Fulfilling Statistical Policies with 

Data Curation Practices

https://doi.org/10.21949/1527466

Leighton L Christiansen 

http://orcid.org/0000-0002-0543-4268

Data Curator, National Transportation Library,

Bureau of Transportation Statistics, 

OST-R , US Department of Transportation

leighton.christiansen@dot.gov

ntldatacurator@dot.gov

Jesse Long 

https://orcid.org/0000-0002-4962-1380

Data Curation & Data Management Fellow, 

National Transportation Library,

Bureau of Transportation Statistics, 

OST-R , US Department of Transportation

jesse.long.ctr@dot.gov

Presentation to the Federal Committee on Statistical Methodology 2022 Research and Policy Conference 2022-10-25 1
Overview

● About Us

● Statistical Laws & Practices

● About Data Curation

● Data Curation for Transparent Statistics: Suggestions

● Conclusions

● Questions

● Supplemental Slides

2
About Us

Leighton:

● MLIS, CAS Data Curation (UIUC) 2012

● Library Director and Data Governance 

Committee (Iowa DOT) 2012 – 2016

● NTL Data Curator, May 2016

○ Public Access Implementation Lead

○ BTS Data Curation

○ DOT representative to White House 

OSTP Subcommittee on Open Science

Jesse:

● MLIS (Syracuse), 2019

● NTL Data Management and Data 

Curation Fellow, June 2019

○ Preservation of Legacy BTS data

○ NTL lead on Persistent Identifiers in 

federal consortia and working groups

○ Research Data Management training

3
Statistical Laws & Practices 

● Safeguard the confidentiality of individually identifiable information 

acquired under a pledge of confidentiality for statistical purposes;

● Statistical agencies should continuously seek to improve their efficiency;

● More sharing of data among designated statistical agencies;

● Increase access to data for evidence

https://doi.org/10.17226/26360

“...envision a future where...”

● greater care in the documentation of methods, the use of uniform processes 

for archiving of input data and all official statistics, and the greater use of 

metadata standards.

● archived and documented materials will be retained in permanent Web 

locations and code will be fully commented.... 

● Identical machine-readable metadata standards will be used by all statistical 

programs, which will make sharing of methods and data easier among the 

statistical community

4
About Data Curation Actions

●

●

●

●

●

Proactive

Creation & Collection

● Standard Workflows: File Naming

● Data Management & Training: DMPs

● Robust Documentation: Readme & Codes

● Controlled Vocabularies: Data Dictionaries

● Metadata Standards: Choose & Publicize 

● Persistent Identification: DOI, ORCID, ROR

● Preservation Planning: Repository & Backups 5
Benefits of Data Curation

● Protects Unique Data from 

Loss

● Improves Data Search & 

Retrieval 

● Enables Reuse

● Facilitates Longitudinal 

and/or Meta Analyses

● Avoids Duplication of 

Effort & Spending

● Increases Verifiability

● Opens New Lines of 

Scientific Discovery

● Satisfies Public Access & 

Open Government & Legal 

Requirements

6
Data Curation: Definitions

● Data Management: 

○ deliberate planning, creation, storage, access and preservation of data 

produced from a given investigation1, 2

● Data Curation

○ enables data discovery and retrieval, maintains data quality, adds value, and 

provides for re-use over time3

● Data Science

○ drawing useful conclusions from large and diverse data sets through 

exploration, prediction, and inference4

7
Linked 

Processes

DM is Necessary

element of DC

Data 

Management ∈

Data 

Curation

DC Enables DS Data 

Curation ⇒ Data 

Science

8
Data Curation Dependencies Model

Data Management ∈ Data Curation ⇒ Data Science

DM ∈ DC ⇒ DS

9
Data Curation & the Data Lifecycle

● Data Curation

○ Enables data discovery and retrieval, maintains data 

quality, adds value, and provides for re-use over time3

● Data Lifecycle

○ All the phase of data’s existence from planning to collection, 

through preservation, to reuse and potential destruction

10
USGS Data 

Lifecyle Model

6

● Plan FIRST!! ● Collect second ● Curation steps 

throughout 

11
Data Curation for Transparent Statistics: 

Three Main Suggestions

Data 

Management 

&

Sharing

Plans

Plan for 

FAIR & to 

Share

Embed 

Data 

Curators & 

Curation 

Practices

12
Suggestion 1:

Data Management [& Sharing] Plans

● Explicit documentation 

of knowledge

○ Sets project standards

○ Plan for data capture

○ Links to policies

● Living document: 

review and update

Potential DMP Sections

● Project Title and Information

● Data Description

● Roles & Responsibilities

● Standards Used

● Access Policies

● Sensitive Data Policies

● Sharing Policies

● Archiving and Preservation Plans

● Applicable laws and policies

13
Suggestion 2:

Plan for FAIR7

and to Share

Findable

Accessible

Interoperable

Reusable

https://www.force11.org/group/fairgroup/fairprinciples

Sharing Data

● Last step of USGS Data Lifecycle: 

Publish/Share

● Sharing: Culture Change that affects decisions

● Encourages new discovery & efficiencies

● Consistent with developing U.S. policy and law

14
FAIR

Challenge

JISC Report: 

FAIR in Practice8

Tools are needed, 

remain elusive

While there is 

“[s]trong support 

for growing the 

body of tools and 

resources 

available that 

reduced the 

burden of data 

management,” 

there is also a “

[l]ack of good 

tooling to 

support 

metadata 

capture at data 

generation.”

http://doi.org/10.5281/zenodo.1245568 15
Suggestion 3:

Embed Data Curators & Curation Practices

● Necessary skills other 

team members may not 

possess

● Fresh eyes for workflows 

and implicit knowledge

● Assume preservation and 

sharing

● Improve team efficiency 

around sharing and 

preservation

● Lifecycle view of data

● End of lifecycle planning

16
Conclusions & Suggestions Review

• Data curation enables data science

• Data Curation lifecycle view defaults to transparency

• Data management and sharing planning is THE first step

• FAIR data principles apply to metadata, data, and paradata

• Plan for sharing; create a sharing culture

• Embed data curators and curation practices into projects from 

the start for best results and most transparent statistics

17
References 1

1. University Library, Texas A&M University. “Data Management Defined - Research Data Management - Guides at Texas A&M University.” Research Data 

Management, October 1, 2013. http://guides.library.tamu.edu/DataManagement

2. Briney, Kristin. 2015. Data management for researchers: organize, maintain and share your data for research success. http://www.pelagicpublishing.com/datamanagement-for-researchers.html

3. Graduate School of Library and Information Science at the University of Illinois at Urbana-Champaign. “Specialization in Data Curation,” 2013. 

http://www.lis.illinois.edu/academics/programs/specializations/data_curation

4. Definition based on Ani Adhikari and John DeNero, “The Foundations of Data Science” http://www.inferentialthinking.com/index.html “What is Data 

Science” http://www.inferentialthinking.com/chapter1/what-is-data-science.html

5. Digital Curation Centre. Data Curation Lifecycle Model. http://www.dcc.ac.uk/resources/curation-lifecycle-model

6. Faundeen, J.L., Burley, T.E., Carlino, J.A., Govoni, D.L., Henkel, H.S., Holl, S.L., Hutchison, V.B., Martín, Elizabeth, Montgomery, E.T., Ladino, C.C., 

Tessler, Steven, and Zolly, L.S., 2013, The United States Geological Survey Science Data Lifecycle Model: U.S. Geological Survey Open-File Report 2013–

1265, 4 p., http://dx.doi.org/10.3133/ofr20131265

7. FORCE11. “The FAIR Data Principles.” 2016. https://www.force11.org/group/fairgroup/fairprinciples

8. Allen, Robert, & Hartland, David. (2018, May 21). FAIR in practice - Jisc report on the Findable Accessible Interoperable and Reuseable Data Principles 

(Version 1). Zenodo. http://doi.org/10.5281/zenodo.1245568

18
References 2

9. United States. Congress. “H.R.4174 - 115th Congress (2017-2018): Foundations for Evidence-Based Policymaking Act of 2018.” January 14, 2019. 

https://www.congress.gov/bill/115th-congress/house-bill/4174

10. National Academies of Sciences, Engineering, and Medicine. 2022. “Transparency in Statistical Information for the National Center for Science and 

Engineering Statistics and All Federal Statistical Agencies.” Washington, DC: The National Academies Press. https://doi.org/10.17226/26360

19
Thank you!

Questions?

Leighton L Christiansen 

http://orcid.org/0000-0002-0543-4268

Data Curator, National Transportation Library,

Bureau of Transportation Statistics, 

OST-R , US Department of Transportation

leighton.christiansen@dot.gov

ntldatacurator@dot.gov

Jesse Long 

https://orcid.org/0000-0002-4962-1380

Data Curation & Data Management Fellow, 

National Transportation Library,

Bureau of Transportation Statistics, 

OST-R , US Department of Transportation

jesse.long.ctr@dot.gov

20
About BTS

Founded in 1991

Preeminent source of statistics, and 

statistical datasets, on:

● Commercial Aviation, 

● Multimodal Freight Activity, and,

● Transportation Economics,

Provides context to decision makers 

and the public for understanding 

transportation statistics

BTS Director is, by law, the senior 

advisor to the Secretary of 

Transportation on data and statistics

https://www.bts.gov/

21
About NTL

NTL is an open access digital repository 

of transportation information

All collection materials are in the public 

domain, available for reuse without 

restriction

NTL is one of five national libraries

NTL is the only national library within a 

Principal Federal Statistical Agency

NTL provides access to:

● Digital collections

● Data services

● Reference services

● Knowledge networking

https://ntl.bts.gov/ 22
NTL’s Guiding Mandates

Transportation 

Equity Act for 

the 21st Century 

(TEA-21) 1998

Established NTL 

to provide 

national and 

international 

access to 

transportation 

information

Moving Ahead 

for Progress in 

the 21st Century 

(MAP-21) 2012

Expanded NTL 

role as a central 

clearinghouse for 

transportation 

research 

publications and 

data

US DOT Public 

Access Plan 

2016

Requires NTL 

host repository 

for research and 

datasets; provide

searchable DMP 

collection, and, 

assign persistent 

identifiers

Foundations for 

Evidence-Based 

Policymaking 

Act 2018

Codifies efforts to 

ensure public 

access to 

federally-funded 

research reports 

and datasets

23
About Data Curation: Reactive Actions

Reactive

Curation & Preservation

● Repository Ingest

● Access & Reuse

● Preservation/Mitigation

● Format Migration

● Disposition

24
About Data Curation: Proactive Actions

●

●

●

●

●

Proactive

Creation & Collection

● Standard Workflows: File Naming

● Data Management & Training: DMPs

● Robust Documentation: Readme & Codes

● Controlled Vocabularies: Data Dictionaries

● Metadata Standards: Choose & Publicize 

● Persistent Identification: DOI, ORCID, ROR

● Preservation Planning: Repository & 25