A beginner’s guide to conducting reproducible research

This is a rather good read for anyone interested in reproducible research (i.e., everyone who tries to become a researcher as a career)

TLDR:

Why reproducible research

  1.  reproducible research helps researchers remember how and why they performed specific analyses during the course of a project.
  2.  reproducible research enables researchers to quickly and simply modify analyses and figures.
  3.  reproducible research enables quick reconfiguration of previously conducted research.
  4.  conducting reproducible research is a strong indicator to fellow researchers of rigor, trustworthiness, and transparency in scientific research.
  5.  reproducible research increases paper citation rates (Piwowar et al. 2007, McKiernan et al. 2016) and allows other researchers to cite code and data in addition to publications.

A three-step framework for conducting reproducible research

Before data analysis: data storage and organization

  1. data should be backed up at every stage of the research process and stored in multiple locations.
  2. Digital data files should be stored in useful, flexible, portable, nonproprietary formats.
  3. It is often useful to transform data into a “tidy” format (Wickham 2014) when cleaning up and standardizing raw data.
  4. Metadata explaining what was done to clean up the data and what each of the variables means should be stored along with the data.
  5. Finally, researchers should organize files in a sensible, user-friendly structure and make sure that all files have informative names.
  6. Throughout the research process, from data acquisition to publication, version control can be used to record a project’s history and provide a log of changes that have occurred over the life of a project or research group.

During analysis: best coding practices

  1. When possible, all data wrangling and analysis should be performed using coding scripts—as opposed to using interactive or point-and-click tools—so that every step is documented and repeatable by yourself and others.
  2. Analytical code should be thoroughly annotated with comments.
  3. Following a clean, consistent coding style makes code easier to read.
  4. There are several ways to prevent coding mistakes and make code easier to use.
    1. First, researchers should automate repetitive tasks.
    2. Similarly, researchers can use loops to make code more efficient by performing the same task on multiple values or objects in series
    3. A third way to reduce mistakes is to reduce the number of hard-coded values that must be changed to replicate analyses on an updated or new data set.
  5.  create a software container, such as a Docker (Merkel 2014) or Singularity (Kurtzer et al. 2017) image (Table 1) for ensuring that analyses can be used in the future

After data analysis: finalizing results and sharing

  1. produce tables and figures directly from code than to manipulate these using Adobe Illustrator, Microsoft PowerPoint, or other image editing programs. (comment: for example, can use csvsimple package in latex)
  2. make data wrangling, analysis, and creation of figures, tables, and manuscripts a “one-button” process using GNU Make (https://www.gnu.org/software/make/).
  3. To increase access to publications, authors can post preprints of final (but preacceptance) versions of manuscripts on a preprint server, or postprints of manuscripts on postprint servers.
  4. Data archiving in online general purpose repositories such as Dryad, Zenodo, and Figshare

Useful Links

Ford Foundation Fellowship Programs

The National Academies of Sciences, Engineering, and Medicine is accepting applications for the 2023 Ford Foundation Fellowship Programs. Eligibility and online application information are available on the Ford Foundation Fellowship Programs website.

 

Through its program of fellowships, the Ford Foundation seeks to increase the diversity of the nation’s college and university faculties by increasing their ethnic and racial diversity, maximize the educational benefits of diversity, and increase the number of professors who can and will use diversity as a resource for enriching the education of all students.

 

Please encourage eligible candidates at your institution to apply to be a part of the Ford Fellows and celebrate the legacy of this impactful program!

 

Eligibility Requirements: 
  • U.S. citizens, U.S. nationals, and U.S. permanent residents (holders of a Permanent Resident Card); individuals granted deferred action status under the Deferred Action for Childhood Arrivals Program (DACA) program;¹ Indigenous individuals exercising rights associated with the Jay Treaty of 1794; individuals granted Temporary Protected Status; asylees; and refugees, regardless of race, national origin, religion, gender, age, disability, or sexual orientation;
  • Individuals with evidence of superior academic achievement (such as grade point average, class rank, honors or other designations); and
  • Individuals committed to a career in teaching and research at the college or university level in the U.S.
1Eligibility includes individuals with current status under the DACA Program, as well as individuals whose status may have lapsed but who continue to meet all the USCIS guidelines for DACA.

 

Stipends: 
  • Predoctoral: $27,000 per year for three years
  • Dissertation: $28,000 for one year
  • Postdoctoral: $50,000 for one year

 

Application Deadline Dates: 
  • Predoctoral: December 15, 2022 (5:00 PM EST)
  • Dissertation: December 8, 2022 (5:00 PM EST)
  • Postdoctoral: December 8, 2022 (5:00 PM EST)

 

Supplementary Materials Deadline Date (Submitted Applications): 
  • January 5, 2023 (5:00 PM EST)

 

Thank you for your assistance in forwarding this message to prospective applicants!

 

Sincerely yours,

 

Elizabeth Prescott, D.Phil.
Director, Fellowships Office
500 5th Street, NW
Washington, DC 20001

 

Graduate Study Center Opening November 1st and 2nd

The time has finally arrived to open the Graduate Student Study Center on the 3rd and 4th floors of Devon Energy Hall. This area is open only to graduate students and is a quiet study area where you can meet your fellow ECE colleagues, make new friends and help one another as you become engineers.
This event is sponsored by the ECE Graduate Student Society. There will be drinks, snacks and of course a giveaway!
If you would like to choose a desk, please meet the new ECE GSS and faculty advisor Dr. Sangpil Yoon along with the ECE staff on November 1, 2022, from 3pm-5pm at the DEH large team room 345 or November 2, 2022, from 2pm-5pm at the DEH large team room 326. Selections for both the 3rd and 4th floors will be done here.
The configuration has changed. If you had a spot previously, you will be choosing a new spot. If you have a refrigerator or microwave, on either floor please contact Dr. Keele, rckeele@ou.edu. If you are unable to make either date, please contact Stephanie Gill, srg@ou.edu on November 3rd. Desks will be chosen on a first come first served basis.

Seeking outstanding female PhD candidates within two years of completion

The Norman chapter of P.E.O. (a women’s philanthropic organization) is looking for an outstanding female PhD candidate.  Their mission is to support women in the attainment of their educational goals.  The P.E.O. Scholars Award (PSA) a $20k grant for PhD students within two years of completion of their degree, with a well-defined research project and within at least a year to completion: https://www.peointernational.org/peo-scholar-awards . The deadline for us to submit a candidate in 11/13.

OU Engineering Information Session

 
When the first president of the University of Oklahoma stepped off the train to look at his new environment, he exclaimed, “What possibilities!” 
 
At the Gallogly College of Engineering, we’re still exploring what’s possible.
 
Join us!
OU Engineering Information Session
 
Be part of the next generation of researchers
 
Join us to learn about:
Graduate Programs
Research and Opportunities
Financial Support and Scholarships
Alumni Engagement
And more!
 
Monday, October 24, 2022
9:00am CDT
 
Zoom link will be provided upon registration.
 
 
For more information visit ou.edu/coe

Faculty Position Openings

Three Faculty Positions in Data Science: Human-Computer Teaming and  Interactive Decision Making; Artificial Intelligence Architectures; and Trustable  Artificial Intelligence at the University of Oklahoma, Norman Campus 

Positions Available: As part of a multiyear effort to grow world-class data science and data enabled research across The University of Oklahoma (OU), the Gallogly College of Engineering  (GCoE), Department of Electrical and Computer Engineering and/or Department of Computer  Science, in partnership with the Dodge Family College of Arts and Sciences (CAS), welcomes  applications for a cluster of three (3) faculty positions from candidates whose experiences and  interests have prepared them to be an integral contributor engaged in scientific discovery,  developing talent, solving global challenges, and serving our society. This year we are focusing  on data science foundational and enabling technologies. In subsequent years, we’ll be hiring  additional data science and data-enabled research faculty.  

The University, as part of its Lead On, University strategic plan has committed to creating world class capabilities in data science, artificial intelligence (AI), machine learning (ML), and data enabled research. In July 2020, the University established the Data Institute for Societal  Challenges (DISC) to grow convergent data-enabled research to solve global challenges. DISC  currently has over 130 faculty members across OU campuses, nine communities of practice, seed  funding programs, and an extended network of approximately 300 data scientists and data enabled researchers across many disciplines (https://www.ou.edu/disc). 

Three positions: 

1) Professor or Associate Professor in Human-Computer Teaming and Interactive  Decision Making: Humans and computers have complementary knowledge and skillsets.  To solve challenging problems, we need to team this expertise together for effectiveness,  reliability, efficiency, and adoption of many data-driven solutions. This area is cross 

disciplinary, and we seek a senior faculty member with expertise in one or more of human computer teaming, visualization, visual analytics, human-machine interaction, decision  theory, HCI, human factors and industrial engineering, or cognitive psychology. This  faculty member will be a vital core team member in data science and data-driven decision  making with a home department in ECE and possible joint appoint in ISE, Computer  Science, Psychology, and/or Political Science. 

Applications should be submitted online via Interfolio at  

http://apply.interfolio.com/112374. Inquires can be addressed to Professor David Ebert,  chair of the search committee at ebert@ou.edu.  

2) Assistant Professor in AI Architectures: We seek to recruit a transdisciplinary faculty  member with expertise in one or more of the following areas: scalable, high-performance  software and hardware architectures for AI and advanced analytics, advanced and  domain-tailored data science, AI (trustable, science-based, and human-guided), and  human-computer teaming. Specific areas of interest include probabilistic, neuromorphic,  and novel architectures, software pipelines and operating system architectures to support  high-performance analytics, and enable real-time trustable AI and decision-making.  Since  traditional computing architectures are still based on solving problems from the 20th  century, new computing hardware and software architectures are needed to optimize  computing for AI and machine learning and many new approaches to science and  engineering. This faculty member will grow and complement work in computer  engineering, computer science and the new OU quantum center (CQRT) with a home  department in ECE and possible joint appointments where appropriate.

Applications should be submitted online via Interfolio at   

http://apply.interfolio.com/112359.  Inquires can be addressed to Professor David Ebert,  chair of the search committee at ebert@ou.edu.  

3) Assistant Professor in Trustable AI. We are seeking an Assistant Professor in Trustable  AI. Human-guided, science-based, explainable AI (xAI) are key areas to ensure AI is  understandable, reliable, and robust for real-world applications. This faculty member will  grow our expertise in one of the most rapidly developing and vital fields of data science,  with a primary home in ECE and potentially joint appointments in CS, Psychology, and  ISE. We seek a faculty member with expertise in one or more of science-based AI or  machine learning (ML), human-guided AI/ML, explainable AI/ML, and closely related  topics.  This faculty member will be a vital core team member in data science, AI, and  data-driven convergent research solutions to global challenges. This faculty member will  provide vital capabilities that will empower research in all four strategic verticals and grow  the data science ecosystem on campus to create the critical mass in data science needed  for the success of the university’s strategic plan, Lead On, University. 

Applications should be submitted online via Interfolio at 

http://apply.interfolio.com/112372.  Inquires can be addressed to Professor David Ebert,  chair of the search committee at ebert@ou.edu.  

Gallogly College of Engineering:  The mission of the GCOE is to foster creativity, innovation and  professionalism through dynamic research, development and learning experiences.   

The Gallogly College of Engineering is home to the Data Science and Analytics Institute  (https://www.ou.edu/coe/dsai). The DSAI provides undergraduate and graduate certificates,  Master’s degrees, and PhD degrees in data science and analytics as well as offering workforce  upskilling to industry partners. Faculty members in GCoE and across campus participate in the  DSAI.  

The University of Oklahoma:  OU is a Carnegie-R1 comprehensive public research university  known for excellence in teaching, research, and community engagement, serving the educational,  cultural, economic and healthcare needs of the state, region, and nation from three campuses:  Norman, Health Sciences Center in Oklahoma City and the Schusterman Center in Tulsa. OU  enrolls over 30,000 students and has more than 2700 full-time faculty members in 21 colleges.   

Norman is a vibrant university town of around 113,000 inhabitants with a growing entertainment  and art scene. With outstanding schools, amenities, and a low cost of living, Norman is a perennial  contender on “best place to live” rankings.  

Visit http://www.ou.edu/flipbook and http://soonerway.ou.edu for more information. Within an easy  commute, Oklahoma City features a dynamic economy and outstanding cultural venues adding to  the region’s growing appeal.  

Qualifications 

Successful candidates must have the interest and ability to contribute significantly to the  advancement of these fields and develop a nationally recognized program of sponsored research;  teach at both the undergraduate and graduate levels; supervise graduate students and  postdoctoral fellows. A Ph.D. in computer science, engineering, or related discipline is required. 

Application Instructions 

Confidential review of applications will begin October 1, 2022. Candidates are invited to submit a 

letter of interest, names of three references who will be contacted only upon approval from the  applicant, curriculum vitae, and brief (~2-3 pages) statements of interest regarding 1) research, 2)  teaching, and 3) service. The research statement should summarize your prior contributions to  research and your goals for developing a research program at OU. The teaching statement should  summarize past instructional and mentorship experiences, and plans/goals for teaching at OU  (including existing and proposed courses) and advising a varied cohort of undergraduate and  graduate students. The service statement should describe your vision for internal service to the  academic unit, the College and the University, and for external service to our scientific community  and other stakeholders. Candidates are requested to submit their applications to the appropriate position listed above and inquiries should be directed to the search committee chairs, also listed  above. 

Inquiries should be directed to the search committee chair: 

Dr. David S. Ebert, Gallogly Chair Professor 

School of Electrical and Computer Engineering and School of Computer Science Associate Vice President of Research and Partnerships 

Director, Data institute for Societal Challenges  

University of Oklahoma 

Email: ebert@ou.edu 

Equal Employment Opportunity Statement  

The University of Oklahoma, in compliance with all applicable federal and state laws and  regulations does not discriminate on the basis of race, color, national origin, sex, sexual  orientation, genetic information, gender identity, gender expression, age, religion, disability,  political beliefs, or status as a veteran in any of its policies, practices, or procedures. This includes,  but is not limited to:  admissions, employment, financial aid, housing, services in educational  programs or activities, or health care services that the University operates or provides.  

Diversity Statement   

 The University of Oklahoma is committed to achieving a diverse, equitable and inclusive university  community by recognizing each person’s unique contributions, background, and perspectives. The  University of Oklahoma strives to cultivate a sense of belonging and emotional support for all,  recognizing that fostering an inclusive environment for all is vital in the pursuit of academic and  inclusive excellence in all aspects of our institutional mission.  

Mission of the University of Oklahoma 

The Mission of the University of Oklahoma is to provide the best possible educational  experience for our students through excellence in teaching, research and creative activity,  and service to the state and society.

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