{ "_schema": "https://data.nist.gov/od/dm/nerdm-schema/v0.7#", "@context": [ "https://data.nist.gov/od/dm/nerdm-pub-context.jsonld", { "@base": "ark:/88434/mds2-4229" } ], "@type": [ "nrdp:DataPublication", "nrdp:PublicDataResource", "dcat:Dataset" ], "_extensionSchemas": [ "https://data.nist.gov/od/dm/nerdm-schema/pub/v0.7#/definitions/PublicDataResource" ], "@id": "ark:/88434/mds2-4229", "ediid": "ark:/88434/mds2-4229", "version": "1.1.0", "doi": "doi:10.18434/mds2-4229", "title": "NIST-Curated Soybean Genomic Variant and Phenotype Benchmark Data for the PETs Testbed", "contactPoint": { "fn": "Gary Howarth", "hasEmail": "mailto:gary.howarth@nist.gov" }, "modified": "2026-06-10 00:00:00", "status": "available", "landingPage": "https://data.nist.gov/od/id/mds2-4229", "description": [ "This repository contains phenotype and genotype data of soybeans (Glycine max) prepared as benchmark data for the NIST Privacy-Preserving Federated Learning (PPFL) Testbed, a component of the NIST PETs Testbeds. See https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/pets-testbed for more information on the Testbeds.", "The Readme in this repository provides a detailed description of the data.", "The data are designed as test data for the Privacy Enhancing Technology Testbed. We include raw source files and processed, machine-learning ready files. The samples come from the United States Department of Agriculture (USDA), Soybean Germplasm Collection with 18348 accessions represented here. The collection was genotyped with the SoySNP50K BeadChip containing more than 50K single-nucleotide polymorphisms (SNPs). Each germplasm sample has phenotypes reported by the USDA Germplasm Resources Information Network. Detailed origins of the data are described below. A variety of phenotype data is available for each germplasm including soybean yield, plant height, seed coat color, and many others. Not all phenotype data are available for all plants. The seed coat color task has 100 percent coverage (18348), all others have less. The file `./raw_data_and_processing_scripts/Phenotype_data_dictionary.csv` reports phenotype availability and coverage.", "These data are derived from two sources: the [Soybase Project](https://www.soybase.org), which collects, organizes, and maintains Soybean genomics data; and the [Germplasm Resources Information Network](https://www.ars-grin.gov), a seed and phenotype repository for crop research hosted by the Agricultural Research Service of the U.S. Department of Agriculture.", "If you use these data, please cite us as well as the following work.", "Brown AV, Conners SI, Huang W, Wilkey AP, Grant D, Weeks NT, Cannon SB, Graham MA, and Nelson, RT. A new decade and new data at SoyBase, the USDA-ARS soybean genetics and genomics database. Nucl. Acids Res. (2021) 49 (D1): D1496-D15012. doi: 10.1093/nar/gkaa1107", "Song Q, Hyten DL, Jia G, Quigley CV, Fickus EW, Nelson RL, and Cregan PB. (2015). Fingerprinting soybean germplasm and its utility in genomic research. G3: Genes, Genomes, Genetics, 5(10), 1999-2006. doi: 10.1534/g3.115.025338", "Espina MJC, Lovell JT, Jenkins J, Shu S, Sreedasyam A, Jordan BD, Webber J, Boston L, Bruna T, Talag J, Goodstein D, Grimwood J, Stacey G, Cannon SB, Lorenz AJ, Schmutz J, and Stupar, R. M. (2024). Assembly, comparative analysis, and utilization of a single haplotype reference genome for soybean. The Plant Journal, 120(3), 1221\u20131235. doi: 10.1111/tpj.17026" ], "keyword": [ "Genomics", "machine learning", "federated learning", "privacy" ], "topic": [ { "@type": "Concept", "scheme": "https://data.nist.gov/od/dm/nist-themes/v1.1", "tag": "Information Technology: Privacy" }, { "@type": "Concept", "tag": "Information Technology:Cybersecurity and Privacy" }, { "@type": "Concept", "scheme": "https://data.nist.gov/od/dm/nist-themes/v1.1", "tag": "Bioscience: Genomics" }, { "@type": "Concept", "tag": "Artificial Intelligence:Applied AI" }, { "@type": "Concept", "tag": "Artificial Intelligence:AI measurement and evaluation" }, { "@type": "Concept", "tag": "Artificial Intelligence:Trustworthy and responsible AI" }, { "@type": "Concept", "tag": "Artificial Intelligence:Machine learning" } ], "accessLevel": "public", "license": "https://www.nist.gov/open/license", "publisher": { "name": "National Institute of Standards and Technology", "@type": "org:Organization" }, "language": [ "en" 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