On Tuesday, March 9th, MRC doctoral student Sam Grabus presented about her dissertation research as invited guest speaker for the graduate chapter of the Rutgers University Library and Information Science Student Association.
Presentation title: “Historical Subject Representation: Identifying Temporal Concept Drift for Temporally-Aligned Access Points.”
View the full presentation on the Rutgers LISSA Youtube Channel: LINK
The Metadata Research Center is proud to present a lecture from Dr. Marcia Zeng (Professor in the School of Information at Kent State University) as part of the 2021 Distinguished Speaker Series and LOVE Data Week.
Presenter: Dr. Marcia Zeng, Kent State University
Time: 11am- 12pm EST
Location: Zoom
Zoom Registration: LINK
Title: Ensuring the FAIRness of Metadata in the Open Data Mainstream— Requirements and opportunities
Abstract:
The FAIR principles have been widely implemented in the open data environment during the past several years to ensure that published digital resources are Findable, Accessible, Interoperable, and Reusable (FAIR). The principles refer to three types of entities, including data (or any digital object), metadata (information about that digital object), and infrastructure. In this presentation, the focus will be on metadata. After an introduction of the FAIR principles and W3C’s DCAT (Data Catalog Vocabulary) ontology, the presentation will report the new efforts of the AGRIS (The International System for Agricultural Science and Technology), a global public service provided by the Food and Agriculture Organization (FAO) of the United Nations (UN). Using the U.S. Department of Agriculture (USDA) research data’s metadata in the pilot study, and enabled by the interoperability of the metadata structures, AGRIS effectively extended the metadata spectrum. Now, it not only continually covers bibliographic metadata of publications worldwide, but also includes research data resources. The presentation will share the research findings on ensuring the FAIRness of metadata in the Open Data and Open Science movement.
On Thursday, January 21st, Drexel Engineering Librarian Jay Bhatt presented at the virtual International Conference on Statistical Tools & Techniques and Research Data Analysis (ICSTTRDA 2021) at Central University of Gujarat, India. The presentation abstract and slide-deck are available below.
Citation:
Bhatt, Jay. (January 2021). Information Awareness of Research Data in Science and Engineering. Virtual International Conference on Statistical Tools and Techniques for Research Data Analysis (ICSTTRDA 2021), the School of Library and Information Science, Central University of Gujarat, Gandhinagar, India (ICSTTRDA 2021), Central University of Gujarat, India.
On Thursday, January 21st, CCI professors Jane Greenberg and David Breen, along with PhD student Joel Pepper, presented at the virtual International Conference on Statistical Tools & Techniques and Research Data Analysis (ICSTTRDA 2021) at Central University of Gujarat, India. The presentation abstract and slide-deck are available below.
Citation:
Breen, D., Pepper, J., & Greenberg, J. (2021, January 21-22). Approaches for Computing Specimen Image Research Data. International Conference on Statistical Tools & Techniques and Research Data Analysis (ICSTTRDA 2021), Central University of Gujarat, India. [Abstract]
On Friday, December 11th, doctoral student Christopher Rauch will present a paper for the Computational Archival Science workshop at the IEEE International Conference on Big Data (IEEE Big Data 2020). The paper, titled “A Computational Approach to Historical Ontologies,” is co-authored with CCI’s Mat Kelly, Jane Greenberg, Sam Grabus, and Joan Boone, as well as California Digital Library’s John Kunze, and Temple University’s Peter Logan.
Citation:
Kelly, M., Greenberg, J., Rauch, C. B., Grabus, S., & Boone, J. P. (In Press, December 10-13, 2020). A Computational Approach to Historical Ontologies 2020 IEEE International Conference on Big Data (IEEE BigData 2020), Atlanta, Georgia, US. [PDF]
On December 4th, 2020, doctoral candidate Jeremy Leipzig and Dr. Jane Greenberg were awarded the “Best Research Paper Award” at the 14th International Conference on Metadata and Semantics Research (MTSR 2020). The paper, titled “Biodiversity Image Quality Metadata Augments Convolutional Neural Network Classification of Fish Species,” was co-authored by J. Leipzig, Y. Bakis, X. Wang, M. Elhamod, K. Diamond, M. Maga, W. Dahdul, A. Karpatne, P. Mabee, H. L. Bart Jr. and J. Greenberg.
Research supported by NSF OAC Office of Advanced Cyberinfrastructure (OAC) #1940233and #1940322.
Metadata Research Center, Drexel University, doctoral students (Xintong Zhao, Deborah Garwood, and Jeremy Leipzig), will each present original research at MTSR 2020–14th International Conference on Metadata and Semantics Research on December 3rd in a session starting at 10:15 AM EST (17:15 EET-Eastern European Time). This year, MTSR is virtual, and REGISTRATION IS FREE FOR ALL.
Metadata Research Center affiliated papers being presented include:
To register:
Full conference program is available @: http://www.mtsr-conf.org/programme
On December 3rd, the Metadata Research Center will host an Alice B. Kroeger Distinguished Lecture, featuring James Briggs Murray, Founding Curator (1972-2009) of the Moving Image and Recorded Sound Division, Schomburg Research Center, at the New York Public Library.
Presenter: James Briggs Murray, Founding Curator (1972-2009),
Moving Image and Recorded Sound Division, Schomburg Research Center, The New York Public Library
Title: Understanding and Developing Black Popular Music Collections
Date: Thursday, December 3rd
Time: 4:30-6:00pm EDT
Location: Zoom Registration Link
Participants must register in order to attend.
Abstract: The retired Founding Curator of the Moving Image and Recorded Sound Division of The New York Public Library’s Schomburg Center for Research in Black Culture uses recorded audio clips to illustrate his three and a half-decade mission to create a comprehensive recorded music collection in a research library setting. The journey begins in West Africa and moves through such globally impactful genres as work songs, blues, spirituals, jazz (in its many iterations), gospel, rhythm & blues, rock & roll, rock, funk, disco, and rap.
James’ presentation harkens back to his 1983 Drexel Library Quarterly article on black music collections.
Citation: Murray, J. B. (1983). Understanding and Developing Black Popular Music Collections. Drexel library quarterly, 19(1), 4-54.
*Also available in ERIC: ERIC Number EJ300012
Bio:
Among the highlights of his career as a Curator, first and foremost, Mr. Murray, in the mid-1970s, conceived and founded the Moving Image and Recorded Sound Division of NYPL’s Schomburg Center for Research in Black Culture, the world’s largest and most comprehensive research library devoted to the preservation of the history and culture of peoples of African descent worldwide.
CCI Professor Il-Yeol Song has received the DKE Best Paper Award for his 2017 paper, “Big data technologies and management: What conceptual modeling can do.”
The Elsevier journal best paper announcement is available here.
On August 5th, Xintong Zhao, CCI/Drexel University, doctoral student and MRC research assistant (RA), presented her research at the ACM/JCDL 2020 (Association of Computing Machinery/Joint Conference on Digital Libraries) workshop, “Organizing Big Data, Information, and Knowledge.”
Zhao’s presentation, “Scholarly Big Data: Computational Approaches to Semantic Labeling in Materials Science,” is from research she is conducting in collaboration with team members: the NSF supported Harnessing the Data Revolution (HDR) initiative, Accelerating the Discovery of Electronic Materials through Human-Computer Active Search. Zhao’s research examines computation and semantic labeling for scholarly big data in materials science. She reported on a baseline comparative analysis she led, comparing the ontology-based automatic indexing with the Helping Interdisciplinary Vocabulary Engineering (HIVE-4-MAT) application and the MATScholar system, which uses named entity recognition (NER), supported by an RNN (Recursive Neural Network). [presentation slides]
