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Elliott MJ, Poelen JH, Fortes JAB. Signing data citations enables data verification and citation persistence. Sci Data 2023; 10:419. [PMID: 37369663 DOI: 10.1038/s41597-023-02230-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/12/2023] [Indexed: 06/29/2023] Open
Abstract
Commonly used data citation practices rely on unverifiable retrieval methods which are susceptible to content drift, which occurs when the data associated with an identifier have been allowed to change. Based on our earlier work on reliable dataset identifiers, we propose signed citations, i.e., customary data citations extended to also include a standards-based, verifiable, unique, and fixed-length digital content signature. We show that content signatures enable independent verification of the cited content and can improve the persistence of the citation. Because content signatures are location- and storage-medium-agnostic, cited data can be copied to new locations to ensure their persistence across current and future storage media and data networks. As a result, content signatures can be leveraged to help scalably store, locate, access, and independently verify content across new and existing data infrastructures. Content signatures can also be embedded inside content to create robust, distributed knowledge graphs that can be cited using a single signed citation. We describe applications of signed citations to solve real-world data collection, identification, and citation challenges.
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Affiliation(s)
| | - Jorrit H Poelen
- Ronin Institute, Montclair, NJ, USA.
- UC Santa Barbara Cheadle Center for Biodiversity and Ecological Restoration, Santa Barbara, CA, USA.
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Hardisty AR, Ellwood ER, Nelson G, Zimkus B, Buschbom J, Addink W, Rabeler RK, Bates J, Bentley A, Fortes JAB, Hansen S, Macklin JA, Mast AR, Miller JT, Monfils AK, Paul DL, Wallis E, Webster M. Digital Extended Specimens: Enabling an Extensible Network of Biodiversity Data Records as Integrated Digital Objects on the Internet. Bioscience 2022; 72:978-987. [PMID: 36196222 PMCID: PMC9525127 DOI: 10.1093/biosci/biac060] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The early twenty-first century has witnessed massive expansions in availability and accessibility of digital data in virtually all domains of the biodiversity sciences. Led by an array of asynchronous digitization activities spanning ecological, environmental, climatological, and biological collections data, these initiatives have resulted in a plethora of mostly disconnected and siloed data, leaving to researchers the tedious and time-consuming manual task of finding and connecting them in usable ways, integrating them into coherent data sets, and making them interoperable. The focus to date has been on elevating analog and physical records to digital replicas in local databases prior to elevating them to ever-growing aggregations of essentially disconnected discipline-specific information. In the present article, we propose a new interconnected network of digital objects on the Internet—the Digital Extended Specimen (DES) network—that transcends existing aggregator technology, augments the DES with third-party data through machine algorithms, and provides a platform for more efficient research and robust interdisciplinary discovery.
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Affiliation(s)
| | | | - Gil Nelson
- Florida Museum of Natural History , Gainesville, Florida, United States
| | - Breda Zimkus
- Museum of Comparative Zoology , Cambridge, Massachusetts, United States
| | | | | | - Richard K Rabeler
- University of Michigan Herbarium , Ann Arbor, Michigan, United States
| | - John Bates
- Field Museum of Natural History , Chicago, Illinois, United States
| | - Andrew Bentley
- Biodiversity Institute, University of Kansas , Lawrence, Kansas, United States
| | | | - Sara Hansen
- Central Michigan University Herbarium, Central Michigan University , Mt. Pleasant, Michigan, United States
| | | | - Austin R Mast
- Department of Biological Science, Florida State University , Tallahassee, Florida, United States
| | - Joseph T Miller
- Global Biodiversity Information Facility Secretariat , Copenhagen, Denmark
| | - Anna K Monfils
- Central Michigan University Herbarium, Central Michigan University , Mt. Pleasant, Michigan, United States
| | - Deborah L Paul
- University of Illinois Urbana Champaign , Champaign, Illinois, United States
| | - Elycia Wallis
- Atlas of Living Australia, CSIRO , Melbourne, Australia
| | - Michael Webster
- Macaulay Library, Cornell Lab of Ornithology , Ithaca, New York, United States
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Zhao M, Rattanatamrong P, DiGiovanna J, Mahmoudi B, Figueiredo RJ, Sanchez JC, Príncipe JC, Fortes JAB. BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure. Annu Int Conf IEEE Eng Med Biol Soc 2008; 2008:646-649. [PMID: 19162738 DOI: 10.1109/iembs.2008.4649235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.
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Affiliation(s)
- Ming Zhao
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.
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