Lesson 2: Modern Data Management for Computational Research¶

Lesson Overview

Duration: 50 minutes

Structure:

Introduction (5 min)
Core Concepts (25 min)
Hands-on Activity (15 min)
Wrap-up (5 min)

Learning Objectives¶

After completing this lesson, you will be able to:

Recognize data as the foundation of open science
Describe the complete "life cycle of data"
Apply FAIR principles to your research data
Understand CARE principles for sensitive data
Use self-assessments to evaluate your data management practices
Identify tools and resources to improve data management
Create a basic data management plan
Choose appropriate licenses for your data

Introduction (5 minutes)¶

The Hidden Crisis in Research¶

Critical Questions

If you gave your data to a colleague unfamiliar with your project, could they make sense of it?
If you returned to your own data in five years, would you understand it?
When publishing, can you easily find all correct versions of your data?

DUST Research Scenario

Imagine a collaborator asks you to share:

Arsenic concentration measurements from 50 mine tailings samples
Lung tissue images from an inhalation exposure study
Plant biomass and metal uptake data from phytoremediation field plots
GPS coordinates and soil characterization from multiple Superfund sites

Could they understand your file naming conventions? Would they know which samples came from which sites? Would they understand the units, detection limits, and quality control procedures? Environmental health research generates complex, multi-dimensional datasets requiring exceptional organization.

The Biggest Challenge¶

The #1 Data Management Problem

Making it an afterthought.

Poor data management has no upfront cost. You can do substantial work before realizing you are in trouble. By then, fixing the problem is exponentially harder.

The solution? Make data management the first thing you consider when starting research.

Why Data Management Matters¶

Well-managed datasets:

Make life much easier for you and collaborators
Enable others to reuse and build upon your work
Are increasingly required by funders and journals
Protect against data loss and irreproducibility
Save time and prevent costly errors

NSF is Getting Stricter

Recent NSF policies require:

2-page Data Management Plan (DMP) in all proposals
Persistent identifiers for data
Machine-readable DMPs
Privacy, IP, and reuse policies
Review of DMP as part of proposal merit

NIH Superfund Program Requirements

NIH Superfund Research Program grants require:

Data Management and Sharing Plans (DMSP) describing data types, standards, preservation, and timelines
Data sharing within specified timeframes (often 1 year after collection or upon publication)
Consideration of human subjects protections for community health data
Documentation of sensitive data restrictions (location data for contaminated sites, participant privacy)
Use of appropriate repositories (EPA ScienceHub, NIEHS repositories, or domain-specific archives)

Core Concepts (25 minutes)¶

What Qualifies as Data?¶

Different data types require different management strategies:

Data Types:

Text - Field notes, survey responses, interview transcripts
Numeric - Tables, measurements, counts, statistics
Audiovisual - Images, videos, sound recordings
Models & Code - Simulations, algorithms, analysis scripts
Discipline-specific - FASTA (biology), FITS (astronomy), CIF (chemistry)
Instrument-specific - Raw equipment outputs, sensor readings

DUST Data Types

Environmental Chemistry - ICP-MS outputs (arsenic/metalloid concentrations), XRD patterns, synchrotron XAS spectra

Toxicology - Flow cytometry data, histopathology images, gene expression arrays, biomarker measurements

Phytoremediation - Plant biomass measurements, metal uptake data, hyperspectral imaging, LiDAR point clouds

Epidemiology - Survey responses (with PII protections), biomarker data (HIPAA-compliant), geospatial health data

Field Sampling - GPS coordinates, soil cores, air quality measurements, meteorological data

Data Sources:

ObservationalExperimentalSimulationDerived

Captured in real-time, typically outside lab
Usually irreplaceable - most important to safeguard
Examples: Sensor readings, telescope observations, field surveys

Generated under controlled conditions
Often reproducible but expensive/time-consuming
Examples: Lab measurements, controlled trials, sequencing

Machine-generated from computational models
Reproducible if model and inputs preserved
Examples: Climate projections, molecular dynamics

Generated from existing datasets
Reproducible but potentially expensive
Examples: Meta-analyses, compiled databases, data mining results

The Data Life Cycle¶

Understanding where data are in their lifecycle helps plan management strategies:

Plan¶

Describe data to be collected
Plan for organization before collection
Consider all lifecycle stages
Create Data Management Plan

Planning Questions

What data will you generate or reuse?
What file formats will you use?
How will you organize and document data?
Where will data be stored and backed up?
How will you ensure data quality?
Who will have access and when?
How long must data be preserved?

Collect¶

Implement organizational system before collecting
Capture observation metadata simultaneously
Take advantage of automatic metadata generation
Use consistent naming conventions
Document collection conditions

File Naming Best Practices

# Good Examples
2025-01-15_site-A_temp-sensor-01_raw.csv
experiment-03_rep-02_treated_microscopy.tif
survey_2025_wave-01_demographics.xlsx

# Bad Examples
data.csv
final_FINAL_v3_revised_2.xlsx
New Folder (2)/results!!!.csv

DUST File Naming Examples

# Mine Tailings Samples
2025-01-20_IronKing_MT-001_As-concentration_ICP-MS.csv
2025-01-20_IronKing_MT-002_XRD-pattern_raw.xy

# Lung Tissue Images
exp-027_mouse-14_lung-section-03_H&E-stain_40x.tif
exp-027_mouse-14_lung-section-03_collagen-IF_20x.tif

# Phytoremediation Field Data
2025-03-15_site-B_plot-12_Atriplex-canescens_biomass.csv
2025-03-15_site-B_hyperspectral_processed_NDVI.tif

# Keep consistent: YYYY-MM-DD_site_sample-ID_analysis-type_details.ext

Assure¶

Record quality conditions during collection
Distinguish estimated from measured values
Double-check manually entered data
Run statistical summaries to find outliers
Flag questionable or missing values
Perform validation checks

Quality Assurance Checklist:

Define acceptable ranges for measurements
Implement automated validation scripts
Document calibration procedures
Track instrument performance over time
Create visualizations to spot anomalies
Maintain audit trail of quality checks

Describe¶

Metadata is Key

"Without thorough description of context, collection methods, measurements, and quality, data are unlikely to be discovered, understood, or effectively used."

Essential Metadata:

Dataset information - Title, dates, version, related datasets
People - Authors, affiliations, sponsors, ORCID IDs
Scientific context - Research question, hypotheses, methods
Data details - Variables, units, formats, missing value codes
Quality - Precision, accuracy, uncertainty, QA procedures
Access - License, restrictions, citation instructions

Metadata Standards:

DataCite - Publishing data
Dublin Core - Web-based sharing
ISO 19115 - Geospatial data
MIxS - Environmental samples (soil, water)
Darwin Core - Biodiversity/ecological data
Domain-specific standards - Check FAIRsharing.org

DUST Metadata Needs

Mine Tailings Samples - Site GPS coordinates, collection date/time, depth, weather conditions, proximity to mining activity, historical context, chain of custody

Toxicology Experiments - Animal strain/source, exposure protocol (concentration, duration, route), housing conditions, institutional approvals (IACUC), treatment randomization

Chemical Analysis - Instrument make/model, calibration standards, detection limits, QA/QC procedures, analyst ID, date of analysis, method references

Field Studies - Plot layout, vegetation surveys, soil characterization, meteorological data, disturbance history, GPS accuracy

Preserve¶

Not Just Backup - Preservation

Preservation means ensuring data remain accessible and usable long-term, not just keeping copies on a hard drive.

Preservation Repositories:

Domain-specific - GenBank (sequences), PANGAEA (Earth science)
Environmental/Health - EPA ScienceHub, NIEHS data repositories
Institutional - University libraries, research centers
General purpose - Zenodo, Dryad, Figshare
CyVerse - Data Commons for computational biology
Geospatial - USGS ScienceBase, DataONE

DUST Repository Choices

Toxicology Data - Consider NIEHS Data Portal or NIH-supported repositories (dbGaP for human subjects, figshare for animal studies)

Environmental Chemistry - EPA ScienceHub for data relevant to contaminated sites, or Zenodo with appropriate environmental keywords

Geospatial Data - DataONE or USGS ScienceBase for mine site characterization and remediation monitoring data

Spectroscopy Data - Zenodo allows large files and assigns DOIs, perfect for synchrotron XAS or hyperspectral datasets

Preservation Best Practices:

Choose repositories with TRUST principles
Use open, non-proprietary formats when possible
Include comprehensive documentation
Assign persistent identifiers (DOIs)
Apply appropriate licenses
Consider embargo periods if needed

Discover¶

Good metadata enables discovery by you and others:

Repository search interfaces
Google Dataset Search
DataOne
Discipline-specific portals
Repository aggregators

Integrate¶

Data integration requires careful work
Standards and ontologies are crucial
Know the data before integrating
Never assume column headers mean the same thing
Always cite data you reuse
Use DOIs for citations

Analyze¶

Follow reproducible practices
Record all software, versions, parameters
Use computational notebooks (Jupyter, R Markdown)
Version control analysis code
Pre-register analysis plans when possible
Document decision points

FAIR Principles¶

In 2016, the FAIR Guiding Principles revolutionized how we think about data management.

Why Principles, Not Rules?

FAIR is intentionally a set of principles, not rigid rules. Different disciplines must interpret and implement these principles appropriate to their contexts and technologies.

F - Findable¶

(Meta)data assigned globally unique persistent identifier
Data described with rich metadata
Metadata includes identifier of described data
(Meta)data registered in searchable resource

Practical Implementation:

Use DOIs for datasets
Create comprehensive README files
Register with domain repositories
Use descriptive, searchable keywords

A - Accessible¶

(Meta)data retrievable via standardized protocol
Protocol is open, free, universally implementable
Protocol allows authentication when necessary
Metadata accessible even when data unavailable

Practical Implementation:

Store in repositories with standard access protocols (HTTP, FTP)
Provide clear access instructions
Maintain metadata permanently
Document access restrictions clearly

I - Interoperable¶

(Meta)data use formal, shared, broad language
(Meta)data use vocabularies following FAIR principles
(Meta)data include qualified references to other data

Practical Implementation:

Use standard file formats (CSV, NetCDF, GeoTIFF)
Apply community ontologies
Link related datasets
Document relationships between datasets

R - Reusable¶

(Meta)data richly described with accurate attributes
Released with clear, accessible usage license
Associated with detailed provenance
Meet domain-relevant community standards

Practical Implementation:

Include comprehensive documentation
Apply recognized license (CC-BY, CC0)
Document data collection and processing
Follow discipline-specific standards

FAIR ≠ Open

FAIR does not require data be open. Data can be FAIR but restricted:

Human subjects data may be FAIR but require access approval
Endangered species locations should be findable in metadata but not accessible
Indigenous data may be FAIR to community members but restricted to others

CARE Principles¶

Nothing About Us Without Us

The CARE Principles for Indigenous Data Governance ensure Indigenous Peoples' rights and interests in data are respected. CARE complements FAIR by centering Indigenous rights and interests in data governance.

C - Collective Benefit

Data for inclusive development and innovation
Data for improved governance and citizen engagement
Data for equitable outcomes

A - Authority to Control

Recognize Indigenous rights and interests
Empower data for governance
Support governance of data

R - Responsibility

Foster positive relationships
Expand capability and capacity
Support Indigenous languages and worldviews

E - Ethics

Minimize harm and maximize benefit
Promote justice
Consider future use

Applying CARE in Practice

When working with data about Indigenous peoples, traditional knowledge, or Indigenous lands:

Engage with communities early
Establish data sovereignty agreements
Respect cultural protocols
Share benefits equitably
Support community capacity building

Data Management Plans¶

Failure to Plan is Planning to Fail

A Data Management Plan (DMP) is a formal document outlining how data will be handled during and after a research project.

Why Create a DMP?

Stick: You have to - funders require them

Carrot: They make your life easier

Clarify your thinking before starting
Anticipate and avoid problems
Budget appropriately
Enable collaboration
Facilitate data sharing and preservation

Essential DMP Components:

Data Description
Types, volumes, formats
Existing vs. new data
Relationship to other data
Metadata & Documentation
Standards to be used
Tools for documentation
Completeness of documentation
Storage & Backup
Short-term storage during project
Backup frequency and methods
Data security measures
Access & Sharing
Who can access data and when
How others can access data
Restrictions on sharing
Preservation
Where data will be deposited
How long data will be preserved
Costs and responsibilities
Ethics & Compliance
Privacy considerations
Intellectual property issues
Ethical approvals needed

DMP Tools

DMPTool - Create plans using funder templates

Data Stewardship Wizard - Knowledge-based DMP creation

Both tools provide guidance, templates, and examples to help you write effective DMPs.

Choosing a License¶

By default, creative work is under exclusive copyright. To enable reuse, you must license your work.

Common Data Licenses:

CC0 Public DomainCC-BY AttributionCC-BY-SA Share-Alike

Complete surrender of copyright
Data freely usable without attribution
Most open option
Recommended for maximum reuse

Requires attribution to creator
Allows any use with credit
Balances openness with recognition
Most common for research data

Requires attribution
Derivative works must use same license
Ensures openness propagates
Less commonly used for data

Non-Commercial Restrictions

Avoid "NC" (non-commercial) licenses for research data:

Ambiguous definition of "commercial"
Restricts institutional and infrastructure use
Prevents integration with other datasets
Limits reproducibility

Choosing a License:

Check funder requirements
Consider community norms
More open = more reuse
Document license clearly in repository
Include LICENSE file with data

Resources:

Hands-on Activity (15 minutes)¶

Data Management Self-Assessment¶

Evaluate your current practices across multiple dimensions:

Assessment: The Three Vs

Volume - Size and quantity of data

I know the total size of my active research data
I have enough storage for my data
I have a plan for when data exceed current storage
I've budgeted for data storage costs

Velocity - Speed of data generation/analysis

I can keep up with data processing
I have automated workflows for routine tasks
Data are processed in reasonable timeframes
Backlogs are manageable

Variety - Diversity of data types

I use standard file formats when possible
Different data types are organized logically
I have appropriate tools for each data type
Data can be integrated when needed

Assessment: FAIR Principles

Findable

Data have unique identifiers
Metadata are comprehensive
Data are registered in searchable repositories
Identifiers are persistent (DOIs)

Accessible

Data are stored in reliable locations
Access methods are documented
Authentication is appropriate
Metadata will persist long-term

Interoperable

Standard formats are used
Community vocabularies are applied
Related datasets are linked
Data work with analysis tools

Reusable

Clear license is applied
Provenance is documented
Quality is described
Usage guidelines are provided

Group Exercise: DMP Scenario¶

Work in small groups on this scenario:

Scenario: Mine Waste Phytoremediation Study

You are planning a 4-year NIH Superfund-funded study investigating arsenic uptake by native plant species at abandoned mine sites in Arizona.

Data to be collected:

Soil samples (100+ samples/year): ICP-MS for As/metal concentrations
Plant tissue samples: Biomass, metal uptake, tissue distribution
Hyperspectral drone imagery (quarterly): Plant stress detection
Weather station data (15-min intervals): Temperature, humidity, precipitation
GPS/GIS data: Site characterization, vegetation mapping
Laboratory notebooks: Experimental procedures, observations
Microscopy images: Root structure, fungal associations

Project involves:

3 mine sites (some on/near Tribal lands)
4 institutions (UA, ASU, external lab, Tribal consultation)
10 team members (PIs, grad students, technicians, community partners)
NIH Superfund funding requiring DMSP
Data must be public 1 year after publication
Some location data may need restriction to prevent looting of remediation plants

Your Task: Draft key sections of the DMP addressing:

Data types and volumes - Estimate sizes, formats (ICP-MS output, drone imagery, etc.)
Metadata - What standards apply? (MIxS for soil samples, Darwin Core for plants, ISO 19115 for spatial data)
Storage - During project, where will data live? (Institutional storage, field backups)
Quality - How ensure accuracy? (Calibration standards, duplicate samples, QA/QC protocols)
Sharing - Timeline, repository (EPA ScienceHub? Zenodo?), restrictions for sensitive locations
Roles - Who is responsible for what? (Data manager, PI oversight, institutional compliance)
Ethical considerations - Tribal consultation, community benefit, location data sensitivity

Discussion Points:

How do you balance open data with protecting sensitive site locations?
What metadata is critical for someone to reuse your phytoremediation data?
How do you handle data from external labs with different formats?
What are the implications of working near/on Tribal lands?

Individual Action Planning¶

Choose one improvement to implement this week:

Possible Actions

Create README template for my lab
Set up automated backups
Register for ORCID and start using it
Reorganize one project's file structure
Document one dataset with comprehensive metadata
Choose and apply license to existing dataset
Create data dictionary for current project
Set up version control for analysis code

Wrap-up (5 minutes)¶

Key Takeaways¶

Remember These Concepts

Plan early - Data management starts before data collection
FAIR principles provide a framework but require interpretation
CARE principles emphasize ethics and Indigenous data sovereignty
Metadata matters - Future you (and others) need excellent documentation
Tools exist - DMPTool, repositories, standards can help
Start small - One improvement at a time compounds over time

Self-Assessment Quiz¶

What is the biggest challenge in data management?

Making it an afterthought

Data management problems are not immediately obvious. You can collect substantial data before realizing organization, documentation, or backup is inadequate. By then, fixing problems is exponentially harder. Make data management the first consideration in any project.

True or False: FAIR and CARE principles are the same

False

FAIR focuses on making data findable, accessible, interoperable, and reusable - primarily technical concerns. CARE addresses Indigenous data governance, emphasizing collective benefit, authority to control, responsibility, and ethics. Both are important but address different aspects of data stewardship.

True or False: Data available upon request meets Open Data standards

False

Open data must be freely accessible in public repositories without requiring individual requests. "Available upon request" creates barriers, does not ensure data persist long-term, and does not meet FAIR findability or accessibility principles.

Your project needs a license allowing others to use your work with attribution. Which do you choose?

CC-BY (Creative Commons Attribution)

CC-BY allows anyone to use, modify, and distribute your data as long as they provide appropriate attribution. This balances openness (maximizing reuse) with recognition (ensuring credit to creators). It is the most common license for research data.

Looking Ahead¶

In Lesson 3, we will address ethical considerations in modern research by exploring:

Bias and discrimination in AI systems
Responsible use of AI tools in research
Transparency and accountability
Best practices for ethical AI integration

Additional Resources¶

Previous: ← Lesson 1: Open Science | Next: Lesson 3: AI Ethics →

Last updated: 2025-10-30