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   Sealy, Cordelia (October 2017, Nano today)

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   Hazbón, Manzour Hernando; Rigouts, Leen; Schito, Marco; Ezewudo, Matthew; Kudo, Takuji; Itoh, Takashi; Ohkuma, Moriya; Kiss, Katalin; Wu, Linhuan; Ma, Juncai; et al (April 2018, Pathogens and Disease)
3. Twitter-Based Knowledge Graph for Researchers

   Vincent, Kyle; Meno, Emma (May 2020, Virginia tech)

   null (Ed.)

   The Twitter-Based Knowledge Graph for Researchers project is an effort to construct a knowledge graph of computation-based tasks and corresponding outputs. It will be utilized by subject matter experts, statisticians, and developers. A knowledge graph is a directed graph of knowledge accumulated from a variety of sources. For our application, Subject Matter Experts (SMEs) are experts in their respective non-computer science fields, but are not necessarily experienced with running heavy computation on datasets. As a result, they find it difficult to generate workflows for their projects involving Twitter data and advanced analysis. Workflow management systems and libraries that facilitate computation are only practical when the users of these systems understand what analysis they need to perform. Our goal is to bridge this gap in understanding. Our queryable knowledge graph will generate a visual workflow for these experts and researchers to achieve their project goals. After meeting with our client, we established two primary deliverables. First, we needed to create an ontology of all Twitter-related information that an SME might want to answer. Secondly, we needed to build a knowledge graph based on this ontology and produce a set of APIs to trigger a set of network algorithms based on the information queried to the graph. An ontology is simply the class structure/schema for the graph. Throughout future meetings, we established some more specific additional requirements. Most importantly, the client stressed that users should be able to bring their own data and add it to our knowledge graph. As more research is completed and new technologies are released, it will be important to be able to edit and add to the knowledge graph. Next, we must be able to provide metrics about the data itself. These metrics will be useful for both our own work, and future research surrounding graph search problems and search optimization. Additionally, our system should provide users with information regarding the original domain that the algorithms and workflows were run against. That way they can choose the best workflow for their data. The project team first conducted a literature review, reading reports from the CS5604 Information Retrieval courses in 2016 and 2017 to extract information related to Twitter data and algorithms. This information was used to construct our raw ontology in Google Sheets, which contained a set of dataset-algorithm-dataset tuples. The raw ontology was then converted into nodes and edges csv files for building the knowledge graph. After implementing our original solution on a CentOS virtual machine hosted by the Virginia Tech Department of Computer Science, we transitioned our solution to Grakn, an open-source knowledge graph database that supports hypergraph functionality. When finalizing our workflow paths, we noted some nodes depended on completion of two or more inputs, representing an ”AND” edge. This phenomenon is modeled as a hyperedge with Grakn, initiating our transition from Neo4J to Grakn. Currently, our system supports queries through the console, where a user can type a Graql statement to retrieve information about data in the graph, from relationships to entities to derived rules. The user can also interact with the data via Grakn's data visualizer: Workbase. The user can enter Graql queries to visualize connections within the knowledge graph. 

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4. Community Roles for Supporting Emerging Education Researchers

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   Bennett, Frank (Ed.)

   DBER attracts many faculty from other STEM disciplines, and these faculty have little or no specific training in DBER. DBER requires a mastery of quantitative, qualitative, and/or mixed methodologies, and also a nuanced understanding of breadth of topic, research questions, and theoretical frameworks. This interdisciplinarity is particularly challenging for emerging DBER researchers who often switch into DBER with only discipline specific content and research training. As part of a large study about how STEM faculty become involved with DBER, we interviewed a number of emerging DBER faculty about their pathways into DBER. We conducted a thematic analysis of these interviews grounded in the theoretical frameworks of the reasoned action approach and conjecture mapping. Based on our analysis we identified 3 roles that support new faculty entering DBER. These roles are the peer, the subject matter expert, and the project manager. 

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5. Authorship agreements benefit researchers and research culture

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   Rasmussen, Lisa M; Banks, George; Demeter, Elise; Holladay-Sandidge, Holly D; McBride, Andrew; Hall-Hertel, Katherine; Tonidandel, Scott (November 2023, Nature Human Behaviour)