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1. A Query Understanding Framework for Earth Data Discovery

   https://doi.org/10.3390/app10031127  

   Li, Yun; Jiang, Yongyao; Goldstein, Justin C.; Mcgibbney, Lewis J.; Yang, Chaowei (February 2020, Applied Sciences)

   One longstanding complication with Earth data discovery involves understanding a user’s search intent from the input query. Most of the geospatial data portals use keyword-based match to search data. Little attention has focused on the spatial and temporal information from a query or understanding the query with ontology. No research in the geospatial domain has investigated user queries in a systematic way. Here, we propose a query understanding framework and apply it to fill the gap by better interpreting a user’s search intent for Earth data search engines and adopting knowledge that was mined from metadata and user query logs. The proposed query understanding tool contains four components: spatial and temporal parsing; concept recognition; Named Entity Recognition (NER); and, semantic query expansion. Spatial and temporal parsing detects the spatial bounding box and temporal range from a query. Concept recognition isolates clauses from free text and provides the search engine phrases instead of a list of words. Name entity recognition detects entities from the query, which inform the search engine to query the entities detected. The semantic query expansion module expands the original query by adding synonyms and acronyms to phrases in the query that was discovered from Web usage data and metadata. The four modules interact to parse a user’s query from multiple perspectives, with the goal of understanding the consumer’s quest intent for data. As a proof-of-concept, the framework is applied to oceanographic data discovery. It is demonstrated that the proposed framework accurately captures a user’s intent. 

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2. Sequencing data discovery with MetaSeek

   https://doi.org/10.1093/bioinformatics/btz499  

   Hoarfrost, Adrienne; Brown, Nick; Brown, C Titus; Arnosti, Carol; Wren, Jonathan (June 2019, Bioinformatics)

   Abstract Summary Sequencing data resources have increased exponentially in recent years, as has interest in large-scale meta-analyses of integrated next-generation sequencing datasets. However, curation of integrated datasets that match a user’s particular research priorities is currently a time-intensive and imprecise task. MetaSeek is a sequencing data discovery tool that enables users to flexibly search and filter on any metadata field to quickly find the sequencing datasets that meet their needs. MetaSeek automatically scrapes metadata from all publicly available datasets in the Sequence Read Archive, cleans and parses messy, user-provided metadata into a structured, standard-compliant database and predicts missing fields where possible. MetaSeek provides a web-based graphical user interface and interactive visualization dashboard, as well as a programmatic API to rapidly search, filter, visualize, save, share and download matching sequencing metadata. Availability and implementation The MetaSeek online interface is available at https://www.metaseek.cloud/. The MetaSeek database can also be accessed via API to programmatically search, filter and download all metadata. MetaSeek source code, metadata scrapers and documents are available at https://github.com/MetaSeek-Sequencing-Data-Discovery/metaseek/. 

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3. Direct, Orienting, and Scenic Paths: How Users Navigate Search in a Research Data Archive

   https://doi.org/10.1145/3576840.3578275  

   Lafia, Sara; Million, A.J.; Hemphill, Libby (March 2023, Proceedings of the ACM SIGIR Conference On Human Information Interaction and Retrieval)

   Social scientists increasingly share data so others can evaluate, replicate, and extend their research. To understand the process of data discovery as a precursor to data use, we study prospective users’ interactions with archived data. We gathered data for 98,000 user sessions initiated at a large social science data archive, the Inter-university Consortium for Political and Social Research (ICPSR). Our data reflect four years (2012-16) of users’ interactions with archival resources, including a data catalog, study-level metadata, variables, and publications that cite nearly 10,000 datasets. We constructed a network of user interactions linking website landing (e.g., site entrances) to exit pages, from which we identified three types of paths that users take through the research data archive: direct, orienting, and scenic. We also interpreted points of failure (e.g., drop-offs) and recurring behaviors (e.g., sensemaking) that support or impede data discovery along search paths. We articulate strategies that users adopt as they navigate data search and suggest ways to enhance the accessibility of data, metadata, and the systems that organize each. 

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4. Data, not documents: Moving beyond theories of information‐seeking behavior to advance data discovery

   https://doi.org/10.1002/asi.24962  

   Million, Anthony J; York, Jeremy; Lafia, Sara; Hemphill, Libby (November 2024, Journal of the Association for Information Science and Technology)

   Abstract Many theories of human information behavior (HIB) assume that information objects are in text document format. This paper argues four important HIB theories are insufficient for describing users' search strategies for data because of assumptions about the attributes of objects that users seek. We first review and compare four HIB theories: Bates'berrypicking, Marchionni'selectronic information search, Dervin'ssense‐making, and Meho and Tibbo'ssocial scientist information‐seeking. All four theories assume that information‐seekers search for text documents. Next, we compare these theories to search behavior by analyzing Google Analytics data from the Inter‐university Consortium for Political and Social Research (ICPSR). Users took direct, scenic, and orienting paths when searching for data. We also interviewed ICPSR users (n = 20), and they said they needed dataset documentation and contextual information to find data. However, Dervin'ssense‐makingalone cannot explain the information‐seeking behaviors that we observed. Instead, what mattered most were object attributes determined by the type of information that users sought (i.e., data, not documents). We conclude by suggesting an alternative frame for building user‐centered data discovery tools. 

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5. A minimum data standard for wildlife disease research and surveillance

   https://doi.org/10.1038/s41597-025-05332-x  

   Schwantes, Collin J; Sánchez, Cecilia A; Stevens, Tess; Zimmerman, Ryan; Albery, Greg; Becker, Daniel J; Brookson, Cole B; Kading, Rebekah C; Keiser, Carl N; Khandelwal, Shashank; et al (December 2025, Engineering sciences data Composites)

   Abstract Rapid and comprehensive data sharing is vital to the transparency and actionability of wildlife infectious disease research and surveillance. Unfortunately, most best practices for publicly sharing these data are focused on pathogen determination and genetic sequence data. Other facets of wildlife disease data – particularly negative results – are often withheld or, at best, summarized in a descriptive table with limited metadata. Here, we propose a minimum data and metadata reporting standard for wildlife disease studies. Our data standard identifies a set of 40 data fields (9 required) and 24 metadata fields (7 required) sufficient to standardize and document a dataset consisting of records disaggregated to the finest possible spatial, temporal, and taxonomic scale. We illustrate how this standard is applied to an example study, which documented a novel alphacoronavirus found in bats in Belize. Finally, we outline best practices for how data should be formatted for optimal re-use, and how researchers can navigate potential safety concerns around data sharing. 

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