News & Events

Jane Greenberg Receives ASIS&T Research in Information Science Award

The Metadata Reasearch Center congratulates Jane Greenberg, its Director and Founder, for receiving the Association for Information Science & Technology’s (ASIS&T) 2023 Research in Information Science Award. The award “recognizes an individual or team who has made an outstanding contribution to information science research. The award is for a systematic “program of research” in a single area at a level beyond the single study.” ASIS&T recognized Dr. Greenberg’s wide-ranging contributions, including her current positions as principal investigator on the Metadata Capital Initiative (MetaDataCAPT’L) and the NSF-funded Institute for Data Driven Dynamical Design (ID4), and the IMLS-funded project LEADING (LIS Education and Data Science Integrated Network Group). The award committee also singled out her work with the Biology-guided Neural Network (BGNN) project and the Helping Interdisciplinary Vocabulary Engineering (HIVE) tool. Please click here for the full press release from ASIS&T, “Jane Greenberg Receives Association for Information Science and Technology (ASIS&T) Research in Information Science Award.”

News & Events

Summer 2023 NSF Research Experiences for Undergraduates (REU) Opportunities at the MRC

Two (2) virtual National Science Foundation Research Experience for Undergraduate research opportunities @ the Metadata Research Center, Drexel University, as part of the Harnessing (HDR) Institute for Data Driven Dynamical Design (ID4)

Dates: Mid-July through Mid-September

REU stipend: $5,500

Deadline: Rolling basis (Friday, July, 7th for first consideration)


Interested applicants, please sent resume and brief statement of interest (1 paragraph) indicating: 1) which REU option you would like to apply for, and 2) why you would like to participate in the REU program.

Please send your application to:

REU Option 1: Materials Science Repository Semantics

Standards are an integral component of data repository infrastructure and support of the FAIR (findable, accessible, interoperable, and reusable) data. Terminology, specifically the language (vocabulary) used to represent data, is standardized through metadata and semantic ontologies. The focus of this REU will be on investigating metadata infrastructures across a sub-set of materials science repositories, and looking specifically at the terminological representation used and alignment with semantic ontologies.

REU applicants for this project should have:

  • Some disciplinary exposure to chemistry, engineering, physics, and/or materials science.
  • Interest in semantic systems (terminology/vocabulary) and their value for representation, machine learning, and AI
  • Appreciation standards for communication human to human, human to machine, machine to machine 
  • Knowledge of Excel, Tableau, Orange, or other data science software that allows analysis and visualization, or interest in learning
  • Python, R, or other coding experience helpful, but not necessary

Research Goals

  • Explore similarities and differences of standards and data representation practices across a subset of materials science data representations.
  • Analyze and visualize data representation, specifically metadata and semantic systems.
  • Assess the effectiveness of standards and identifying areas needing more attention.

Learning Goals

  • Gain knowledge of metadata standards and semantic ontologies are key to the FAIR data principles.
  • Advance analytical and visualization research skills
  • Obtain better understanding of the relationship of standards to ML/AI

REU Option 2: Metal-Organic Frameworks (MOFs) Synthesis Extraction from Scholarly Big Data

Metal-Organic Frameworks (MOFs) are a kind of crystals (natural or synthetic) that have advanced the field of materials and solid-state sciences over the last quarter century. The synthesis procedure often reported in literature can play a critical role in data-driven discovery of Metal-organic framework materials. Unfortunately, this valuable knowledge is significantly underutilized as it remains buried in text, which is unstructured and not machine understandable. This challenge is exasperated because it is simply not feasible for human researchers to read every single article in their fields, given there are over thousands of publications, and the number is still growing exponentially. In this project, students will work with researchers in Drexel University’s Metadata Research Center, University of Central Florida and Colorado School of Mines, connected with the NSF/ID4 (Institute for Data Driven Dynamical Design) project. The focus will be on investigating the use of natural language processing techniques to extract key synthesis knowledge from unstructured text data. We seek to develop robust deep learning models which enable automatic knowledge extraction and ultimately construct knowledge graphs from scholarly corpus. REU summer students will gain deeper understanding of natural language processing and use of large pre-trained language models through the text annotation process.

Research Goals

  • Pre-train language models for downstream NLP tasks in materials science
  • Develop different deep learning models to improve extraction performance
  • Construct solid external knowledge sources (e.g., taxonomy, ontology) for future research

Learning Goals

  • Gain knowledge of deep learning frameworks such as Pytorch
  • How to generate language representations as features for deep learning models
  • Obtain better understanding of the complete workflow of information extraction (named entity recognition/relation extraction)
News & Events

LEADING Moves Forward: LEADING Forum and Welcome 2023 Fellows

On Friday, May 19th, the 2023 LEADING Forum took place in the Science Center/Quorum at Drexel University. Highlights form the Forum included a panel on ChatGPT, a fellows panel, a fellow poster session, and keynote presentations from Florence Hudson (Executive director, Northeast Big Data Innovation Hub at Columbia University), and Laurie Allen (Chief, Digital Innovation Lab (LC Labs), Library of Congress). The 2023 OCLC/LEADING Data Challenge preceded the forum on Thursday, May 18th.

In moving forward, we welcome the incoming LEADING 2023 fellows. This year’s cohort includes 18 fellows from 14 iSchools and LIS institutions from across the country. June kicks off the 2023 LEADING boot camp, which precedes the 6-month fellowship period. Read more about the 2023 fellows here.

News & Events

Scott McClellan presents at 20th RDA Plenary’s Session on Materials Science Ontologies

Scott McClellan, a second year doctoral student, presented research results to the “Data representation in materials and chemicals based on harmonised domain ontologies” birds of a feather group at the Research Data Alliance’s 20th Plenary meeting in Gothenburg, Sweden on March 21-23, 2023. His presentation, titled “Along the Border: Term Overlap Among 5 Matportal Ontologies,” focused on term overlap among a subset of ontologies maintained at the Matportal repository. It looked at how term matching algorithms for materials science semantic artifacts differed when locating terminological or URI results. His presentation stemmed from prior research done with Drs. Yuan An and Jane Greenberg and fellow graduate student Xintong Zhao. [Slides]

News & Events

Jane Greenberg and Richard Marciano Present at DLF 2022

MRC’s Jane Greenberg and Richard Marciano, Advanced Information Collaboratory (AI Collaboratory) University of Maryland, presented at the 2022 DLF Forum on Wednesday, October 12th.

Jane Greenberg presenting at DLF 2022

Their panel, titled “Innovating Data Science Education and Computational Thinking: Connecting iSchools and LAMs,” presented about two national Institute of Museum and Library Services (IMLS) initiatives connecting leading GLAMs (galleries, libraries, archives, and museums) and educators, and innovative data science education. Jane presented about the LEADING (The LIS Education and Data Science Integrated Network Group) fellowship project, and Richard presented about the TALENT (Training of Archival & Library Educators with iNnovative Technologies) Network.

Richard Marciano presenting at DLF 2022

The presentation slides are available here: [LINK].

News & Events

MRC Publication Updates

Sharing news on MRC recent and forthcoming publication! 

For more information on MRC student and faculty outputs, see the publications page.

News & Events

MRC Hosts Two REUs

This summer the Metadata Research Center, College of Computing & Informatics, hosted two NSF Research Experiences for Undergraduates (REUs) as a partner in the Institute for Data Driven Dynamical Design (ID4). The ID4 REU interlinked with Drexel’s Smart Manufacturing REU (SMREU), which is focused on the application of computational methods and automation to support smart manufacturing and productivity and supply chains. ID4 connects to SMREU through the use of computational methods to accelerate the discovery of new materials.

Xintong Zhao, Jane Greenberg, David Venator, and Elijah Kellner at the REU poster session

This summer’s REU’s included David Venator, Materials Science and Engineering major from Northwestern University, and Elijah Kellner Materials Chemistry major from Winona State University. The REUs collaborated on two projects: Automated Identification of Metal-Organic Framework Synthesis Information and Exploring Faceted Ontologies for the Indexing of Materials Science Literature.

Elijah Kellner

The REU’s were mentored by MRC doctoral students Xintong Zhao and Scott McClellan. On Thursday, August 11th, the REU’s presented their work at a poster session in the Bossone Research Center.

News & Events

MRC Presents at NSF Harnessing the Data Revolution Institute for Data Driven Dynamical Design (ID4)

May 23-25 Metadata Research Center team members Scott McClellan, Xintong Zhao, and Jane Greenberg visited Colorado School of Mines for a NSF-supported Harnessing the Data Revolution Institute for Data Driven Dynamical Design (ID4) meeting. McClellan and Greenberg presented “Shared, Standardized Semantics: Accelerating Human and Computational Intelligence.”

Scott McClellan, Xintong Zhao, and Jane Greenberg at Colorado School of Mines for ID4

McClellan also worked with Rachel Orenstein on poster, “Facilitating Laboratory Procedures with Semantics,” and Zhao along with Kyle Langlois, Jacob Furst, Jiaxing Qu, and Ferdaushi Bipasha developed a poster entitled “Materials Knowledge Extraction from Scientific Literature.”

Jane Greenberg and Xintong Zhao at ID4

The Metadata Research Center is a research partner in the ID4, focusing on knowledge extraction, with specific interests in ontology alignment, knowledge graph construction, as well as their applications for automatic indexing.

News & Events

Alice B. Kroeger Distinguished Lecture Series: Juliane Schneider

In celebration of Women’s History Month, the Metadata Research Center is honored to have metadata expert and Drexel graduate, Juliane Schneider, present the 2022 Alice B. Kroeger Distinguished Lecture.

Presenter: Juliane Schneider, Senior Bioinformatics Analyst/Data Liaison, Sage Bionetworks
Date: Thursday, March 31st
Time: 12:00 PM EDT
Location: Zoom Registration Link
Participants must register in order to attend.
Title: Metadata: Attitude and Practice Change

Read more about the upcoming talk and presenter below.

Continue reading “Alice B. Kroeger Distinguished Lecture Series: Juliane Schneider”
News & Events

Drexel’s Metadata Research Center (MRC) and LEADING Fellows active at 2021 IEEE Big Data Conference

Great participation at this year’s IEEE Big Data conference (IEEE Big Data), which ran virtually, December 15-18th, 2021.

On Wednesday, December 15, MRC members and collaborators affiliated with the NSF-HDR projects, Accelerating the Discovery of Electronic Materials through Human-Computer Active Search and the new Institute for Data-Driven Dynamical Design project presented two papers: “Fine-Tuning BERT Model for Materials Named Entity Recognition” by Xintong Zhao, Jane Greenberg, Yuan An, and Xiaohua Tony Hu, and “Knowledge Graph-Empowered Materials Discovery,” by X. Zhao, J. Greenberg, S. McClellan, Y. Hu, S. Lopez, S. Saikin, X. Hu, and Y. An.

On Friday December 17th,  Sam Grabus and Jane Greenberg participated in the IEEE Big Data 6th Annual Computational Archival Science workshop, and presented their paper, “Computational Curation and the Application of Large-Scale Vocabularies,” stemming from work with the 19th C. and historical vocabularies [Presentation Recording]. LEADING ‘21 Fellows Lencia Beltran and Emily Ping O’Brien, along with LEADING advisory board member, Richard Marciano, also presented on their paper, “A Framework for Unlocking and Linking WWII Japanese American Incarceration Biographical Data,” connected with the Densho collection.

Also, to note, Jennifer Proctor, a LEADING ’21 Fellow, presented a paper, titled “An AI-Assisted Framework for Rapid Conversion of Descriptive Photo Metadata into Linked Data.”