (scientific idea, expectations gener- ated by the idea, and relevant observations) are always related in the same logical way, in terms of the process of science, those ele- ments may be assembled in different orders. Sometimes the idea comes first and then scientists go looking for the observations that bear on it. Sometimes the observations are made first, and they suggest a particular idea. Sometimes the idea and the observa- tions are already out there, and someone comes along later and figures out that the two might be related to one another. Testing ideas with evidence may seem like plain old common sense—and at its core, it is!—but there are some subtleties to the process: • Ideas can be tested in many ways. Some tests are relatively straightforward (e.g., raising 1000 fruit flies and counting how many have red eyes), but some re- quire a lot of time (e.g., waiting for the next appearance of Halley’s Comet), effort (e.g., painstakingly sorting through thousands of microfossils), and/or the devel- opment of specialized tools (like a particle accelerator). • Evidence can reflect on ideas in many different ways. • There are multiple lines of evidence and many criteria to consider in eval- uating an idea. • All testing involves making some assumptions. Despite these details, it’s important to remember that, in the end, hypotheses and theories live and die by whether or not they work—in other words, whether they are useful in explaining data, generating expectations, providing satisfying explanations, inspiring research questions, answering questions, and solving problems. Science fil- ters through many ideas and builds on those that work! Page 8 14 © 2012 The University of California Museum of Paleontology, Berkeley, and the Regents of the University of California • www.understandingscience.org Digging into data Evaluating an idea in light of the evidence should be simple, right? Either the results match the expectations generated by the idea (thus, supporting it) or they don’t (thus, refuting it). Sometimes the process is relatively simple (e.g., drilling into a coral atoll either reveals a thick layer of coral or a thin veneer), but often it is not. The real world is messy and complex, and often, in- terpreting the evidence relating to an idea is not so clear-cut. To complicate things further, we often have to weigh multiple lines of evidence that are all relevant to the validity of a particular idea. Tests typically generate what scientists think of as raw data—unaltered observations, descriptions, or measure- ments—but those must be analyzed and interpreted. Data become evidence only when they have been interpreted in a way that reflects on the accuracy or inaccuracy of a scientific idea. For example, an investigation of the evolutionary relationships among crustaceans, insects, millipedes, spiders, and their relatives might tell us the genetic sequence of a particular gene for each organism. This is raw data, but what does it mean? A long series of the As, Ts, Gs, and Cs that make up genetic sequences don’t, by themselves, tell us whether insects are more closely related to crustaceans or to spiders. Instead, those data must be analyzed through statistical calcula- tions, tabulations, and/or visual representations. In this case, a biologist might begin to analyze the genetic data by aligning the different sequences, highlighting similari- ties and differences, and performing calculations to com- pare the different sequences. Only then can she interpret the results and figure out whether or not they support the hypothesis that insects are more closely related to crusta- ceans than to spiders. Furthermore, the same data may be interpreted in different ways. So another scientist could analyze the same genetic data in a new way and come to a different conclusion about the relationships between insects, crustaceans, and spiders. Ultimately, the scien- tific community will come to a consensus about how a set of data should be interpreted, but this process may take some time and usually involves additional lines of evidence. CALCULATING CONFIDENCE Interpreting test results often means dealing with uncertainty and error. “Now, hold on,” you might be thinking, “I thought that science was supposed to build knowledge and decrease uncertainty and error.” And that’s true; however, when scientists draw a conclusion or make a calculation, they frequently try to give a statistical indication of how confident they are in the result. In everyday lan- guage, uncertainty and error mean that the answer is unclear or that a mistake has been made. However, when scientists talk about uncertainty and error, they are usually indicating their level of confidence in a number. So reporting a tem- perature to be 98.6° F (37° C) with an uncertainty of plus or minus 0.4° F actu- ally means that we are highly confident that the true temperature falls between 98.2 and 99.0° F. Page 9 © 2012 The University of California Museum of Paleontology, Berkeley, and the Regents of the University of California • www.understandingscience.org Competing ideas: A perfect fit for the evidence We’ve seen that evaluating an idea in science is not always a matter of one key ex- periment and a definitive result. Scientists often consider multiple ideas at once and test those ideas in many different ways. This process generates multiple lines of evi- dence relevant to each idea. For example, two competing ideas about coral atoll for- mation (island subsidence vs. formation on debris-topped underwater mountains) were evaluated based on multiple lines of evidence, including observations of reef and atoll shapes, island geology, stud- ies of the distribution of plank- tonic debris, and reef drilling. Furthermore, different lines of evidence are assembled cumulatively over time as dif- ferent scientists work on the problem and as new technolo- gies are developed. Because of this, the evaluation of sci- entific ideas is provisional. Science is always willing to resurrect or reconsider an idea if warranted by new evidence. It’s no wonder then that the evaluation of scientific ideas is iterative and depends upon interactions within the scientific community. Ideas that are accepted by that community are the best explanations we have so far for how the natural world works. But what makes one idea better than another? How do we judge the accuracy of an explanation? The most important factors have to do with evidence—how well our actu- al observations fit the expectations generated by the hypothesis or theory. The better the match, the more likely the hypothesis or theory is accurate. • Scientists are more likely to trust ideas that more closely explain the ac- tual observations. For example, the theory of general relativity explains why Mercury’s orbit around the Sun shifts as much as it does with each lap (Mercury is close enough to the Sun that it passes through the area where space-time is dim- pled by the Sun’s mass). Newtonian mechanics, on the other hand, suggests that this aberration in Mercury’s orbit should be much smaller than what we actually observe. So general relativity more closely explains our observations of Mercury’s orbit than does Newtonian mechanics. Mercury’s orbit around the sun shifts a bit with each lap, which can be explained by the theory of general relativity. • Scientists are more likely to trust ideas that explain more disparate ob- servations. For example, many scientists in the 17th and 18th centuries were Atoll satellite image by NASA/Goddard Space Flight Center; coral core sample photo by Jeff Anderson, Florida Keys National Marine Sanctuary Page 10 18 © 2012 The University of California Museum of Paleontology, Berkeley, and the Regents of the University of California • www.understandingscience.org puzzled by the presence of marine fossils high in the Alps of Europe. Some tried to explain their presence with a massive flood, but this didn’t address why these fossils were of animals that had gone extinct. Other scientists suggested that sea level had risen and dropped several times in the past, but had no explanation for the height of the mountains. However, the theory of plate tectonics helped explain all these disparate observations (high mountains, uplifted chunks of the seafloor, and rocks so ancient that they contained the fossils of long extinct organisms) and many more, including the locations of volcanoes and earthquakes, the shapes of the continents, and huge rifts in the ocean’s floor. • Scientists are more likely to trust ideas that explain observations that were previously inexplicable, unknown, or unexpected. For an example, see Rudolph Marcus’s story below … JUMPING ELECTRONS! As chemical reactions go, electron transfers might seem to be minor players: an elec- tron jumps between molecules without even breaking a chemical bond. Nevertheless, such reactions are essential to life. Photo- synthesis, for example, depends on pass- ing electrons from one molecule to another to transfer energy from light to molecules that can be used by a cell. Some of these reactions proceed at breakneck speeds, and others are incredibly slow—but why should two reactions, both involving a single electron transfer, vary in speed? In the 1950s, Rudolph Marcus and his colleagues developed a simple mathemati- cal explanation for how the rate of the reaction changes based on the amount of free energy absorbed or released by the system. The explanation fit well with actual observations that had been made at the time, but it also generated an unintuitive expectation—that some reactions, which release a lot of energy, should proceed surprisingly slowly, and should slow down as the energy released increases. It was a bit like suggesting that for most ski slopes, a steeper incline means faster speeds, but that on the very steepest slopes, skiers will slide down slowly! The expectation generated by Marcus’s idea was entirely unanticipated, but nevertheless, almost 25 years later, experiments confirmed the surprising ex- pectation, supporting the idea and winning Marcus the Nobel Prize. What happens when science can’t immediately produce the evidence relevant to an idea? Absence of evidence isn’t evidence of absence. Science doesn’t reject an idea just because the relevant evidence isn’t readily available. Sometimes, we have to wait for an event (e.g., the next solar eclipse), hope for a key discovery (e.g., transitional whale fossils in the deserts of Pakistan), or try to develop a new technology (e.g., a more powerful telescope), and until then, must suspend our judgment of an idea. Rudolph Marcus Rudolph Marcus image provided by the California Institute of Technology Page 11 © 2012 The University of California Museum of Paleontology, Berkeley, and the Regents of the University of California • www.understandingscience.org Competing ideas: Other considerations In evaluating scientific ideas, evidence is the main arbiter; however, sometimes the available evidence supports several different hypotheses or theories equally well. In those cases, science often applies other criteria to evaluate the explanations. Though these are more like rules of thumb than firm standards, scientists are more likely to put their trust in ideas that: • generate more specific expectations (i.e., are more testable). For example, a hypothesis about hurricane formation that generates more specific expectations about the conditions under which they are likely to form might be preferred over one that just suggests what time of year they should be common. • can be more broadly applied. For example, a theory about the nature of force that applies to both macroscopic interactions (e.g., the pull of Earth’s gravity on an apple) and subatomic interactions (e.g., between protons and electrons) might be preferred over one that only applies to interactions between large objects. Page 12 20 © 2012 The University of California Museum of Paleontology, Berkeley, and the Regents of the University of California • www.understandingscience.org • are more parsimonious. For example, a hypothesis about the evolutionary rela- tionships among hummingbird species that involves only 70 evolutionary changes might be preferred over one that postulates 200 changes. THE PRINCIPLE OF PARSIMONY The principle of parsimony suggests that when two explanations fit the observa- tions equally well, a simpler explanation should be preferred over a more convo- luted and complex explanation. For a hypothetical illustration, imagine that we have only a few lines of evidence in a case of cookie jar pilfering: a broken and empty cookie jar, a crumb trail leading to the doggie door, and Fido’s bellyache. Perhaps Fido stole the cookies, or perhaps it was all a set-up: the parrot knocked the jar off the table and ate the cookies, the cat tracked the crumbs to the door, and Fido has a bellyache because he got into the neighbor’s garbage can. Both explanations fit all the available evidence—but which is more parsimonious? • are more consistent with well-established theories in neighboring fields. For example, a major argument against the theory of evolution when Darwin first proposed it was that the theory didn’t mesh with what was known about the age of the Earth at the time. Physicists had estimated the Earth to be just 100 million years old, a length of time that was deemed insufficient for evolution to account for the diversity of life on Earth today. However, as our understanding of geol- ogy and physics have improved, the age of the Earth has been more accurately pegged at several billion years old—a view that squares well with the idea that all life on Earth evolved from a common ancestor. Page 13 © …