Chemically intensive crop production depletes wildlife food resources, hinders animal development, health, survival, and reproduction, and it suppresses wildlife immune systems, facilitating emergence of infectious diseases with excessive mortality rates. Gut microbiota is crucial for wildlife’s response to environmental stressors. Its composition and functionality are sensitive to diet changes and environmental pollution associated with modern crop production. In the study entitled Exposure to crop production alters cecal prokaryotic microbiota, inflates virulome and resistome in wild prairie grouse we use shotgun metagenomics to demonstrate that exposure to modern crop production detrimentally affects cecal microbiota of sharp-tailed grouse (Tympanuchus phasianellus) and greater prairie chickens (T. cupido). Although microbiota richness was greater in exposed than in unexposed birds, some beneficial bacteria dropped out of exposed birds’ microbiota or declined and were replaced by potential pathogens. Exposed birds also had higher richness and load of virulome and resistome than unexposed birds. This release contains eight tables of scientific data collected for and derived during this study. Table A1 presents sample IDs, National Museum of Natural History voucher numbers, National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) accession numbers, collection date and locality information, host species, exposure to crop production, sex, age, crop content, weight, wing span, host/exposure group, sequencing depth, prokaryotic strain richness, Chao1 and Shannon indexes, virulome richness, Shannon index, and load, resistome richness, Shannon index, and load, resistome group, Cumulative Sum Scaled (CSS)-normalized and log2-transformed abundance scores for Synergistes jonesii, Coprobacter fastidiosus NSB1, Alistipes_obesi, Oscillibacter sp. ER4, Shigella dysenteriae, and Ruminococcus gnavus, liver wet and dry weight, liver water weight (g) and %, liver sample dry and wet mass, imidacloprid and bifenthrin dry and wet content. Table A.2 presents cumulative sum scaled (CSS)-normalized and log2-transformed abundance scores for each prokaryotic strain in each cecum sample. Table A.3 Presents a matrix of pairwise Bray-Curtis dissimilarities in microbiota composition among samples. Table A.4 presents taxa identified by the linear discriminant analysis (LDA) effect size (LEfSe) algorithm as the most likely responsible for compositional differences in microbiota between unexposed birds and those exposed to crop production, their log LDA scores, and false discovery rate (FDR) adjusted p-values. Table A.5 presents virulence factors and their abundance in each cecum sample. Table A.6 presents a matrix of pairwise Bray-Curtis dissimilarities in virulome composition among samples. Table A.7 presents antibiotic resistance genes and their abundance in each cecum sample. Table A.8 presents a matrix of pairwise Bray-Curtis dissimilarities in resistome composition among samples.