1. Research Data Management Philip Tarrant Global Institute of Sustainability

2. New research data management world Federal funding agencies now expect researchers to include data management plans in proposals Article 26. Sharing of Findings, Data, and Other Research Products a. NSF expects significant findings from research and education activities it supports to be promptly submitted for publication, with authorship that accurately reflects the contributions of those involved. It expects investigators to share with other researchers, at no more than incremental cost and within a reasonable time, the data, samples, physical collections and other supporting materials created or gathered in the course of the work. It also encourages grantees to share software and inventions or otherwise act to make the innovations they embody widely useful and usable. b. Adjustments and, where essential, exceptions may be allowed to safeguard the rights of individuals and subjects, the validity of results, or the integrity of collections or to accommodate legitimate interests of investigators. Federal funding agencies now expect researchers to include data management plans in proposals Article 26. Sharing of Findings, Data, and Other Research Products a. NSF expects significant findings from research and education activities it supports to be promptly submitted for publication, with authorship that accurately reflects the contributions of those involved. It expects investigators to share with other researchers, at no more than incremental cost and within a reasonable time, the data, samples, physical collections and other supporting materials created or gathered in the course of the work. It also encourages grantees to share software and inventions or otherwise act to make the innovations they embody widely useful and usable. b. Adjustments and, where essential, exceptions may be allowed to safeguard the rights of individuals and subjects, the validity of results, or the integrity of collections or to accommodate legitimate interests of investigators.

3. New research data management world Promise (DMP) Deliver (share data) Measure compliance

4. New research data management world Promise (Easy) Deliver (Hard) Measure (TBD) Get this right

5. Context : The scientist’s view Design experiments that will collect the data needed to answer the research question(s) Process those data in a way that will produce the results needed to draw sensible conclusions Publish those conclusions so that they can be shared with the wider scientific community and ultimately the public Investigators often feel that their responsibility ends here. Design experiments that will collect the data needed to answer the research question(s) Process those data in a way that will produce the results needed to draw sensible conclusions Publish those conclusions so that they can be shared with the wider scientific community and ultimately the public Investigators often feel that their responsibility ends here.

6. Context: The data manager’s view Consolidate the data collected by investigators and produce datasets in formats that encourage re-use by other investigators Make those data accessible to the wider scientific community and potential citizen scientists Publish the metadata necessary to enable the data to be interpreted by third parties The investigators’ responsibility actually ends HERE! Consolidate the data collected by investigators and produce datasets in formats that encourage re-use by other investigators Make those data accessible to the wider scientific community and potential citizen scientists Publish the metadata necessary to enable the data to be interpreted by third parties The investigators’ responsibility actually ends HERE!

7. The “reusable” data challenge How do we: Consolidate interdisciplinary data from disparate sources in ways that provide practical (and achievable) opportunities for powerful, complex, synthetic analysis? Encourage commonality in scientific measurements and data standards so that we can perform a meaningful comparison between “apples” and “apples”? Increase data re-use to extract the maximum possible value from precious research dollars Share these data with collaborators in a way that supports answering the big questions? Someone’s responsibility ends HERE! How do we: Consolidate interdisciplinary data from disparate sources in ways that provide practical (and achievable) opportunities for powerful, complex, synthetic analysis? Encourage commonality in scientific measurements and data standards so that we can perform a meaningful comparison between “apples” and “apples”? Increase data re-use to extract the maximum possible value from precious research dollars Share these data with collaborators in a way that supports answering the big questions? Someone’s responsibility ends HERE!

8. Current data management process Researcher completes project DM asks for data and metadata DM asks for data and metadata again Researcher plans next project DM pledges eternal friendship and reminds about metadata Researcher publishes research Researcher starts next project DM dies waiting for metadata Researcher sends data to DM DM rescinds friendship pledge while pleading for metadata Researcher completes project DM asks for data and metadata DM asks for data and metadata again Researcher plans next project

9. Ideally, where do we want to be? I need information…

10. Realistically, where do we want to be? A single, consolidated repository (“virtual” notebook) where we can store information about projects, organization, methods and protocols, and datasets (metadata) The means to enter data wherever we may be A data catalog that helps find useful research data (both published and unpublished) Visualization tools to help assess the value of data A single, consolidated repository (“virtual” notebook) where we can store information about projects, organization, methods and protocols, and datasets (metadata) The means to enter data wherever we may be A data catalog that helps find useful research data (both published and unpublished) Visualization tools to help assess the value of data

11. A Single Datasource Data Archives GIOS DB Projects People Media/ refs Metadata Methods

12. Enter information in different ways GIOS DB

13. A catalog to help find and evaluate data GIOS DB Organization data 1 1 Public datasets Metadata 2 2 Internal datasets Metadata 3 3

14. Data management workflow Create project record Create metadata record(s) Project initiation Project initiation Project completion Project completion Describe field methods Describe data attributes Submit data and metadata Data collection Data collection Data analysis Data analysis Finalize datasets Describe lab methods Create datasets Data Management System Research publication Publish data and metadata

15. What does this mean to investigators? Effort required to input project and dataset information “Pay As You Go” model reduces the back end effort when you really want to be planning for the future A single resilient place to store project information – accessible by all team members Latest “version” always available Content available as input to manuscripts The next data management plan is… Effort required to input project and dataset information “Pay As You Go” model reduces the back end effort when you really want to be planning for the future A single resilient place to store project information – accessible by all team members Latest “version” always available Content available as input to manuscripts The next data management plan is…

16. Project Timeline Design phase: December – February Development: – Organization/project module: Jan – April – Metadata module: March – July Assistance with testing: June - July Training: July – August Design phase: December – February Development: – Organization/project module: Jan – April – Metadata module: March – July Assistance with testing: June - July Training: July – August

17. Questions?