Project & data management

Best practices

Rick Gilmore & Alaina Pearce

Prelude

– NYU Health Sciences Library (2013)

Figure 1: https://penn-state-open-science.github.io/sleic-best-practices-2025-12-02/

Agenda

  • Our hidden one
  • How
  • Where

Rick Gilmore

Alaina Pearce

Our hidden agenda

https://penn-state-open-science.github.io

Best practices in

  • Project management
  • \(+\) Data management
  • \(\rightarrow\) Sharing

Sharing…

  • Accelerates discovery
  • Bolsters credibility

An ethical thing to do

Whereas regulators of human subjects research often view data sharing solely in terms of potential risks to subjects, we argue that the principles of human subject research require an analysis of both risks and benefits…

– Brakewood & Poldrack (2013)

…such an analysis suggests that researchers may have a positive duty to share data in order to maximize the contribution that individual participants have made.

– Brakewood & Poldrack (2013)

Tenopir et al. (2020) Fig 7

Tenopir et al. (2020) Fig 8

Tenopir et al. (2020)
  • Must publish first
  • No time, funding
  • No permission
  • Not required
  • No place to put
  • Insufficient skills

Houtkoop et al. (2018)

Houtkoop et al. (2018) Figure 3a

Houtkoop et al. (2018) Figure 3a
  • Misinterpretations of the data
  • Scooped
  • Control over IP
  • Unintended secondary use
  • Invalid conclusions, errors

Houtkoop et al. (2018) Figure 3b

Houtkoop et al. (2018) Figure 3b
  • Invalid conclusions
  • Control over IP
  • Errors
  • Misinterpretation

Moving the needle

Houtkoop et al. (2018) Figure 3b

Houtkoop et al. (2018) Figure 3b
  • Required by funder, journal, institution
  • Encouraged by funder, journal, institution
  • Easy, shown how
  • Garner citations, recognition, bigger/more grants

Funders think you should

“Preparing your data management and sharing plan” (n.d.)

“Data management & sharing policy overview” (n.d.)

NIH Data Management and Sharing Plan (DMS)

to promote the management and sharing of scientific data generated from NIH-funded or conducted research.

– National Institutes of Health (n.d.)

NIH Data Management and Sharing Plan (DMS)

NIH Data Management and Sharing Plan (DMS)

  • 2p DMS plan required
  • Must justify reasons if not sharing
  • Sharing plans become formal part of award agreement

“Research data management” (n.d.)

Houtkoop et al. (2018) Figure 3b
  • Required by funder, journal, institution
  • Encouraged by funder, journal, institution
  • Easy, shown how
  • Garner citations, recognition, bigger/more grants

How

Giphy

https://teacherhead.com/2017/02/27/reinventing-the-wheel-again/

Pay me now, or pay me later

– PathfindersPerform (2013)

Principles

  • Plan your work, work your plan
  • “Good enough” practices
  • Document
  • Automate
  • Get credit

Plan your work

flowchart LR
  A(["Idea"]) --> B["Proposal"]
  B --> C("Project_1")
  B --> D("Project_2")
  D --> E("Paper_2")
  C --> F("Paper_1")
  E --> G("Fame & Glory")
  F --> G
Figure 2: From proposal to project 
flowchart LR
  A(["Idea"]) --> B["Proposal"]
  B --> C("Project_1")
  B --> D("Project_2")
  B --> I("IRB protocol")
  G(["Data mgmt plan"]) --> B
  G <--> I
  C --> I
  D --> I
  D --> E("Paper_2")
  C --> F("Paper_1")
Figure 3: From proposal to project: More realistic 
flowchart LR
  A["Project_1"] --> B["Survey"]
  A --> C["fMRI"]
  A --> D["Task_1"]
  A --> E["Task_2"]
  B --> P["Data_pipeline_1"]
  C --> Q["Data_pipeline_2"]
  D --> R["Data_pipeline_3"]
  E --> R
  F["Data mgmt plan"] --- B & C & D & E & P & Q & R
  P --> G["Analysis_1"]
  Q & R --> H["Analysis_2"]
  G & H --> I["Poster"]
  G & H --> J["Paper"]
Figure 4: One project workflow 
flowchart TD
  A["Project_1"] --> B["Survey"]
  A --> C["fMRI"]
  A --> D["Task_1"]
  A --> E["Task_2"]
  B --> P["Data_pipeline_1"]
  C --> Q["Data_pipeline_2"]
  D --> R["Data_pipeline_3"]
  E --> R
  F["Data mgmt plan"] --- B & C & D & E & P & Q & R
  P --> G["Analysis_1"]
  Q & R --> H["Analysis_2"]
  G & H --> I["Poster"]
  G & H --> J["Paper"]
  C & D & G & H & F --> K["Data repository"]
Figure 5: Planned data sharing to a repository 
flowchart LR
  A["Project_1"] --> B["Survey"]
  A --> C["fMRI"]
  A --> D["Task_1"]
  A --> E["Task_2"]
  B --> P["Data_pipeline_1"]
  C --> Q["Data_pipeline_2"]
  D --> R["Data_pipeline_3"]
  E --> R
  F["Data mgmt plan"] --- B & C & D & E & P & Q & R
  P --> G["Analysis_1"]
  Q & R --> H["Analysis_2"]
  G & H --> I["Poster"]
  G & H --> J["Paper"]
  B & C & D & G & H & F --> K["OpenNeuro"]
  B --> L["OSF"]
  K --- L
Figure 6: Planned data sharing in multiple repositories

Lesson learned

Plan your workflow in as much detail as you possibly can before you start.

Lesson learned

Plans change.

So, evaluate, revise, and update.

Document what you changed, when, and why.

Lessons learned

Recommended practice

Version your protocol.

Manual is fine: e.g, nsf-2412345-protocol-2025-12-02v01.docx.

Automated is better:

Google Docs keeps version histories.

Quarto enables git-versioned web sites as protocols.

Examples

Soska et al. (2021) Figure 1

Soska et al. (2021) Figure 3

Gilmore & Adolph (2017)

– “Databrary” (n.d.)

– “PLAY project” (n.d.)

– Gilmore, Raudies, & Jayaraman (2015)

Lesson learned

Documented plans & practices can be reused for new projects.

Be kind to your future (forgetful) self.

Good Enough Practices (Alaina)

Alaina Pearce

Automate

# These are bash shell commands
mkdir -p project/{admin,protocol,data}
mkdir -p project/admin/{financial,irb,meeting_documents,recruitment,research_assistants}
mkdir -p project/data/sub-control01/{anat,func,dwi,fmap}

Automate

Automate

  • Capture data in electronic form as much as possible
  • Let the computer do tedious, repetitive, error-prone work
    • e.g., data export & cleaning
    • Code as documentation

Automate

Alaina Pearce
  • Project-specific R packages
  • Using REDcap to capture project metadata

Automate

Rick Gilmore
  • Executable Quarto documents
    • Bootcamp 2025 site
    • Bootcamp 2025 Workshops I and II
    • Open Scholarship Survey

Common themes

  • Raw data live in secure place
  • Use API calls to gather, clean, process

(Painful) lesson learned

APIs can change, so be prepared to update your procedures & code.

Where

Big picture

  • Privacy & permission
  • Type(s) of data
  • Repositories
  • Data papers

Privacy & permission

  • Ask your participants
  • Broad consent vs. narrow
  • Store sharing permission & consider sharing it
  • Pseudo-anonymizing
    • May or may not be required

Type(s) of data

  • Imaging data
  • Non-identifiable
  • Identifiable

Imaging data

https://openneuro.org

Imaging data

Non-identifiable data

https://scholarsphere.psu.edu

Non-identifable data

https://osf.io

Identifiable data

  • Especially video & audio
  • Can be shared unaltered

Giphy

Sharing identifiable data

  • Databrary
  • Among institutionally authorized researchers
  • With explicit participant permission

Sharing identifiable data

https://www.icpsr.umich.edu

Learn more

Wrap-up

Take-homes

  • Why
    • Sharing data maximizes benefits to participants, accelerates discovery
    • Sharing data required by funders

Take-homes

  • How
    • Plan your work, work your plan
    • Automate
    • Make documentation a practice

Take-homes

  • Where
    • Imaging data
    • Identifiable vs. non-identifiable

Resources

About

This talk was produced using Quarto, using the RStudio Integrated Development Environment (IDE), version 2025.9.2.418.

The source files are in R and R Markdown, then rendered to HTML using the revealJS framework. The HTML slides are hosted in a GitHub repo and served by GitHub pages: https://penn-state-open-science.github.io/sleic-best-practices-2025-12-01/

References

Brakewood, B., & Poldrack, R. A. (2013). The ethics of secondary data analysis: Considering the application of belmont principles to the sharing of neuroimaging data. NeuroImage, 82, 671–676. https://doi.org/10.1016/j.neuroimage.2013.02.040
Data management & sharing policy overview. (n.d.). Retrieved November 12, 2025, from https://grants.nih.gov/policy-and-compliance/policy-topics/sharing-policies/dms/policy-overview
Databrary: Obtaining sharing release. (n.d.). Retrieved November 12, 2025, from https://nyu.databrary.org/support/irb/examples
Gilmore, R. O., & Adolph, K. E. (2017). Video can make behavioural science more reproducible. Nature Human Behaviour, 1, 0128. https://doi.org/10.1038/s41562-017-0128
Gilmore, R. O., Raudies, F., & Jayaraman, S. (2015). What accounts for developmental shifts in optic flow sensitivity? In 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (pp. 19–25). Providence, RI, USA: IEEE. https://doi.org/10.1109/devlrn.2015.7345450
Houtkoop, B. L., Chambers, C., Macleod, M., Bishop, D. V. M., Nichols, T. E., & Wagenmakers, E.-J. (2018). Data sharing in psychology: A survey on barriers and preconditions. Advances in Methods and Practices in Psychological Science, 1, 70–85. https://doi.org/10.1177/2515245917751886
National Institutes of Health. (n.d.). NOT-OD-21-013: Final NIH policy for data management and sharing. Retrieved October 24, 2021, from https://grants.nih.gov/grants/guide/notice-files/NOT-OD-21-013.html
NYU Health Sciences Library. (2013). Data sharing and management snafu in 3 short acts (higher quality). Youtube. Retrieved from https://www.youtube.com/watch?v=66oNv_DJuPc
PathfindersPerform. (2013). Pay me now or pay me later; pay me you will! Youtube. Retrieved from https://www.youtube.com/watch?v=Ij1yDpfZI8Q
PLAY project: Collection overview. (n.d.). Retrieved November 12, 2025, from https://play-project.org/collection.html
Preparing your data management and sharing plan. (n.d.). Retrieved November 12, 2025, from https://www.nsf.gov/funding/data-management-plan
Research data management. (n.d.). Retrieved November 12, 2025, from https://policy.psu.edu/policies/rp15
Soska, K. C., Xu, M., Gonzalez, S. L., Herzberg, O., Tamis-LeMonda, C. S., Gilmore, R. O., & Adolph, K. E. (2021). (Hyper)active data curation: A video case study from behavioral science. Journal of Escience Librarianship, 10. https://doi.org/10.7191/jeslib.2021.1208
Tenopir, C., Rice, N. M., Allard, S., Baird, L., Borycz, J., Christian, L., … Sandusky, R. J. (2020). Data sharing, management, use, and reuse: Practices and perceptions of scientists worldwide. PloS One, 15, e0229003. https://doi.org/10.1371/journal.pone.0229003