This deliverable is not counted toward your grade
Project Start and Technical Implementation (site, data and investigation) pages are required practice but are not counted toward your final grade. Your engineering grade comes from Chapter 4 and Chapter 5.
Data Management
Students design a data management plan establishing file structure, version control, and backup procedures for the entire investigation dataset.
Section progress
0% of the workflow complete
Field & Laboratory · Plan, execute and document field and laboratory data collection to a defensible quality standard.
Deliverable: Data management plan document with file structure and backup policy.
How to complete this section
Do this next: Read the Data Management lecture and the worked example so you know what "Data management plan document with file structure and backup policy." has to contain.
Not sure how to start or how much depth is expected? Read the fully written model example for this deliverable first — it shows the structure, tables and level of justification your advisor grades against.
Site Investigation Lab — what this workspace teaches
Plan, execute and document field and laboratory data collection to a defensible quality standard.
- Planning a subsurface, structural or traffic field investigation
- Instrumentation selection, resolution, accuracy and calibration records
- GPS/GNSS positioning: datums, projections, RTK vs. handheld accuracy
- GIS data capture, attribute schemas and coordinate metadata
- Land surveying: traverses, levelling, closure and error adjustment
- Sampling strategy: representative sampling, spacing, depth intervals, replicates
- ASTM/AASHTO laboratory testing procedures and reporting requirements
- Chain of custody, sample labelling and preservation
- QA/QC: duplicates, blanks, repeatability and data validation rules
End-of-term milestones
- Tuesday, November 17, 2026 — Poster printed and ready. 36 in × 48 in poster finalized and printed one week before the November 24 showcase.
- Wednesday, November 18, 2026 — Final document package uploaded for scoring. Chapters 4–5, calculation package, drawings and appendices uploaded in the app for advisor scoring.
- Wednesday, November 18, 2026 — Poster presentation to faculty and industry. Wednesday poster session — printed 36 in × 48 in poster presented in person; industry reviewers score communication and impact.
- Wednesday, November 25, 2026 — Oral presentation and defense (scored). Scored oral presentation and defense held on Wednesday, November 25.
Data Management
Students design a data management plan establishing file structure, version control, and backup procedures for the entire investigation dataset.
Section B
Engineering story
A real project situation that frames this module
A engineering computation and digital delivery team hits data management in week 3, with the model, dataset and documented computational workflow already promised to the owner. Students design a data management plan establishing file structure, version control, and backup procedures for the entire investigation dataset. The reviewer starts at the end and works backwards, and the chain breaks at project file structure and naming conventions for cross-discipline retrieval.
Storing the only copy of raw field data on a single field laptop. Because version control practices for datasets that are revised as new field data arrives, the error does not stay local: it is carried into the data foundation every later calculation silently depends on, and every downstream product inherits it before anyone notices.
Every downstream discipline that inherits the model and the engineer who seals it carry the consequence. On this module specifically, the exposure runs through backup and redundancy requirements (3-2-1 rule) for irreplaceable field data, and the cost of correction rises every week the model, dataset and documented computational workflow moves closer to issue.
Decisions the engineer must make
- What record establishes project file structure and naming conventions for cross-discipline retrieval, and is that record in the project data inventory?
- Does ISO 19115 (2014), Sec. 6, govern here — and is that the edition adopted by the jurisdiction?
- What is the acceptance criterion for version control practices for datasets that are revised as new field data arrives, and was it written before the result was known?
- Is the documented procedure valid for the conditions this project actually presents?
- If the check fails, does the team revise the model, dataset and documented computational workflow or raise a change request against the locked baseline?
Photo 1. Field review: the conversation in which a scope, a constraint or a decision is actually settled.
Capstone Studio instructional photograph
Section C
Why this matters
Professional
A licensed engineer defending data management cites ISO 19115 (2014), Sec. 6, and shows the record behind each input. Your data management plan document with file structure and backup policy. is reviewed the same way — traceability is assessed before arithmetic.
Technical
Project file structure and naming conventions for cross-discipline retrieval controls the numbers this module hands forward. Version control practices for datasets that are revised as new field data arrives determines whether those numbers remain valid once conditions change.
Safety
The failure mode this module guards against is an unverified model output accepted as an engineering result. It reaches people through metadata standards enabling data reuse by other disciplines without re-contacting the originator, which is why the safety check is recorded explicitly here rather than inferred from a passing strength or performance check.
Economic
The data foundation every later calculation silently depends on is priced from this work. Quantities, unit costs and schedule float all trace to project file structure and naming conventions for cross-discipline retrieval; a late correction here is paid for as a change order, not a redline.
Environmental
Environmentally, this module fixes decisions on quantity and material that the model silently drives. Choosing conservatively without justification is not free — the excess shows up as material, energy and land that the project consumes for no measurable gain.
Community
The public that depends on results no one outside the modelling team can reproduce inherit whatever this module decides — performance, accessibility, cost of ownership and resilience are set here, not at the ribbon-cutting.
Section D
Learning objectives
By the end of this module you will be able to:
- 1.Explain project file structure and naming conventions for cross-discipline retrieval, using this project's own conditions rather than a textbook case.
- 2.Interpret version control practices for datasets that are revised as new field data arrives, using this project's own conditions rather than a textbook case.
- 3.Interpret backup and redundancy requirements (3-2-1 rule) for irreplaceable field data, using this project's own conditions rather than a textbook case.
- 4.Compare metadata standards enabling data reuse by other disciplines without re-contacting the originator, using this project's own conditions rather than a textbook case.
- 5.Apply ISO 19115 (2014), Sec. 6, and cite the section that governs your acceptance decision.
- 6.Produce data management plan document with file structure and backup policy. at a standard the independent model checker would accept without a second revision cycle.
Section E
Instructional content
Full lecture notes with figures and governing equations
Data Management: from proposal statement to engineering product
Students design a data management plan establishing file structure, version control, and backup procedures for the entire investigation dataset. That single sentence hides the substance of the module: project file structure and naming conventions for cross-discipline retrieval, and version control practices for datasets that are revised as new field data arrives. Both must be established from project evidence before anything downstream is credible.
In engineering computation and digital delivery, this work is the input to the model, dataset and documented computational workflow. Backup and redundancy requirements (3-2-1 rule) for irreplaceable field data — which is why this page asks you to record the source of every quantity, not just its value. The data foundation every later calculation silently depends on depends on it.
- Project file structure and naming conventions for cross-discipline retrieval
- Version control practices for datasets that are revised as new field data arrives
- Backup and redundancy requirements (3-2-1 rule) for irreplaceable field data
- Metadata standards enabling data reuse by other disciplines without re-contacting the originator
Photo 1. Data Management: from proposal statement to engineering product in practice — Field review: the conversation in which a scope, a constraint or a decision is actually settled.
Capstone Studio instructional photograph
Decision logic: the procedure that replaces a closed-form solution
Data Management is governed by a documented procedure rather than a single expression, so the decision logic is the deliverable: what you accept, what you reject, and on what evidence. Project file structure and naming conventions for cross-discipline retrieval.
Write the acceptance criterion before you look at the result. Version control practices for datasets that are revised as new field data arrives — recording the criterion afterwards lets it be shaped to fit the number you happened to get.
Photo 2. Decision logic: the procedure that replaces a closed-form solution in practice — Field review: the conversation in which a scope, a constraint or a decision is actually settled.
Capstone Studio instructional photograph
Constraints, adopted standards and the safety case for data management
ISO 19115 (2014), Sec. 6, governs this module: Geographic information metadata standard applicable to spatial data management NIST SP 800-53 (Rev. 5), Sec. AC/CM families, adds the second constraint: Access control and configuration management guidance applicable to project data security
The safety case is explicit here. The failure mode is an unverified model output accepted as an engineering result; the people exposed are every downstream discipline that inherits the model and the engineer who seals it; the control that prevents it is metadata standards enabling data reuse by other disciplines without re-contacting the originator together with an independent check by someone who did not perform the work.
- Controlling criterion for this module: project file structure and naming conventions for cross-discipline retrieval.
- Adopted reference: ISO 19115 (2014) — cite Sec. 6 by number.
- Failure mode guarded: an unverified model output accepted as an engineering result.
- Evidence produced: Data management plan document with file structure and backup policy..
Photo 3. Constraints, adopted standards and the safety case for data management in practice — Field review: the conversation in which a scope, a constraint or a decision is actually settled.
Capstone Studio instructional photograph
Where this method stops being valid
Every method has a domain of validity. State the range of geometry, loading, material behaviour or flow regime over which your approach holds, and state what you would do instead beyond it.
For this project, the boundary you are most likely to push is metadata standards enabling data reuse by other disciplines without re-contacting the originator. If you cross it, say so in writing, bound the error, and carry the limitation into your results chapter. A disclosed limitation is professional practice; a silent extrapolation is not.
Photo 4. Where this method stops being valid in practice — Compression test on a concrete cylinder: the measurement behind every f′c used in design.
Wikimedia Commons, public domain
Section F
Engineering workflow
Steps
- 1. Assemble the inputs this module needs — project file structure and naming conventions for cross-discipline retrieval; version control practices for datasets that are revised as new… — each with a unit and a source record.
- 2. Confirm ISO 19115 (2014) is the adopted edition and locate Sec. 6.
- 3. State the assumptions and the acceptance criterion for project file structure and naming conventions for cross-discipline retrieval.
- 4. Execute the documented procedure, recording each judgement and the evidence behind it.
- 5. Test the result against backup and redundancy requirements (3-2-1 rule) for irreplaceable field data.
- 6. Audit units and run an order-of-magnitude check by hand before the number leaves your desk.
- 7. Obtain an independent check from a teammate who did not perform the work, and record their name and date.
- 8. Assemble data management plan document with file structure and backup policy. and submit it to the independent model checker for review.
Decision points
- Is every input behind project file structure and naming conventions for cross-discipline retrieval traceable? If not — stop and collect the record.
- Does the result satisfy version control practices for datasets that are revised as new field data arrives? If not — revise the work, never the criterion.
- Would the correction change the data foundation every later calculation silently depends on? If yes — raise a change-control request before proceeding.
- Have you ruled out the most common error on this module — storing the only copy of raw field data on a single field laptop?
Quality checklist
- Documented: project file structure and naming conventions for cross-discipline retrieval
- Documented: version control practices for datasets that are revised as new field data…
- Documented: backup and redundancy requirements (3-2-1 rule) for irreplaceable field data
- ISO 19115 Sec. 6 cited by section number
- Procedure steps recorded in order with evidence
- Acceptance criterion recorded before the result
- Independent check signed and dated
- Data management plan document with file structure and backup policy. attached and named per the course convention
Section H
Interactive visualization
Data Management — step-through
Advance one frame at a time. Each frame adds one engineering decision to the previous state.
Step 1 of 6
Define the project file/folder naming convention.
Section I
Applicable codes and standards
ISO 19115
2014 · Sec. 6
Adopted design/analysis reference governing this module.
Relevance: Geographic information metadata standard applicable to spatial data management
Reference the section number and edition in your calculation package. Do not reproduce code text.
NIST SP 800-53
Rev. 5 · Sec. AC/CM families
Adopted design/analysis reference governing this module.
Relevance: Access control and configuration management guidance applicable to project data security
Reference the section number and edition in your calculation package. Do not reproduce code text.
Section J
Worked examples
Full engineering solution format
Section K
Common mistakes and how to avoid them
- Storing the only copy of raw field data on a single field laptop.
- Allowing designers to edit 'raw' data files directly instead of working from a QA-reviewed copy.
- Treating project file structure and naming conventions for cross-discipline retrieval as a given instead of establishing it from a project record.
- Producing data management plan document with file structure and backup policy. without showing how version control practices for datasets that are revised as new field data… was satisfied.
- Recording the outcome of this module without recording the judgement and evidence that produced it.
- Missing metadata standards enabling data reuse by other disciplines without re-contacting the originator, which is exactly the path to an unverified model output accepted as an engineering result.
- Collecting data before defining what decision the data has to support.
- Accepting a laboratory or field value without its method, date, operator and uncertainty.
- Citing the wrong edition of a standard, or citing a standard that does not govern the jurisdiction.
- Leaving boundary conditions undefined so the model is not reproducible by an independent checker.
- Using inputs that no field record, laboratory report, or published source supports.
Section L
Industry case study
Documented failure related to data management
A constructed civil works project where this module's decision was made incorrectly or skipped.
Official findings
- Published investigation identified a breakdown between analysis assumption and constructed condition.
Field observations
- The controlling assumption was documented nowhere in the design record.
- No independent check existed at the stage where the error entered the work.
Engineering interpretation
- Interpretation below is student analysis for instructional purposes, not an official finding.
- Map the failure to a step in your own workflow and state where your process would have caught it.
Lessons learned
- Document the assumption, then have someone else check it before it becomes construction.
Source: Summarize the published investigation; cite it in your reference list. Do not reproduce copyrighted report text.
Section M
FE Civil exam connection
Handbook FE Reference Handbook — engineering computation and digital delivery section (record the section number from your handbook edition).
Exam topics
Handbook formulas
Weak results here feed your FE Civil Academy weak-area queue for targeted practice.
Question 1 of 2
Score: 0/2In data management, which item must be established BEFORE the analysis is run?
Section N
Apply it to your project — Data Management
Complete this using your own capstone project data. Every field is saved to your project record and routed to your advisor with this module's submission.
Inputs and sources
Every value needs a traceable source.
| Quantity | Value | Unit | Source / record |
|---|
Assumptions and consequences
| Assumption | Basis | Consequence if wrong |
|---|
Self-check before submission
Section O
Design challenge
Consulting challenge — Data Management
Your firm has been retained to deliver the data management scope for a municipal client on a compressed schedule. Produce the technical position your firm would defend at a public meeting.
Client request: The client wants a defensible recommendation, the basis of design, and an honest statement of what remains unresolved.
Constraints
- Adopted local code edition governs; no exceptions without written variance.
- Budget and schedule are fixed; scope changes require change control.
- Public safety and accessibility requirements are non-negotiable.
Deliverables
- One-page basis of design
- Supporting calculation extract
- Risk and limitation statement
Evaluation
- Technical correctness
- Standard compliance
- Clarity of engineering judgment
- Honest treatment of uncertainty
Section P
Documentation workspace
Write the report section for this module in the academic editor
Section Q
File uploads
Accepted: PDF, DOCX, XLSX, CSV, PNG, JPG, ZIP
No files uploaded yet.
Section R
Deliverable and advisor review
Data management plan document with file structure and backup policy.
Submissions route to your assigned faculty advisor and are scored independently by faculty and administrator rubrics.
Reflection
What was the hardest engineering judgment in this module, and how did you resolve it?
Section S
ABET outcome mapping
Data management plan document with file structure and backup policy. with advisor review and dual scoring.
Assessment: Faculty rubric score and administrator rubric score on this module's submission.
Rubric: Data quality · Target: 70% of students at or above 'meets expectations'.
Section T
References and further study
ISO 19115 (2014)
Adopted reference — cite section numbers, do not reproduce text.
NIST SP 800-53 (Rev. 5)
Adopted reference — cite section numbers, do not reproduce text.
Data Management — instructor design procedure
Course template for the calculation package format expected in the final report appendix.
NCEES FE Reference Handbook
Locate the equations used here and note the handbook section for exam recall.
Advisor meeting agenda item
Bring the unresolved decision from this module to your next weekly advisor meeting.