Illinois Data Bank Dataset Search Results
Results
published:
2019-08-30
This dataset includes the data from an analysis of bobcat harvest data with particular focus on the relationship between catch-per-unit-effort and population size. The data relate to bobcat trapper and hunter harvest metrics from Wisconsin and include two RDS files which can be open in the software R using the readRDS() function.
keywords:
bobcat; catch-per-unit-effort; CPUE; harvest; Lynx rufus; wildlife management; trapper; hunter
published:
2021-07-21
Rozansky, Zachary; Larson, Eric; Taylor, Christopher
(2021)
This dataset contains 1 CSV file: RozanskyLarsonTaylorMsat.csv which contains microsatellite fragment lengths for Virile and Spothanded Crayfish from the Current River watershed of Missouri, U.S., and complimentary data, including assignments to species by phenotype and COI sequence data, GenBank accession numbers for COI sequence data, study sites with dates of collection and geographic coordinates, and Illinois Natural History Survey (INHS) Crustacean Collection lots where specimens are stored.
keywords:
invasive species; hybridization; crayfishes; streams; freshwater; Cambaridae; virile crayfish; spothanded crayfish; Missouri; Current River; Ozark National Scenic Riverways
published:
2020-08-31
Chen, Luoye; Khanna, Madhu; Debnath, Deepayan; Zhong, Jia; Ferin, Kelsie; VanLoocke, Andy
(2020)
This dataset contains BEPAM model code and input data to replicate the outcomes for "The Economic and Environmental Costs and Benefits of the Renewable Fuel Standard".
The dataset consists of:
(1) The replication codes and data for the BEPAM model. The code file is named as output.gms. (BEPAM-Social cost model-ERL.zip)
(2) Simulation results from the BEPAM model (BEPAM_Simulation_Results.csv)
* Item (1) is in GAMS format. Item (2) is in text format.
keywords:
Social Cost of Carbon; Social Cost of Nitrogen; Cost-Benefit Analysis; Indirect Land-Use Change
published:
2017-09-19
Nute, Michael; Jed, Chou; Molloy, Erin K.; Warnow, Tandy
(2017)
published:
2018-04-26
GBS data from soybean lines carrying introgressions from Glycine tomentella. This project is led by Dr. Randy Nelson, USDA scientist at the University of Illinois. Fastq files contain raw Illumina data. Txt files are keyfiles containing barcodes for each genetic entity.
published:
2018-02-22
Christensen, Sarah; Molloy, Erin K; Vachaspati, Pranjal; Warnow, Tandy
(2018)
Datasets used in the study, "OCTAL: Optimal Completion of Gene Trees in Polynomial Time," under review at Algorithms for Molecular Biology. Note: DS_STORE file in 25gen-10M folder can be disregarded.
keywords:
phylogenomics; missing data; coalescent-based species tree estimation; gene trees
published:
2021-05-14
Miller, Jim; Czesny, Sergiusz; Dai, Qihong; Ellis, James; Iverson, Louis; Matthews, Jeff; Roswell, Charlie; Suski, Cory; Taft, John; Ward, Mike
(2021)
Please cite as: Jim Miller, Sergiusz Czesny, Qihong Dai, James Ellis, Louis Iverson, Jeff Matthews, Charles Roswell, Cory Suski, John Taft, and Mike Ward. 2021. “Climate Change Impacts on Ecosystems: Scientific and Common Species Names”.
keywords:
Scientific names; Common names; Illinois species
published:
2018-05-01
GBS data for G. max x G. soja crosses, a project led by Dr. Randy Nelson.
published:
2020-01-28
Miao, Guofang; Guan, Kaiyu
(2020)
This dataset includes two data files that provide the time series (Jul. - Sep. 2017) data of sun-induced chlorophyll fluorescence (SIF_760) collected under sunny conditions at two maize sites (one rainfed and the other irrigated) in Nebraska in 2017.
Data contain 392 SIF_760 records at the rainfed site and 707 records at the irrigated site. The timestamp uses local standard time. Data are available for the sunny conditions from 8 am to 5 pm (corresponding to 9 am to 6 pm local time) throughout the study period.
keywords:
sun-induced chlorophyll fluorescence (SIF); maize; gross primary production(GPP); light use efficiency(LUE); SIF yield
planned publication date:
2026-03-01
Sundararajan, Sumashini; Chamoli, Gauranshi; Dalling, James; Krishnadas, Meghna
(2026)
This dataset contains seed germination data from two inoculation experiments involving two fig species, Ficus beddomei and Ficus callosa, found in the tropical forests of the Western Ghats, India, and fungal taxa that were isolated from them. The file "first_inoculation_expt_Nov_2025" contains germination data for screening of select fungal taxa for their effects on the two fig species. The file "serial_inoculation_expt_Nov_2025" contains germination data from a serial inoculation experiment involving successive inoculation of seeds with an endophytic followed by a pathogenic fungal taxon.
keywords:
Ficus; seeds; fungi; germination; endophyte; pathogen
planned publication date:
2026-03-01
Edmonds, Devin A.; Fanomezantsoa, Rebecca E.; Rabibisoa, Nirhy H. C.; Roberts, Sam H.
(2026)
This dataset contains ecological and demographic data for William’s bright‑eyed frog (Boophis williamsi), a critically endangered amphibian restricted to the Ankaratra Massif in Madagascar’s central highlands. Field surveys were conducted between September 2018 – March 2019 and July 2021 across ten 100‑m stream transects to estimate abundance and identify habitat associations for both tadpoles and adult frogs. Data include repeated counts of individuals and associated habitat variables (e.g., canopy cover, substrate type, stream depth, discharge, and temperature). Abundance was estimated using N‑mixture models implemented in R (version 4.3.1) with the ubms package, with separate models for tadpoles and frogs to account for differences in detection probability. The dataset consists of multiple CSV files capturing microhabitat, environmental variables, and raw survey count data (y_frogs.csv and y_tadpoles.csv) and an R script (boophis_abundance.R) used for model fitting. The dataset was compiled for an article accepted in the Herpetological Journal by the British Herpetological Society and is intended to support long‑term monitoring and conservation planning for B. williamsi and other threatened amphibians in Madagascar.
keywords:
amphibian conservation; biodiversity conservation; detection probability; endangered species; N-mixture model