Critical thinking is central to scientific endeavor. While there exist many definitions of critical thinking, some more esoteric than others, perhaps the one that resonates best with scientists and employers who value this skill in individuals with scientific training is that by Michael Scriven and Richard Paul for the National Council for Excellence in Critical Thinking. Critical thinking, according to them, “is the intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action.” They note that critical thinking is based on “clarity, accuracy, precision, consistency, relevance, sound evidence, good reasons, depth, breadth, and fairness.” Critical thinking so defined essentially frames what scientific investigation is all about at an intellectual level. The following represent key stages of critical thinking within a scientific context and are in accord with commonly accepted facets of experimental design:

Key Stages of Critical Thinking

Acquisition of information.
It is important in beginning to frame a question or hypothesis to acquire all the information that is potentially relevant. This is a task that must be accomplished efficiently, thoroughly, and without bias. The skill is acquired through predoctoral training, and like anything of a sophisticated technical nature relies on intuition as well. The most important source of information are studies published in scientific journals. Tools used in exploring journals for specific types of information are search engines such as PubMed and Medline and links within journal articles of interest to later articles citing them, to articles which they themselves cite, or to related articles. Being made aware of emerging articles of interest by topics or authors can be achieved by services such as CiteTrack or sometimes the journals of interest themselves. We all have a shortlist of go-to journals as well, which send links to tables of contents as issues are published. Other sources of information include, of course, data from our own experiments, unpublished data of others to which we’re privy, conversations, posters, and formal presentations. The volume of published data alone is enormous and growing exponentially. Acquiring the right information in an efficient, thorough fashion is a considerable skill.

Conceptualization.
Conceptualization is a process of discernment – what of the acquired information is important, and what isn’t – and of identifying key relationships. Its purpose is to provide a construct that is meaningful and coherent in its application to a problem. Conceptualization is in many ways an intuitive process, a modeling, and therefore one that invites and is propelled by creativity. It should be noted that conceptualization and acquisition of information are reiterative processes, one informing the other, as a conceptual construct is increasingly better honed. Conceptualization relies on weighting information based on the ‘strength’ of data, which incorporates a statistical dimension. So conceptualization, while in many ways intuitive, requires technical savvy as well. The conceptual construct is in some ways related to the premise in experimental design.

Reasoning.
The conceptualization above leads to a construct, a model of the way things might work or might relate to something of experimental interest. The question now is how to frame a question or hypothesis to test the construct or take it further. This leads us to the domain of deductive and inductive reasoning, and the closely allied processes of hypothesis-testing and model-building. Deduction, according to David Glass in Experimental Design for Biologists, is a process of reasoning from a general statement (a premise) to reach a specific and logically certain conclusion. The conclusion is bounded by and dependent on the truth of the initial premise. Induction is a process of reasoning that uses past experience, e.g. data obtained, to provide a platform for prediction, one that involves verification as opposed to falsification. Deductive reasoning draws close parallels with hypothesis testing, or more formally in an experimental context, hypothesis falsification. Inductive reasoning is used in forming questions that lead to experimental models. Glass refers to hypotheses as backward looking, and models as inductive, or future-predicting. Both are relevant to scientific thought and process.

Resources
Foundation for Critical Thinking: https://www.criticalthinking.org/

Glass, David J. Experimental Design for Biologists, 2nd ed., Cold Spring Harbor Laboratory Press.

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(Contributed by Ben Voight)

Skill sets in mathematics and computation are becoming increasingly central to scientific endeavor. Skills in mathematics developed in predoctoral training relate to statistical inference prominently, but also to kinetics, equilibria and underlying chemical energetics, modeling of systems, and imaging. Skills in computation relate to the development of algorithms to process large amounts of information, with a highly overlapping skill set formalized by the term data science.

Mathematics

I discuss here only statistical inference, as skills developed in statistical inference have the widest applicability to careers a PhD in biomedicine might pursue. Statistics continues to evolve as a science. It is as much about asking questions as it is about giving one principles to ask those questions. It has permeated virtually every field of science, owing to the growing preponderance of data and need for data scientists to make meaningful conclusions from these data sets. Statistical-minded thinking, moreover, is a natural expression of the scientific method: often, one is faced with one or more competing models and wants to determine which model best explains the (often large) amount of data one has generated or collected. Whether it is hypothesis testing, significance assessment, model comparison, there are a core set of statistical principles that are simply essential for life as a data-driven scientist and leader.

Some overview example of these points:

http://www.nytimes.com/2009/08/06/technology/06stats.html?_r=0
https://fivethirtyeight.com/.  Nate Silver is a quintessential data scientist and practitioner of  statistical inference a large range of contexts, for example politics, sports, economics, and science.

My personal advice on the subject is two-fold. First, it is important to have a solid, fundamental understanding of the basics of probability theory, and applications of that theory in statistical inference. In this regard, there are a lot of great online resources to get started:

Basics of probability theory, Statistics, applications:
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010/

Open Source Text books:
https://www.openintro.org/stat/textbook.php?stat_book=os
https://www.openintro.org/stat/index.php?stat_book=isrs

Second, identify a problem that you are interested in, a data set that you are curious about. Statistics is as much about asking questions as it is about giving one principles to ask those questions. I learned a great deal of the statistical principles I personally know by working through biological and genetics research problems.

Computation

It is clear that students who develop computational fluency, in addition to other fluencies during their dissertation work, are able to excel within the biological sciences and also in other, alternative career paths. The key reason is simple: Large amounts of information that are accessible via computer have led to data science as a complementary skill set (and even a profession!) within many fields, including biology. To me, computational fluency in data science is the ability to be accurate, efficient, flexible, and reproducible when working with a large amount of data and in performing scientific inquiries.

It is impossible to talk about fluency without referring to a specific set of skills codified in generic computational toolkits. I outline four areas where basic competencies allow for a broad set of skills translatable within and outside of biological sciences:

(i)    Effectively working on a command-line (non graphical user interface) system environment, i.e., UNIX environment.
(ii)    Computer programming using a “scripting” language to facilitate the parsing, processing, and analysis of one (or many) data sets or files
(iii)    R programming to facilitate parsing of data files, routine to advanced statistical analysis, and visualization of results in interpretable and reproducible forms.
(iv)    Reproducibility to ensure that the results one generates can be obtained by others, so that key findings stand the test of time.

A. Operating in a UNIX environment.

The key idea is that everything you can do in a “graphical” view of the world, you can do on the command line, but not everything that you want to do in data science – write programs, analyze data, etc. – can be done in a graphical user interface. If you’ve watched The Matrix, you might appreciate a little more what is involved here.  There are several standard books and online materials to help students become familiar with this environment.

A short primer: https://github.com/KorfLab/unix_and_perl
A cheat sheet: https://files.fosswire.com/2007/08/fwunixref.pdf

B. Programming: in Python

One of the first things I taught myself in graduate school was to learn how to program (script) – this completely changed what types of science I could do, and ultimately, what I now do for a living. Unlike when I was teaching myself this many years ago, the Internet is now populated with a vast number of online resources and coursework that can be used to learn this material. Today, Python programming seems to be the scripting language of choice by most students, as it provides both flexibility and extensive packages which captures the needs for most data scientists. In fact, one particular popular package (SciKit-Learn) allows users to easily apply simple to advanced Machine Learning algorithms, with access to tools to ensure reproducibility and accuracy.

Programming in Python:
https://en.wikibooks.org/wiki/Non-Programmer%27s_Tutorial_for_Python_3
http://openbookproject.net/thinkcs/python/english2e/
https://automatetheboringstuff.com/

Some hands-on activities in programming directly:
https://www.codecademy.com/learn/python
http://cscircles.cemc.uwaterloo.ca/

Key package: SciKit-Learn: https://scikit-learn.org/stable/getting_started.html

C. Programming: in R

It turns out that Python is a great tool that can allow you to do a lot of things: parse files, read and process data, and create interactive tools to run larger-scale analyses. However, this tool does not address all issues. For one, we often want to utilize some level of statistical savvy to assess significance, compare models, or perform specific types of statistical and computational analysis (e.g. differential gene expression analysis for microarray data). Second, even for data and results we generate in Python, we often need to visualize these data using summaries that are informative, intuitive, and reproducible. While there are many choices here, R is a go-to programming language of choice that can take on many of these challenges.

Basic programming in R:
https://en.wikibooks.org/wiki/R_Programming
http://tryr.codeschool.com/

Visualization in R using the package ggplot2: great data visuals:
https://rafalab.dfci.harvard.edu/dsbook-part-1/dataviz/ggplot2.html

Data handling: dplyr, tidyr packages:
http://garrettgman.github.io/tidying/

D. Reproducibility in Research

One can think of analysis as driven by a question that takes in some set of inputs (data) and generates some output (results) that bring data to bear on the question. Each step should be clear, and with the proper inputs, file, and program management, should be able to be recapitulated by another individual in good faith. Ideally this pipeline should be human understandable – the exact details should be clear and rationale for each step in the process, i.e. should be transparent to a human reading the pipeline.

It’s easy to state “Keep a good notebook.” Ideally, all biologists should. A computational notebook differs from a conventional notebook, in the sense that the pipeline has a version, and may be updated as computer bugs are found, new data is available, new visualizations are articulated, etc. It’s the job of the creator to maintain and ensure that final, publishable quality results can be issued from a specific frozen version of a developed pipeline. The good news is that there are a number of tools that help users create pipelines that have this type of reproducibility intrinsically tied to the scientific study.

In Python: Jupyter notebooks:
https://jupyter-notebook-beginner-guide.readthedocs.org/en/latest/

In R:
RStudio: https://education.rstudio.com/learn/beginner/
Shiny: http://shiny.rstudio.com/tutorial/
Markdown: http://daringfireball.net/projects/markdown/basics

Code repository/sharing:
via github: http://www.github.com
via subversion: https://subversion.apache.org/

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The validity of an experiment is directly affected by its construction and execution, hence attention to experimental design is crucial. The process of experimental design involves many elements, and often in a reiterative fashion. It can be described in terms of a framework, inductive space, system, set of experiments, and model building (see Glass, below). Implicit to these elements are controls, whose purpose is to negate error and bias. Positive and negative controls come up in virtually every discussion of an experimental protocol, but one must consider as well controls that pertain to the system, assumptions, and experimentalist. Statistics plays an enormous role is both design and interpretation, as so much of the logic used in asking experimental questions is inductive, i.e. relating to predictability and generalizability.

Experimental design uses critical thinking to address a specific scientific question or hypothesis. From the standpoint of a competency, it is in essence a form of problem-solving that focuses one’s talents and skills on pushing the envelope of knowledge. Its exact formulation differs depending on the questions being asked, so that it evolves with time and experience. The question or the tools used may be quite specific, but the skill is generalizable.

The following concepts are relevant to experimental design.

Premise. ‘Premise’ in the context of science is defined as the body of research, both what it is and where it leaves off, that provides the basis for the research question. It is not formally a component of experimental design, but it sets the stage. It is, as a competency, the ability to consider the general strengths and weaknesses of prior research before proposing additional research. It encompasses the rigor of previous experimental designs, as well as the incorporation of relevant biological variables and authentication of key resources.

System. David Glass in Experimental Design for Biologists begins to frame a working experimental design with the development of a ‘system’ that can enable the appropriate experiments to be performed. The system is very much akin to a “Materials and Methods” section of a paper and in many ways represents the resources and methodology one has at her or his disposal to answer the question posed. The system is very much rooted in concepts of validation, which are discussed below, as they relate to resources and methodology. The system is not a competency, of course, but its management is. Management is covered in a separate section of this website. It is important to point out here, however, that the focus of the experimental question and hence system integrates the separate skills of management at both logistical and strategic levels.

Statistics. Statistics is addressed as a competency in a separate section of the website. Statistical concepts most relevant to experimental design are randomization, replication, and blocking. Randomization provides a mechanism for assigning treatments to experimental units that is free from intentional or unintentional biases; replication allows us to assess the degree of existing variation in a response variable among experimental units treated alike; and blocking refers to grouping similar experimental units together and assigning the treatments of interest to the experimental units within such groups (Nettleton, below). Sample size and variation are important determinants of the power of a statistical test.

Controls. A control is defined as a person, group, event, etc., used as a constant and unchanging standard of comparison in experimentation (Dictionary.com). There are many types of controls, for example negative, positive, and assumption controls (see Glass for detailed discussions of these and others). Their deployment is a skill that goes well beyond knowing how they are defined, extending for example teasing out potentially obfuscating variables and how precisely to deal them. The adept use of controls is an art form.

Relevant biological variables. As noted, controls are performed to account for variables. But this presumes one is aware of all the variables that are relevant. NIH is currently concerned in animal and human studies with sex as a biological variable, but it suggests that others need to be considered as well, for example genetic background, age, body mass index, room temperature and lighting. Consideration of these and other variables from the standpoint of NIH enables “reviewers and the scientific community to assess the internal validity of a study – whether the findings hold up after accounting for confounding and selection biases – and the external validity a study – whether the findings, even if internally valid, apply to the ‘real world’” (Mike Lauer, Consideration of Relevant Biological Variables in NIH Grant Applications).

Reagent, methods, and equipment validation. Science is exacting – nothing can be taken for granted. This includes reagents, methods, and equipment. Examples of where projects can go seriously wrong at the level of reagents include misidentification of cell lines, taking at face-value the properties of antibodies, and assuming the genetic backgrounds of knock-out mice. The validity of methods is generally assured through the use of proper controls, but it’s the rare lab in which favored protocols haven’t mutated slowly with time. The need to review and (re)validate such protocols is important. Equipment performance is not necessarily stable with time, hence the need to recalibrate micropipettes, cell incubator CO2 tensions, water bath temperature settings, etc. At one level, having a firm understanding of reagents, protocols, and equipment provides for a productive set of experiments and efficiency in troubleshooting. At another and more important level, it is central to assuring an outcome that is both valid and reproducible.

Robust and unbiased results. NIH expects anyone submitting a research or training proposal to describe in the experimental design how he or she will achieve robust and unbiased results. Robust results are those that can be reproduced under well-controlled and reported experimental conditions. Key elements include appropriate statistical design, control, consideration of relevant biological variables and authentication of key biological or chemical resources, all of which are discussed above.

Resources

Experimental Design for Biologists, David J. Glass, 2nd Edition. This book covers the philosophy of experimentation, system validation, experimental constructs, and model-building in a highly readable and contemporaneous fashion.

At the Bench: A Laboratory Navigator, Kathy Barker, 2005. This book, which is provided to all BGS students upon matriculation, contains a chapter on how to set up and experiment and a large number of chapters on experimental techniques.

Nettleton, D. A Discussion of Statistical Methods for Design and Analysis of Microarray Experiments for Plant Scientists. The Plant Cell 18: 2112-2121, 2006.

NIH’s website on rigor and reproducibility. https://grants.nih.gov/policy-and-compliance/policy-topics/reproducibility

‘Experimental design and transparency’ represents a component of BGS required training. You can view coverage of the topic in a module that provides background, policies, and case studies at the ‘Required Training’ website of BGS (https://www.med.upenn.edu/bgs-rcr-exdes/).

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The ability to manage oneself and one’s team in identifying and achieving goals is of paramount importance in virtually all careers. The sections below describe two areas of management – time and project management – that most students try to cultivate, implicitly or directly, during predoctoral training.

Time Management

Managing time, particularly when there seems to be so little of it in graduate school, is one of the most difficult skills to master. Yet, it is one of the most important, as it is often the time spent on what truly matters that is the limiting factor in productivity. As many students find out, the most effective way to manage time is, in fact, to protect it. The skill of managing time can devolve, in our experience, into four simple activities:

1. Making “to-do” lists
2. Identifying one’s times of peak productivity during the day (or night)
3. Organizing time into “blocks”
4. Eliminating “time wasters”

To-do lists

These lists can be separated into a Master-list and a daily or weekly To-do list. Master-lists might contain goals and commitments for a month or even year at a time, while a to-do list is a way of identifying the more immediate tasks and demands on one’s time. Keeping separate life and work lists is often useful. Lists should be captured centrally, in a notebook, a phone notepad, etc.; one should avoid sticky notes and scraps of paper.  A good to-do list should be quite specific with regard to what needs to be done. Eisenhower’s Urgent/Important matrix (http://www.eisenhower.me/eisenhower-matrix/) can help in this regard, specifically in the sense of categorizing and prioritizing activities that need attention versus those that can be ignored. There are 4 categories in this matrix: urgent and important, urgent and not important, important and not urgent, and not urgent and not important. Items should be listed in a way that ensures the likelihood of accomplishing them so as to prevent frustration and to keep tasks in perspective. Having lists in which items can be crossed off with some degree of frequency is both satisfying and encouraging.

Times of peak productivity

Be cognizant of the times during the day or night you are most productive. Knowing when you work best and subsequently saving this time for important tasks will allow you to use that time on tasks that require concentration and stamina. The Morningness/Eveningness Questionnaire (http://www.cet-surveys.com/index.php?sid=61524&newtest=Y&lang=en) can help to identify times when you may be most productive.

Time Blocks

Focusing on one task at a time helps with completing tasks. Block out time to plan and accomplish high priority tasks, and be realistic about constraints on time.  In graduate school, coursework is pre-arranged but research time is flexible. For example, reading papers and designing or performing technically challenging experiments require more concentration, therefore use peak productivity blocks of time for these types of tasks. BeFocused is an iPhone App that allows you to set blocks of time with prearranged breaks to keep you on task. And remember that focus is implicit to any block of time. As we advise below, don’t let too much in the way of extraneous thoughts distract you from what’s at hand.

“Time-wasters”

Value your time and be ruthless about safe-guarding it. Cluttered environments, constant notifications about new mail, perusing websites for entertainment, and even multi-tasking can force time away from a single, focused task. Check e-mail periodically but turn it off when focusing on “Must do” tasks. If you are involved in unproductive projects stop as soon as you can and recalibrate your day.

Project management

Project management involves a wide-range of skill sets that relate to planning, scheduling, managing conflict, and controlling or assessing individual projects. No formal training in these skills exists in predoctoral work, which is unfortunate, but most students come to grips with what’s needed by the time they begin thesis research.

When graduate students first think about thesis projects there is almost always a sense of wonder and creativity. This wave of enthusiasm can, unfortunately, be lost over time should the student fail to recognize the practicalities of translating goals into reality. This is where pragmatism with regard to project management comes into play. How a student manages a thesis project is a major factor in its timely completion.

Project Overview

Prior to entering a thesis laboratory, a graduate student passes a “candidacy examination,” which typically entails writing a research proposal. This is a good chance to generate a project overview, i.e. a chance to evaluate each and every component of the project, the way in which the components interrelate, probabilities of success for each, potential obstacles, and time-lines. The more time spent thinking about a project at its inception, the more efficient the time spent carrying out the project’s objectives. Any good project, however, demands frequent overviews throughout its life – projects can change with time depending on outcomes, opportunities, and exigencies. Individual development plans (IDPs), required on an annual basis for BGS students, are used in part to facilitate this kind of review. But any student is well advised to review her or his project on a more frequent basis.

An illustration of the overview process is provided through the Critical Path Model (https://asana.com/resources/critical-path-method). The essential aspects of this model require the project manager to 1) make a list of all activities that would be required to complete the project, 2) identify the time that each activity would take to be completed, 3) consider the dependencies among activities, and 4) identify milestones or deliverable items, either from each activity or from the entire project.

Scheduling

Keeping on a schedule and respecting deadlines is critical to the success of a project. Some schedules are non-negotiable, such as timelines for presenting to your thesis committee, abstracts for a meeting, and submission of grant proposals. Others are softer, such as finishing a draft of a paper or completing a set of experiments. One issue to contend with regarding soft deadlines is Parkinson’s Law, which simply states, “Work expands so as to fill the time available for its completion”. While we often set arbitrary deadlines for ourselves, its important not to allow too much time for completion, otherwise according to Parkinson’s Law, you will take a lot longer to complete a project (paper, experiment) than if you set a tighter deadline.

Be realistic about budgeting an appropriate amount of time for any given task. And prioritize, as over-extending can be dangerous. Individual tasks may only take a day or two, but cumulatively they add up. Holding to a realistic schedule, one that’s constantly reviewed and prioritized, ensures productivity externally and a sense of accomplishment internally. It is as important to consider the time to review results with a mentor and planning for next steps as it is to consider the actual experimental timeframe.

Risk Management

This goes beyond brainstorming potential risks, and applies to identifying and strategizing how to deal with those risks if they are to occur.  Are you performing a new technique? Do you have to wait on a lengthy submission process for animal or human subject protocols? Could equipment or reagent delivery be delayed? You may not even be sure what roadblocks will occur until a project is started, but the primary goal with a risk analysis is to determine what potential risks could occur and how you’ll prevent or handle each of them. Even things that may seem out of your control are technically within your control since you can plan for or react once they occur. For example, reviewing a protocol in advance of performing the experiment ensures that you have everything needed for a particular series of experiments so that you give yourself the best chance for the experiment to run smoothly and in the least amount of time possible. Risk management is the process of identifying risk, assessing risk, and taking steps to reduce risk to an acceptable level¹. The overarching goal of a good risk management approach is to determine the processes, techniques, tools, and roles and responsibilities of anyone working on a specific project and to manage them according to the overall benefit of the project.

http://www.diycommitteeguide.org/resource/what-a-risk-management-strategy

¹National Institute of Standards and Technology, July 2002, Risk Management Guide for Information Technology System, Special Publication 800-30, p. 1.

Project Management Institute, A Guide to the Project Management Body of Knowledge, (PMBOK Guide), Fourth Edition, ANSI/PMI 99-001-2008, pp. 273–312.

Conflict management

Conflict in any organization is inevitable. As a graduate student you may experience conflict within the classroom or laboratory. The skills you develop to deal with conflict will support you throughout your career. Conflicts can be broadly categorized into two types: people-focused, which tend to be more emotion based, and issue-focused which tend to be more rational based.

While there are many styles and tactics that individuals and organizations use to manage conflict, most agree the basic principles can be distilled down to a few important strategies. We note these strategies here. Be aware, though, that some conflicts can seem intractable. Students are encouraged to use all resources available to them in such cases, including their graduate group chair and BGS staff.

Accommodation. This strategy is employed when keeping peace is important or the conflict is relatively minor. By making an accommodation for the person who initiated the conflict, or giving in to their demands, tensions are often reduced and conflicts can be resolved quickly. But when other more important conflicts arise, the individual who was accommodated in the past may be more likely to engage in more challenging demands. The expression “pick your battles” is appropriate when considering accommodating strategies.

Avoidance. This strategy aims to postpone or ignore the conflict, thus hoping the problem resolves itself. Using this strategy too often is not beneficial as it may give the impression that by actively avoiding a conflict, the individual has no strong say or and may even be perceived as weak. While this strategy has merit, it should be used sparingly. If you are involved in this type of behavior, it may signal that it is time to take the issue to a supervisor or principal investigator for resolution.

Collaboration. This strategy takes into account many people and ideas that may surround a conflict. By integrating these ideas, an agreement of how to move forward can be mutually agreed upon, primarily because everyone had some say in the resolution. This strategy can be time-consuming, and not useful for some conflicts that need to be resolved quickly.

Compromise. This strategy requires that both sides involved in a conflict give up some aspects of their position and agree on a  common acceptable solution. This is particularly useful when a conflict arises between two individuals or groups that have equivalent power or stature in an organization or laboratory. This strategy may or may not require a moderator, but a clear outcome must be communicated to all involved.

Budgeting

While managing formal budgets is usually beyond the scope of the role a graduate student plays, it is always helpful to be conscientious of how much a project costs. Understanding the basic cost of materials, along with the cost of resources associated with bringing on those resources will help keep your project on task as each added cost can make or break a project.  Most of science has a budget attached to it with a finite amount of money and time to spend it. The better the idea you have of the cost of an overall project, the better you can plan your resources appropriately or look for alternatives that could achieve the same outcome with less cost or money.

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The ability to communicate effectively is a crucial skill in science, one whose importance of course extends to most professions. Graduate groups as a whole, and mentors in later years, place an enormous amount of energy in cultivating this skill as a part of predoctoral training. The intent is to have students be able to communicate their work and ideas with precision, clarity, and purpose. Training is achieved through venues as varied as grant or grant-like proposals, scientific manuscripts, one-on-one sessions with mentors and others, journal clubs, post-rotation talks, candidacy examinations, lab meetings, and local and/or national meetings. Communication is not just about conveying one’s ideas to others: throughout all of these activities one learns to listen effectively, both to improve skills and, as importantly, to exchange ideas that promote great science.

The mechanisms of communication that are a focus in predoctoral training are divided below into written and oral.

Written Communication

We express ourselves in writing every day. But few of these activities are as exacting as writing scientific manuscripts and grant proposals. They are consummate exercises in communication. Several graduate groups have courses in scientific writing that address this need directly. Students also gain a great sense of what works, and what doesn’t, through the enormous number of papers they read and the advice of mentors in their own efforts. And while one might think that this form of communication is more or less specific to science, it’s not. Absent a certain amount of jargon and tailoring, it speaks to getting ideas and facts across in a clear and compelling fashion.

Scientific manuscripts. Every student will write during the course of predoctoral training several manuscripts covering her or his work and sometimes, by way of a review, the discipline itself. Writing is undertaken with the guidance of a mentor, with attention given through often multiple drafts to all elements of style and content. All will agree that it’s a transformative process: it is the culmination of a process that begins with a unique and biologically relevant question, addresses that question through a series of well conceived, executed, and interpreted experiments, and communicates a synthesis of the data to fellow scientists and the public as both a meaningful advance and a premise for the next set of questions. Could it get much more important?

Grant proposals. All students are encouraged to write proposals for individual National Research Service Awards (i.e. F30s and F31s) provided by the National Institutes of Health, or awards from other government or nongovernment agencies. The reasons for doing so should make sense: training, prestige, and funding. The requirement in the candidacy exam for a proposal regarding one’s intended thesis work provides a similar, if more restricted, experience. Additionally, students will have access to mentors’ proposals and will often help in writing or reviewing them. A grant proposal is a vehicle for the ideas, perspectives, and direction unique to an investigator. Putting together a grant proposal is an intellectual exercise without parallel, and one that is immensely satisfying for this and for what the funding affords.

And by way of advice: if you have access to a writing course through your or another graduate group, take advantage of it! Here for example are the resources used in the Cell and Molecular Biology graduate group’s Scientific Writing course, which together with lectures additionally works on writing skills through structured excersises in small-group settings:

• The Science of Scientific Writing by George D. Gopen and Judith A. Swan, American Scientist 78: 550-8, 1990, also available at http://www.americanscientist.org/ (use search term (‘Gopen and Swan’).
• Style: The Basics of Clarity and Grace and/or Style: The Lessons in Clarity and Grace by J. M. Williams.
• How to Write and Publish a Scientific Paper by R.A. Day and B. Gastel.

Oral communication

It’s rare to have a student at the outset of training who can communicate the complexities of a scientific endeavor with poise and confidence. Some individuals have talents that predispose them to talking within small or large groups, to be sure, but effective oral communication is in fact a skill that comes with training, repetition, and patience.

One-on-one meetings with an advisor. An especially vital form of communication is that between a student and his or her mentor, occurring usually on a weekly basis and beginning in the first year of training with rotation projects. The purpose of these meetings is usually to go over acquired data, interpretations, immediate needs, and plans. The student is able to achieve through these meetings a broader perspective on problem-solving, of course, but any worthwhile exchange requires the student to have worked in advance through the data and its implications to the fullest extent possible and to then present his or her thoughts at the meeting concisely, in prioritized form, and without ambiguity. The one-on-one with an advisor is mirrored to varying extents in conversations with collaborators and, at scientific meetings, with individuals interested in your poster or in communicating with you further.

Lab meetings. Students learn quickly through the medium of small group settings – especially lab meetings – the extraordinary value of working with diverse and talented individuals. A lab meeting is perhaps more formal than a one-on-one exchange with an advisor, but it covers the same territory, i.e. data, interpretations, needs, and plans. The need for clarity is paramount. Framing the presentation is also essential – it’s not the venue for simply dumping data and having everyone make of it what they will. Rather, the presentation requires an introduction as an overview to bring everyone up to speed, a logical sequence of data, and a conclusion that makes clear what’s been gained and what’s required going forward. Discussion of any slide relating to data should include the type of experiment it represents, the rationale for why the experiment was undertaken, a description of the data, an interpretation of the data including its statistical strength, and a conclusion. Objectivity is key – trying to put a spin on data will fool no one and will be counterproductive in more ways than one could ever want.

Large group (intramural). An extension of the lab meeting is a presentation to larger groups of affiliated laboratories or one’s graduate group or department. The skills are much like those employed in a lab meeting, but at a higher level. One must be able to gauge the background and needs of the audience, develop a compelling narrative, create an engaging set of slides and/or other audiovisuals, comport oneself well in the sense of strength and modulation of voice, eye-contact, and mannerisms, and keep a sense of proportioned and overall timing.

Regional and national meetings. Participation in regional and national meetings is encouraged for all students, especially when they have a reached a point in their research when publication is imminent. Participation often takes the form of a poster presentation, but it can take the form of a short talk as well, for example based on how organizers perceive the quality and fit of the abstract with other elements of the meeting. As an aside, it is not a bad idea to become a member of the scientific society most relevant to your work or future aspirations and to become involved to the extent possible in administrative/advisory tasks. Your society’s familiarity with you may help in positioning your work for a talk, but it has incredible benefits in terms of networking. Talks at the level of regional and national meetings will be short, generally 15–20 min, and many of the people in attendance will be interested in what you and your lab are doing from their position of expertise. It is important that your talk is clear and to the point – it is not a matter of showing how much you’ve done but what’s most important. One or two points well made, and thus memorable, are far better than a quick succession of too many slides. The slides you use must be crystal clear, and each one must count. You will not be able to speak beyond your time constraint. What this means is that you need to practice your talk and its timing many times beforehand, especially in front of colleagues, all the while honing the words and slides. Talks are usually opened to questions at the end, so it’s important to anticipate what these questions might be. In your response to a question, it is particularly helpful to reiterate the question before proceeding to address it. All the skills mentioned in the previous section have a bearing here, but the constraints and, yes, the opportunity make especially important your professionalism. As an aside, be prepared to engage in discussions with individuals who are potential collaborators/competitors afterward. Your advisor can (and should) help with this.

Interactions with the public. Who of us as scientists have not discussed the work we do with members of our family, friends, the person sitting next to us on an airplane, or others at social occasions? These kinds of discussions are important. But the impact of scientists through interactions with society extends well beyond them. A good discussion of this impact is provided in the RCR module ‘Scientists as Responsible Members of Society.’ This module also provides references and case-studies that that address this in depth. Interactions with the public, of course, extend to outreach. The Neuroscience Graduate Group’s GLIA initiative (https://www.upennglia.com/outreach) provides a number of extraordinary examples of how effective this can be.

Two great resources referring to oral communication skills, provided by Mike Nusbaum, are “Talk Preparations and Presentation” and “Interactive Learning.” They are included here.

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