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Research Spending & Results

Award Detail

Doing Business As Name:Indiana University
  • Franco Pestilli
  • (347) 829-4185
Award Date:09/13/2019
Estimated Total Award Amount: $ 152,802
Funds Obligated to Date: $ 152,802
  • FY 2019=$152,802
Start Date:12/01/2019
End Date:11/30/2022
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Proposal: CRCNS US-German Data Sharing Proposal: DataLad - a decentralized system for integrated discovery, management, and publication of digital objects of science
Federal Award ID Number:1912270
DUNS ID:006046700
Parent DUNS ID:006046700
Program:CRCNS-Computation Neuroscience
Program Officer:
  • Kurt Thoroughman
  • (703) 292-7281

Awardee Location

Street:509 E 3RD ST
Awardee Cong. District:09

Primary Place of Performance

Organization Name:Indiana University
Street:509 E. 3rd St
Cong. District:09

Abstract at Time of Award

Scientists collect terabytes of critical data every year. Recently a strong open science movement has generated traction for the beneficial practice of sharing data across laboratories, universities and research institutions. Yet, sharing data is not enough. Data must be shared using standardized formats and accompanied by curated metadata to allow for tracking, search, and organization. Metadata are essential for scientific discovery, as they are routinely used to complete all data analyses. However, to date, most brain projects focus on collecting or analyzing data, not on metadata management. Typical metadata records consist of heterogeneous study descriptions, developed at study release stage, without consistency across records or standard mechanisms to track changes. This project will increase access to brain data and improve metadata handling by combining two NSF-funded projects. It will develop a first-of-its-kind metadata management system able to track data and metadata distributed across heterogeneous geographical locations, storage systems and data formats. This portion of the project will expand the functionality of a previously funded NSF project DataLad. DataLad will also be enhanced to interoperate with major data repositories such as OSF and Figshare. Furthermore, the project will use the NSF-funded cloud computing platform to create a data and metadata marketplace by gathering data from multiple currently separated repositories into a single ecosystem . The goal is to improve interoperability across open science projects and make data and metadata easily searchable and available for computing on national cyberinfrastructure systems, ultimately advancing scientific discovery by increasing data discoverability, utilization, and publication. This project will generate various technological advances. The core target will be an extensible system capable of automated gathering of metadata from various domains. It will be comprised of two major components: 1) a set of metadata parser algorithms that extract metadata from datasets and individual files using a flexible JSON-LD based data structure (with the ability to encode controlled vocabularies where available) and 2) an aggregation procedure that merges the aggregated metadata across parsers and stores them into compressed files that are optimized for bandwidth-efficient exchange and can be queried directly, or used as input into SQL or graph databases for data discovery applications. Extracted metadata will be included within the same datasets under Git and git-annex version control for unambiguous referencing and versatile data logistics. In parallel development we will improve interoperability of DataLad with existing data publishing portals (such as Figshare and OSF) by taking advantage of extracted metadata (e.g., Author, Description) to prefill required fields, and also by bundling the entire Git object store within the publication to make such published datasets installable back by DataLad without any loss of information. To make such published datasets discoverable, we will establish a crowd-sourced registry (with a RESTful API) which will get announcements on the availability of new datasets upon publication and aggregate their metadata to enable querying across datasets and data hosting providers. The final development will be the integration of DataLad within the data marketplace. This will make it possible to search and install datasets on as well as to process the data utilizing the analyses Apps on various NSF-funded national cyberinfrastructure high-throughput computer systems. A companion project is being funded by the Federal Ministry of Education and Research, Germany (BMBF). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Publications Produced as a Result of this Research

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Kaneko, Takaaki C. and Takemura, Hiromasa Q. and Pestilli, Franco A. and Silva, Afonso and Ye, Frank and Leopold, David "Spatial organization of occipital white matter tracts in the common marmoset" Brain Structure and Function, v., 2020, p.. doi:10.1007/s00429-020-02060-3 Citation details  

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