The research Data Lifecycle:

From Planning to Sharing

Mercè Crosas, IQSS, Harvard University   @mercecrosas

Every two years, the amount of new digitized data is equal to all of the data ever collected before. The world’s knowledge is at our fingertips, and data science allows us to effectively and efficiently make use of that knowledge. This is facilitating a societal shift as big as the Industrial Revolution.

 

Phil Bourne, Data Science Director, UVA, Former Director for Data Science, NIH; UVA Today, Q&A, August 21, 2017

REsearch DATA ARE AN ASSET TO RESEARCHERS and To the University, AND need to be Handled with CARE

Library

Information Sciences

HandLING Data with CARE,
A COLLAborative effort

Information TechnologY

Research

Vice-Provost for Research

Data Governance

Authorship,
data citation

Data  Science

Security

Storage

Computation

Repositories

Sponsored Research

Data Agreements

Software
Tools

Privacy

Research data management concerns the organization of data, from its entry to the research cycle through the dissemination and archiving of valuable results. It aims to ensure reliable verification of results, and permits new and innovative research built on existing information.

 

Whyte, A., Tedds, J. (2011). ‘Making the Case for Research Data Management’. DCC Briefing Papers. Edinburgh: Digital Curation Centre

Research Data management AIMS

Data Collection, Acquisition

Support for the data lifecycle must accommodate differences across research domains, data types, and methodologies:

• natural, physical, social sciences, humanities, health, biomedicine
• qualitative vs quantitative data and methods
• small, medium (MB to GB) vs large (TB to PB)
• structured vs unstructured, static vs streaming

Data Sharing and Archiving

Planning

The Research Data Lifecycle

Data Collection,

Acquisition

• A Data Management Plan (DMP) helps you think how to organize, store, and share the data from your research project.
• A DMP is required by most research funding organizations.
• A DMP is a living document.
• The DMPTool assists you with requirements and templates

Storage and Analysis

Data Sharing and Archiving

Planning

In 2018, InTernational DMPTool LAUNCHES

https://dmptool.org/           

First, rigorously collected, well-preserved data sets — including meaningful descriptors or metadata — will help the data owners to reach solid, meaningful results. Second, they will help future investigators to make sense of and reuse data, thereby enhancing utility and reproducibility. Preserving comprehensive data, ideally for many years, also reduces the risk of duplicating science done by others.

Data Collection, Acquisition

Storage and
Analysis

Data Sharing and Archiving

Planning

• When data are collected by the researcher, consider using data collection tools (e.g., Electronic Lab Notebooks).
• When data are acquired from third party, consider defining a Data Use Agreement.

Data USe Agreement, as Defined at Harvard

Data Use Agreements govern access to and treatment of data:

1. provided by an outside organization to your organization for use in your organization’s research, or
2. provided by your organization to an outside organization for use in its research.

Data Collection, Acquisition

Storage and Analysis

Data Sharing and Archiving

Planning

Sweeney L, Crosas M, Bar-Sinai M. Sharing Sensitive Data with Confidence: The DataTags System. Technology Science. 2015101601. October 16, 2015. http://techscience.org/a/2015101601

DataTags proposes 6 standard levels To SUPPORT sensitive/Restricted data

Data Collection, Acquisition

Storage and  Analysis

Data Sharing and Archiving

Planning

• Make open research data the default, "as open as possible, as closed as needed" (Horizon 2020).
• Many funding organizations and journals require data sharing.
• Reward researchers when sharing data by giving them credit.
• Use trusted data repositories aligned with FAIR data principles.

https://www.nature.com/articles/sdata201618

FAIR Principles:
 4 principles, 15 sub-principles

FAIR Principles For Humans and Machines:
In Brief

• Findable
  • Globally unique, resolvable, and persistent identifier
  • Machine-readable descriptions to support structured search 
• Accessible
  • Metadata is accessible beyond the lifetime of the dataset
  • Clearly defined access and security protocols (FAIR != Open)
• Interoperable
  • Extensible machine interpretable formats for data + metadata
  • Use FAIR vocabularies and link to other resources
• Reusable
  • Provide licensing, provenance, and use community-standard

FAIR slides acknowledgement: Michel Dumontier

Some research communities are taking the LEAD

http://copdess.org

"we commit to these goals:

Ensuring that Earth, space, and environmental science research outputs, including data, software, and samples or standard information about them, are open, FAIR, and curated in trusted domain repositories .."

 

Enabling FAIR DATA in EARTH, SPACE, and Environmental Sciences

Technology can Help

A Solution for Data Sharing and Archiving

Aligned with FAIR data principles

DATAVERSE IS A REPOSITORY

for finding, citing, and publishing data 

 

Dataverse is A Platform

for building your own data repository

 

DATAVERSE IS A Community

which facilitates data access and data sharing around the world

Community
Features
Data

Projects 

Dataverse.ORG

35 Dataverse Repositories sites around the world

Dataverse Community GROWTH 

2006

Dataverse Development starts at Harvard's Institute for Quantitative Social Science (IQSS)

2
Dataverse sites

2015

14
Dataverse sites 

2017

23
Dataverse sites 

2018

35
Dataverse sites 

2016

18

Dataverse sites 

First Annual Dataverse Community Meeting

4 developers

First release

74 contributors

30 releases

12,807 commits

Global Dataverse Community Consortium

http://dataversecommunity.global (coming soon!)

Community
Features
Data

Projects 

Data Citation:

Credit as an incentive to Share Data

• A formal data citation automatically generated
• Attribution to data creators and data providers 
• Persistent identifier (e.g., DOI) resolves to dataset landing page
• Version in citation
• Universal Numerical Fingerprint (UNF):  a checksum independent of file format, for tabular data files
• Compliant with the Joint Declaration of Data Citation Principles

Download data citation ready to be used in reference manager

At multiple Levels:

• Citation metadata
• Custom metadata
• File metadata
• Variable-level metadata 

With multiple Standards:

• Data Documentation Initiative (DDI)
• Dublin Core
• Schema.org 

Download metadata in multiple formats

Schema.org  USed BY GOOGLE DAtaset SearcH

• Schema.org JSON-ld embedded in HTML of dataset landing page
• Datasets become discoverable through Google Dataset Search

Metadata from schema.org in Dataverse dataset landing page

Versioning OF DATASETS AND FILES 

TIERED ACCESS to Data

• Default access is public, with CC0 waiver
• Allow public and restricted files
• Descriptive metadata always public for discoverability
• Custom Terms of Use, when needed 
• Optional Guestbook to collect information from users

Public

Restricted

Tabular data exploration

• Variable metadata automatically extracted
• Descriptive statistics automatically computed

Metadata Extraction from Astronomy files

Metadata (instrument information) is extracted automatically from FITS files header upon data upload

Metadata from FITS Header

MANAGE and customize your own Dataverse

• Create a dataverse to manage your own collection of datasets
• Brand your dataverse or embed in your website 

Extensive API  to Enable Tool Integration 

http://guides.dataverse.org

Community
Features
Data

Projects 

a wide Variety of Data and Dataverses

• Dataverse for Journals
• Dataverse for Researchers
• Dataverse for Research Communities
• Dataverse for one or multiple Institutions

Data Policies IN social science Journals

Crosas, Gautier, Karcher, Kirilova, Otalora, Schwartz, 2018. Data Policies of highly-ranked social science journals

More than 50% of the top 50 journals in anthropology, economics, psychology, and political sciences have data policies that either encourage or require to share the data associated with the article.

Dataverse for a Journal

Hosted at Harvard Dataverse repository  (80 journal dataverses)

Dataverse for a Researcher

Hosted at Harvard Dataverse repository

Dataverse for A RESEARCH COMMUNITY: Strucural Biology

Hosted SBGrid Consortium, Harvard Medical School

Dataverse For multiple Universities

Hosted by Texas Digital Libraries, a  consortium of Texas Higher-Education Institutions

Harvard Dataverse:
OPEN to ALL the RESearch COMMUNITY

Hosted by Harvard University, in collaboration with Harvard Library, HUIT, and IQSS

http://dataverse.harvard.edu

# DatAsets Deposited at Harvard Dataverse:

30,000

Average # Released Datasets per Month (in 2018):

250

 

# of total DOWNLOADs:
5M

Average # downloads per month (in 2018):
150,000

 

Community
Features
Data

Projects 

On-GOIng Projects

• Large data
• Sensitive data
• Data Quality, Reproducibility,  Reusability
• Open Source 'Health' Index

Large Data

 

More ways to upload data

• rsync

More ways to access data:

• Local access
• Compute in the cloud
• Compute in institutional
  research computing 

More storages:

• ​Remote secure storage; data enclaves

 

Funded partially by Helmsley Charitable Trust,  with focus on biomedical data, in collaboration with Piotrek Sliz

Sensitive Data: DataTaGS 

Funded partially by National Science Foundation, 

in collaboration with Latanya Sweeney

Standardize data security and access levels

Sensitive Data: Privacy Preserving Tools

Funded by National Science Foundation,

in collaboration with Harvard Privacy Tools Project

Integration with reprodUcibility Tools:
Code Ocean

Funded by Sloan Foundation, in collaboration with CodeOcean

Integration with repRODUCIBILITY Tools:
Encapsulator

Funded by Sloan Foundation, in collaboration with Margo Seltzer

Integration with reproducibility Tools:
CORE2

Funded by Sloan Foundation, in collaboration with the ODUM institute at UNC Chapel Hill

OPEN Source 'Health' Index

• A quantitative study to determine a health index for open source projects
• Leverage previous work (e.g., LYRASIS project and  Qualification and Selection of Open Source Software (QSOS) )

Funded by IMLS

Thank YOu!

 

dataverse.org

dataverse.harvard.edu

The Dataverse Team @IQSS
https://groups.google.com/forum/#!forum/dataverse-community

 

scholar.harvard.edu/mercecrosas

@mercecrosas