University Library, University of Illinois at Urbana-Champaign

This dataset provides a 50-state (and DC) survey of state-level tax credits modeled after the federal New Markets Tax Credit program, including summaries of the tax credit amount and credit periods, key definitions, eligibility criteria, application process, and degree of conformity to federal law.

This dataset provides a 50-state (and DC) survey of state-level Opportunity Zones laws, including summaries of states' Opportunity Zone tax preferences, supplemental tax preferences, and approach to Opportunity Zones conformity. Data was last updated on January 14, 2022.

The file “Fla.fasta”, comprising 10526 positions, is the concatenated amino acid alignments of 51 orthologues of 182 bacterial strains. It was used for the maximum likelihood and maximum parsimony analyses of Flavobacteriales. Bacterial species names and strains were used as the sequence names, host names of insect endosymbionts were shown in brackets. The file “16S.fasta” is the alignment of 233 bacterial 16S rRNA sequences. It contains 1455 positions and was used for the maximum likelihood analysis of flavobacterial insect endosymbionts. The names of endosymbiont strains were replaced by the name of their hosts. In addition to the species names, National Center for Biotechnology Information (NCBI) accession numbers were also indicated in the sequence names (e.g., sequence “Cicadellidae_Deltocephalinae_Macrostelini_Macrosteles_striifrons_AB795320” is the 16S rRNA of Macrosteles striifrons (Cicadellidae: Deltocephalinae: Macrostelini) with a NCBI accession number AB795320). The file “Sulcia_pep.fasta” is the concatenated amino acid alignments of 131 orthologues of “Candidatus Sulcia muelleri” (Sulcia). It contains 41970 positions and presents 101 Sulcia strains and 3 Blattabacterium strains. This file was used for the maximum likelihood analysis of Sulcia. The file “Sulcia_nucleotide.fasta” is the concatenated nucleotide alignment corresponding to the sequences in “Sulcia_pep.fasta” but also comprises the alignment of 16S rRNA. It has 127339 positions and was used for the maximum likelihood and maximum parsimony analyses of Sulcia. Individual gene alignments (16S rRNA and 131 orthologues of Sulcia and Blattabacterium) are deposited in the compressed file “individual_gene_alignments.zip”, which were used to construct gene trees for multispecies coalescent analysis. The names of Sulcia strains were replaced by the name of their hosts in “Sulcia_pep.fasta”, “Sulcia_nucleotide.fasta” and the files in “individual_gene_alignments.zip”. In all the alignment files, gaps are indicated by “-”.

We developed and delivered in-person training at local health department offices in six of the seven Illinois Department of Public Health “health regions” between April-May of 2019. Pre-, post-, and six-month follow-up questionnaires on knowledge, attitudes, and practices with regards to tick surveillance were administered to training participants.

*Updates for this V3: added a few more records and rearranged the sequence of the tables in order to support our new paper "Evaluation of Indirect and Direct Scoring Methods to Relate Biochemical Soil Quality Indicators to Ecosystem Services" accepted by the Soil Science Society of America Journal. We summarize peer reviewed literature reporting associations between for three soil quality indicators (SQIs) (β-glucosidase (BG), fluorescein diacetate (FDA) hydrolysis, and permanganate oxidizable carbon (POXC)) and crop yield and greenhouse gas emissions. Peer-reviewed articles published between January of 1990 and May 2018 were searched using the Thomas Reuters Web of Science database (Thomas Reuters, Philadelphia, Pennsylvania) and Google Scholar to identify studies reporting results for: “β-glucosidase”, “permanganate oxidizable carbon”, “active carbon”, “readily oxidizable carbon”, or “fluorescein diacetate hydrolysis”, together with one or more of the following: “crop yield”, “productivity”, “greenhouse gas’, “CO2”, “CH4”, or “N2O”. Meta-data for records include the following descriptor variables and covariates useful for scoring function development: 1) identifying factors for the study site (location, duration of the experiment), 2) soil textural class, pH, and SOC, 3) depth of soil sampling, 4) units used in published works (i.e.: equivalent mass, concentration), 5) SQI abundances and measured ecosystem functions, and 6) summary statistics for correlation between SQIs and functions (yield and greenhouse gas emissions). *Note: Blank values in tables are considered unreported data.

These data were collected in 2018 and 2019 at the University of Illinois Energy Farm (N 40.063607, W 88.206926). During each growing season, bulk and rhizosphere soil were collected from replicate Sorghum bicolor nitrogen use efficiency trial plots at three separate time points (approximately July 1, August 1, and September 1). We measured soil moisture, pH, soil nitrate and ammonium, potential nitrification, potential denitrification, and extracted and sequenced the V4 region of the 16S rRNA gene for microbial community analysis. All microbial sequence data is archived in the National Center for Biotechnology Information’s (NCBI) Sequence Read Archive (accession number SRP326979, project number PRJNA741261).

An online knowledge, attitudes, and practices survey on ticks and tick-borne diseases was distributed to veterinary professionals in Southern and Central Illinois during summer and fall 2020. These are the raw data associated with that survey and the survey questions used. * NOTE: "age" and "gender" variables were removed from the data to protect participants.

This dataset contains simulation results from PartMC-MOSAIC-CAPRAM used in the article ”Eval- uating the impacts of cloud processing on resuspended aerosol particles after cloud evaporation using a particle-resolved model”. In this V2, there are eight folders: one for urban plume simulation to provide the initial particle population for cloud processing, the other four folders are for the four cloud cycles simulated and the last two are for the coagulation cases. Within the urban plume simulation, there are 25 NetCDF files hourly output from PartMC-MOSAIC simulations containing the gas and particle information. Within the four cloud cycle folders, there are 25 subdirectories that contain the cloud processing results for aerosol population from urban plume environment. For each subdirectory, there are 31 NetCDF files out- put every minute from PartMC-MOSAIC-CAPRAM simulations containing aerosol and gas information after aqueous chemistry. Another two folders are for the cases considering Brownian coagulation and sedimentation coalescence. Each contained 93 NetCDF files, produced from repeating the 30-minutes simulations for three times to consider the coagulation randomness. The low polluted case folder includes the simulated cloud processing results for 25 urban plume cases with less aerosol number concentration. This dataset was used to investigate the effects of cloud processing on aerosol mixing state and CCN properties.

The dataset shows all poison frogs (superfamily Dendrobatoidea) in private U.S. collections during 1990–2020. For each species and color morph, there is a date of arrival, the way it arrived in U.S. collections, and detailed notes related to its presence in the pet trade.

Data from an a field experiment at El Velo, Chiriqui, Republic of Panama. Data contain information about functional traits of seedlings growing in different treatments including type of forest, nitrogen addition and organic matter.

Shared dataset consists of 16S sequencing data of microbial communities. Each community is composed of heterotrophic bacteria derived from one of two soil samples and the model algae Chlamydomonas reinhardtii. Each comunity was placed in a materially closed environment with an initial supply of carbon in the media and subjected to light-dark cycles. The closed microbial ecosystems (CES) survived via carbon cycling. Each CES was subjected to rounds of dilution, after which the community was sequenced (data provided here). The shared dataset allowed us to conclude that CES consistently self-assembled to cycle carbon (data not provided) via conserved metabolic capabilites (data not provided) dispite differences in taxonomic composition (data provided). --------------------------- Naming convention: [soil sample = A or B][CES replicate = 1,2,3, or 4]_[round number = 1,2,3,or 4]_[reverse read = R or forward read = F]_filt.fastq Example -- A1_r1_F_filt.fastq means soil sample A, CES replicate 1, end of round1, forward read

This dataset contains re-estimated gene trees from the ASTRAL-II [1] simulated datasets. The re-estimated variants of the datasets are called MC6H and MC11H -- they are derived from the MC6 and MC11 conditions from the original data (the MC6 and MC11 names are given by ASTRID [2]). The uploaded files contain the sequence alignments (half-length their original alignments), and the re-estimated species trees using FastTree2. Note: - "mc6h.tar.gz" and "mc11h.tar.gz" contain the sequence alignments and the re-estimated gene trees for the two conditions - the sequence alignments are in the format "all-genes.phylip.splitted.[i].half" where i means that this alignment is for the i-th alignment of the original dataset, but truncating the alignment halving its length - "g1000.trees" under each replicate contains the newline-separated re-estimated gene trees. The gene trees were estimated from the above described alignments using FastTree2 (version 2.1.11) command "FastTree -nt -gtr" [1]: Mirarab, S., & Warnow, T. (2015). ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes. Bioinformatics, 31(12), i44-i52. [2]: Vachaspati, P., & Warnow, T. (2015). ASTRID: accurate species trees from internode distances. BMC genomics, 16(10), 1-13.

This data set contains survey results from a 2021 survey of University of Illinois University Library employees conducted as part of the Becoming A Trans Inclusive Library Project to evaluate the awareness of University of Illinois faculty, staff, and student employees regarding transgender identities, and to assess the professional development needs of library employees to better serve trans and gender non-conforming patrons. The survey instrument is available in the IDEALS repository: http://hdl.handle.net/2142/110080.

This data set contains survey results from a 2021 survey of University of Illinois University Library patrons who identify as transgender or gender non-conforming conducted as part of the Becoming a Trans Inclusive Library Project to assess the experiences of transgender patrons seeking information and services in the University Library. Survey instruments are available in the IDEALS repository: http://hdl.handle.net/2142/110081.

This dataset contains all the data for the results section in the study presented in the paper entitled "Chemistry Across Multiple Phases (CAMP) version 1.0: An integrated multi-phase chemistry mode" submitted to Geoscientific Model Development (GMD). In this paper, two sets of simulations were run to test CAMP with this results included here. This consists of (1) box model inputs and outputs presented in Section 4.2 for modal, binned and particle-resolved simulations to compare the application of identical chemical mechanisms to different aerosol representations and (2) the 3D Eulerian output presented in Section 4.3.

This dataset includes the source data for Figures 1-4 and supplementary figures 1-10 for the manuscript "Kinetic and structural mechanism for DNA unwinding by a non-hexameric helicase".

Bigheaded carp were collected from the Illinois and Des Plaines Rivers, parts of the Illinois Waterway, from May to November 2018. A total of 93 fish were collected during sampling for a study comprised of 40 females, 41 males, and 12 unsexed fish. GC/MS metabolite profiling analysis detected 180 compounds. Livers from carp at the leading edge had differences in energy use and metabolism, and suppression of protective mechanisms relative to downstream fish; differences were consistent across time. This body of work provides evidence that water quality is linked to carp movement in the Illinois River. As water quality in this region continues to improve, consideration of this impact on carp spread is essential to protect the Great Lakes.

This dataset contains daily and hourly temperature measurements in twenty different bat box designs deployed in central Indiana, USA from May to September 2018. Daily and hourly environmental data (temperature, solar radiation, wind speed and direction) are also included for days and hours sampled. Bat box temperature data were reclassified to cool (</= 30°C), permissive (30.1–39.9°C), and stressful (>/= 40°C) categories according to known temperature tolerances of temperate-zone bats.

Atomic oxygen data from SCIAMACHY, for the MLT, 2002-2012, averaged for 26, 14 day periods, beginning January 1.

Atomic oxygen densities in the MLT, averaged for 2002-2018 for 26, 14 day periods, beginning January 1.

Information on the location, dimensions, time of treefall or death, decay state, wood nutrient, wood pH and wood density data, and soil moisture, slope, distance from forest edge and soil nutrient data associated with the publication "Interspecific wood trait variation predicts decreased carbon residence time in changing forests" authored by Sierra Perez, Jennifer Fraterrigo, and James Dalling. ** <b>Note:</b> Blank cells indicate that no data were collected.

This is the 5 states 5000 cells synthetic expression file we used for validation of SimiC, a single cell gene regulatory network inference method with similarity constraints. Ground truth GRNs are stored in Numpy array format, and expression profiles of all states combined are stored in Pandas DataFrame in format of Pickle files.

Drainage network analysis is fundamental to understanding the characteristics of surface hydrology. Based on elevation data, drainage network analysis is often used to extract key hydrological features like drainage networks and streamlines. Limited by raster-based data models, conventional drainage network algorithms typically allow water to flow in 4 or 8 directions (surrounding grids) from a raster grid. To resolve this limitation, this paper describes a new vector-based method for drainage network analysis that allows water to flow in any direction around each location. The method is enabled by rapid advances in Light Detection and Ranging (LiDAR) remote sensing and high-performance computing. The drainage network analysis is conducted using a high-density point cloud instead of Digital Elevation Models (DEMs) at coarse resolutions. Our computational experiments show that the vector-based method can better capture water flows without limiting the number of directions due to imprecise DEMs. Our case study applies the method to Rowan County watershed, North Carolina in the US. After comparing the drainage networks and streamlines detected with corresponding reference data from US Geological Survey generated from the Geonet software, we find that the new method performs well in capturing the characteristics of water flows on landscape surfaces in order to form an accurate drainage network. This dataset contains all the code, notebooks, datasets used in the study conducted for the research publication titled " A Vector-Based Method for Drainage Network Analysis Based on LiDAR Data ". ## What's Inside A quick explanation of the components * `A Vector Approach to Drainage Network Analysis Based on LiDAR Data.ipynb` is a notebook for finding the drainage network based on LiDAR data *`Picture1.png` is a picture representing the pseudocode of our new algorithm * HPC` folder contains codes for running the algorithm with sbatch in HPC ** `execute.sh` is a bash script file that use sbatch to conduct large scale analysis for the algorithm ** `run.sh` is a bash script file that calls the script file `execute.sh` for large scale calculation for the algorithm ** `run.py` includes the codes implemented for the algorithm * `Rowan Creek Data` includes data that are used in the study ** `3_1.las` and `3_2.las ` are the LiDAR data files that is used in our analysis presented in the paper. Users may use this data file to reproduce our results and may replace it with their own LiDAR file to run this method over different areas ** `reference` folder includes reference data from USGS *** `reference_3_1.tif` and `reference_3_2.tif` are reference data for the drainage system analysis retrieved from USGS.

This data set describes temperature, dissolved oxygen, and secchi depth in 1-m interval profiles in the deepest point in 10 Illinois reservoirs between the years 1995 and 2016.

This dataset contains the ClonalKinetic dataset that was used in SimiC and its intermediate results for comparison. The Detail description can be found in the text file 'clonalKinetics_Example_data_description.txt' and 'ClonalKinetics_filtered.DF_data_description.txt'. The required input data for SimiC contains: 1. ClonalKinetics_filtered.clustAssign.txt => cluster assignment for each cell. 2. ClonalKinetics_filtered.DF.pickle => filtered scRNAseq matrix. 3. ClonalKinetics_filtered.TFs.pickle => list of driver genes. The results after running SimiC contains: 1. ClonalKinetics_filtered_L10.01_L20.01_Ws.pickle => inferred GRNs for each cluster 2. ClonalKinetics_filtered_L10.01_L20.01_AUCs.pickle => regulon activity scores for each cell and each driver gene. <b>NOTE:</b> “ClonalKinetics_filtered.rds” file which is mentioned in “ClonalKinetics_filtered.DF_data_description.txt” is an intermediate file and the authors have put all the processed in the pickle/txt file as described in the filtered data text.