Datasets

These datasets provide the basis of our analysis in the paper - The Potential Impact of a Clean Energy Society On Air Quality. All datasets here are from the model output (CAM4-chem). All the simulations were run to steady-state and only the outputs used in the analysis are archived here.

This dataset was developed as part of a study that examined the correlational relationships between local journal authorship, local and external citation counts, full-text downloads, link-resolver clicks, and four global journal impact factor indices within an all-disciplines journal collection of 12,200 titles and six subject subsets at the University of Illinois at Urbana-Champaign (UIUC) Library. While earlier investigations of the relationships between usage (downloads) and citation metrics have been inconclusive, this study shows strong correlations in the all-disciplines set and most subject subsets. The normalized Eigenfactor was the only global impact factor index that correlated highly with local journal metrics. Some of the identified disciplinary variances among the six subject subsets may be explained by the journal publication aspirations of UIUC researchers. The correlations between authorship and local citations in the six specific subject subsets closely match national department or program rankings. All the raw data used in this analysis, in the form of relational database tables with multiple columns. Can be opned using MS Access. Description for variables can be viewed through "Design View" (by right clik on the selected table, choose "Design View"). The 2 PDF files provide an overview of tables are included in each MDB file. In addition, the processing scripts and Pearson correlation code is available at <a href="https://doi.org/10.13012/B2IDB-0931140_V1">https://doi.org/10.13012/B2IDB-0931140_V1</a>.

To generate the bibliographic and survey data to support a data reuse study conducted by several Library faculty and accepted for publication in the Journal of Academic Librarianship, the project team utilized a series of web-based online scripts that employed several different endpoints from the Scopus API. The related dataset: "Data for: An Examination of Data Reuse Practices within Highly Cited Articles of Faculty at a Research University" contains survey design and results. <br /> 1) <b>getScopus_API_process_dmp_IDB.asp</b>: used the search API query the Scopus database API for papers by UIUC authors published in 2015 -- limited to one of 9 pre-defined Scopus subject areas -- and retrieve metadata results sorted highest to lowest by the number of times the retrieved articles were cited. The URL for the basic searches took the following form: https://api.elsevier.com/content/search/scopus?query=(AFFIL%28(urbana%20OR%20champaign) AND univ*%29) OR (AF-ID(60000745) OR AF-ID(60005290))&apikey=xxxxxx&start=" & nstart & "&count=25&date=2015&view=COMPLETE&sort=citedby-count&subj=PHYS<br /> Here, the variable nstart was incremented by 25 each iteration and 25 records were retrieved in each pass. The subject area was renamed (e.g. from PHYS to COMP for computer science) in each of the 9 runs. This script does not use the Scopus API cursor but downloads 25 records at a time for up to 28 times -- or 675 maximum bibliographic records. The project team felt that looking at the most 675 cited articles from UIUC faculty in each of the 9 subject areas was sufficient to gather a robust, representative sample of articles from 2015. These downloaded records were stored in a temporary table that was renamed for each of the 9 subject areas. <br /> 2) <b>get_citing_from_surveys_IDB.asp</b>: takes a Scopus article ID (eid) from the 49 UIUC author returned surveys and retrieves short citing article references, 200 at a time, into a temporary composite table. These citing records contain only one author, no author affiliations, and no author email addresses. This script uses the Scopus API cursor=* feature and is able to download all the citing references of an article 200 records at a time. <br /> 3) <b>put_in_all_authors_affil_IDB.asp</b>: adds important data to the short citing records. The script adds all co-authors and their affiliations, the corresponding author, and author email addresses. <br /> 4) <b>process_for_final_IDB.asp</b>: creates a relational database table with author, title, and source journal information for each of the citing articles that can be copied as an Excel file for processing by the Qualtrics survey software. This was initially 4,626 citing articles over the 49 UIUC authored articles, but was reduced to 2,041 entries after checking for available email addresses and eliminating duplicates.

Example scripts and configuration files needed to perform select simulations described in the manuscript "Percolation transition prescribes protein size-specific barrier to passive transport through the nuclear pore complex."

These data were used to make a predictive model of when ornate box turtles (Terrapene ornata) are likely to be above ground and at risk from fire. The data were generated using shell temperatures, soil temperatures at 0.35 m deep from known overwintering sites, and the spring and fall soil temperature inversion dates during 2019–2022 to infer if 26 individual radio-tracked turtles were above or below ground at three sites in Illinois.

This archive contains all the alignments and trees used in the HIPPI paper [1]. The pfam.tar archive contains the PFAM families used to build the HMMs and BLAST databases. The file structure is: ./X/Y/initial.fasttree ./X/Y/initial.fasta where X is a Pfam family, Y is the cross-fold set (0, 1, 2, or 3). Inside the folder are two files, initial.fasta which is the Pfam reference alignment with 1/4 of the seed alignment removed and initial.fasttree, the FastTree-2 ML tree estimated on the initial.fasta. The query.tar archive contains the query sequences for each cross-fold set. The associated query sequences for a cross-fold Y is labeled as query.Y.Z.fas, where Z is the fragment length (1, 0.5, or 0.25). The query files are found in the splits directory. [1] Nguyen, Nam-Phuong D, Mike Nute, Siavash Mirarab, and Tandy Warnow. (2016) HIPPI: Highly Accurate Protein Family Classification with Ensembles of HMMs. To appear in BMC Genomics.

Ground based radar data sets collected during the 2013 NASA EVEX Campaign conducted in Roi-Namur island of the Kwajalein Atoll in the Republic of Marshall Islands are deposited in this databank. Radar data were collected with IRIS VHF and ALTAIR VHF/UHF systems.

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 dataset helps to investigate the Spatial Accessibility to HIV Testing, Treatment, and Prevention Services in Illinois and Chicago, USA. The main components are: population data, healthcare data, GTFS feeds, and road network data. The core components are: 1) `GTFS` which contains GTFS (<a href="https://gtfs.org/">General Transit Feed Specification</a>) data which is provided by Chicago Transit Authority (CTA) from <a href="https://developers.google.com/transit/gtfs">Google's GTFS feeds</a>. Documentation defines the format and structure of the files that comprise a GTFS dataset: <a href="https://developers.google.com/transit/gtfs/reference?csw=1">https://developers.google.com/transit/gtfs/reference?csw=1</a>. 2) `HealthCare` contains shapefiles describing HIV healthcare providers in Chicago and Illinois respectively. The services come from <a href="https://locator.hiv.gov/">Locator.HIV.gov</a>. 3) `PopData` contains population data for Chicago and Illinois respectively. Data come from The American Community Survey and <a href="https://map.aidsvu.org/map">AIDSVu</a>. AIDSVu (https://map.aidsvu.org/map) provides data on PLWH in Chicago at the census tract level for the year 2017 and in the State of Illinois at the county level for the year 2016. The American Community Survey (ACS) provided the number of people aged 15 to 64 at the census tract level for the year 2017 and at the county level for the year 2016. The ACS provides annually updated information on demographic and socio economic characteristics of people and housing in the U.S. 4) `RoadNetwork` contains the road networks for Chicago and Illinois respectively from <a href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> using the Python <a href="https://osmnx.readthedocs.io/en/stable/">osmnx</a> package. <b>The abstract for our paper is:</b> Accomplishing the goals outlined in “Ending the HIV (Human Immunodeficiency Virus) Epidemic: A Plan for America Initiative” will require properly estimating and increasing access to HIV testing, treatment, and prevention services. In this research, a computational spatial method for estimating access was applied to measure distance to services from all points of a city or state while considering the size of the population in need for services as well as both driving and public transportation. Specifically, this study employed the enhanced two-step floating catchment area (E2SFCA) method to measure spatial accessibility to HIV testing, treatment (i.e., Ryan White HIV/AIDS program), and prevention (i.e., Pre-Exposure Prophylaxis [PrEP]) services. The method considered the spatial location of MSM (Men Who have Sex with Men), PLWH (People Living with HIV), and the general adult population 15-64 depending on what HIV services the U.S. Centers for Disease Control (CDC) recommends for each group. The study delineated service- and population-specific accessibility maps, demonstrating the method’s utility by analyzing data corresponding to the city of Chicago and the state of Illinois. Findings indicated health disparities in the south and the northwest of Chicago and particular areas in Illinois, as well as unique health disparities for public transportation compared to driving. The methodology details and computer code are shared for use in research and public policy.

This document contains the Supplemental Materials for Chapter 4: Climate Change Impacts on Agriculture from the report "An Assessment of the Impacts of Climate Change in Illinois" published in 2021.

This dataset contains MRI data and Imaris modeling analysis of CLARITY-cleared, immunostained tissue associated with a study that assessed the effects of lipid blends containing various levels of a hydrolyzed fat system on myelin development in healthy neonatal piglets. Data are from thirty-two piglets of mixed sexes across four diet treatment groups and includes a sow-fed reference group. MRI data (presented in Figure 2 of the associated article) consists of volumetric data from Voxel-Based Morphometry analysis in brain grey matter and white matter, as well as mean fractional anisotropy and mean orientation dispersion index data from Tract-Based Spatial Statistics analysis. Imaris data (presented in Figure 3 of the associated article) consists of twenty-one select output measures from 3D modeling analysis of PLP-stained prefrontal cortex tissue. All methods used for collection/generation/processing of data are described in the associated article: Louie AY, Rund LA, Komiyama-Kasai KA, Weisenberger KE, Stanke KL, Larsen RJ, Leyshon BJ, Kuchan MJ, Das T, Steelman AJ. A hydrolyzed lipid blend diet promotes myelination in neonatal piglets in a region and concentration-dependent manner. J Neurosci Res. 2023.

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.

Model output dataset (6-hourly) from the Weather Research and Forecasting (WRF) model simulations over South America with the added capability of water vapor tracers to track the moisture that originates over the Amazon and the La Plata river basins. The simulations were performed for the period 2003-2013 at 20-km horizontal resolution fully coupled with the Noah-MP land surface model. Limited number of original output variables sufficient for reproducing the analyses in papers that cite this dataset are included here. The attached wrfout_southamerica_readme.txt contains detailed information about the file format and variables. For the complete model dataset, contact francina@illinois.edu.

This dataset contains EEG and Temperature data acquired from inside the bore of an MRI scanner during scanning with two different types of fMRI sequences: single-band and and multi-band. The EEG data were acquired from the heads of adult humans undergoing scanning, and can be used to assess differences in EEG data quality due to sequence type. The temperature data were acquired from a watermelon phantom and can be used to assess heating differences due to sequence type.

This dataset contains R codes used to produce the figures submitted in the manuscript titled "Understanding the multifaceted geospatial software ecosystem: a survey approach". The raw survey data used to populate these charts cannot be shared due to the survey consent agreement.

This is the dataset that accompanies the paper titled "A Dual-Frequency Radar Retrieval of Snowfall Properties Using a Neural Network", submitted for peer review in August 2020. Please see the github for the most up-to-date data after the revision process: https://github.com/dopplerchase/Chase_et_al_2021_NN Authors: Randy J. Chase, Stephen W. Nesbitt and Greg M. McFarquhar Corresponding author: Randy J. Chase (randyjc2@illinois.edu) Here we have the data used in the manuscript. Please email me if you have specific questions about units etc. 1) DDA/GMM database of scattering properties: base_df_DDA.csv This is the combined dataset from the following papers: Leinonen & Moisseev, 2015; Leinonen & Szyrmer, 2015; Lu et al., 2016; Kuo et al., 2016; Eriksson et al., 2018. The column names are D: Maximum dimension in meters, M: particle mass in grams kg, sigma_ku: backscatter cross-section at ku in m^2, sigma_ka: backscatter cross-section at ka in m^2, sigma_w: backscatter cross-section at w in m^2. The first column is just an index column. 2) Synthetic Data used to train and test the neural network: Unrimed_simulation_wholespecturm_train_V2.nc, Unrimed_simulation_wholespecturm_test_V2.nc This was the result of combining the PSDs and DDA/GMM particles randomly to build the training and test dataset. 3) Notebook for training the network using the synthetic database and Google Colab (tensorflow): Train_Neural_Network_Chase2020.ipynb This is the notebook used to train the neural network. 4)Trained tensorflow neural network: NN_6by8.h5 This is the hdf5 tensorflow model that resulted from the training. You will need this to run the retrieval. 5) Scalers needed to apply the neural network: scaler_X_V2.pkl, scaler_y_V2.pkl These are the sklearn scalers used in training the neural network. You will need these to scale your data if you wish to run the retrieval. 6) <b>New in this version</b> - Example notebook of how to run the trained neural network on Ku- Ka- band observations. We showed this with the 3rd case in the paper: Run_Chase2021_NN.ipynb 7) <b>New in this version</b> - APR data used to show how to run the neural network retrieval: Chase_2021_NN_APR03Dec2015.nc The data for the analysis on the observations are not provided here because of the size of the radar data. Please see the GHRC website (<a href="https://ghrc.nsstc.nasa.gov/home/">https://ghrc.nsstc.nasa.gov/home/</a>) if you wish to download the radar and in-situ data or contact me. We can coordinate transferring the exact datafiles used. The GPM-DPR data are avail. here: <a href="http://dx.doi.org/10.5067/GPM/DPR/GPM/2A/05">http://dx.doi.org/10.5067/GPM/DPR/GPM/2A/05</a>

An online and paper knowledge, attitudes, and practices survey on ticks and tick-borne diseases (TBD) was distributed to farmers in Illinois during summer 2020 to spring 2022 (paper version titled Final Draft Farmer KAP_v.SoftCopy_Revised.docx). These are the raw data associated with that survey and the survey questions used (FarmerTickKAPdata.csv, data dictionary in Data Description.docx). We have added calculated values (columns 286 to end, code for calculation in FarmerKAPvariableCalculation.R), including: the tick knowledge score, TBD knowledge score, and total knowledge score, which are the sum of the total number of correct answers in each category, and score percent, which are the proportion of correct answers in each category.

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".

This dataset contains sequencing data obtained from Illumina MiSeq device to prove the concept of the proposed 2DDNA framework. Please refer to README.txt for detailed description of each file.

1. Rice H2 - Destructive Harvest - These data are for the destructive harvest (above-ground biomass) of 30 diverse indica rice genotypes that were grown to evaluate natural variation as well as the heritability of photosynthesis-related traits. Traits measured include: plant height, leaf area, plant fresh and dry weights, and tiller number. 2. Rice H2 - ACi Response Summary - These data characterize the response of CO2 uptake to change in intercellular CO2 concentration in 30 diverse indica rice genotypes. These measurements were taken to evaluate natural variation and the heritability of photosynthesis-related traits in rice. 3. Rice H2 - Survey Style Gas Exchange Measurements - These data document steady-state survey style gas exchange measurements in 30 diverse indica rice genotypes. These measurements were taken to evaluate natural variation and the heritability of photosynthesis-related traits in rice.

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 is the third version of the dataset*. New changes in this 3rd version: <i>1.replaces simulations where the initial condition consists of a sinusoidal channel with topographic perturbations with simulations where the initial condition consists of a sinusoidal channel without topographic perturbations. These simulations better illustrate the transformation of a nondendritic network into a dendritic one. 2. contains two additional simulations showing how total domain size affects the landscape's dynamism. 3. changes dataset title to reflect the publication's title</i> This dataset contains data from 18 simulations using a landscape evolution model. A landscape evolution model simulates how uplift and rock incision shape the Earth's (or other planets) surface. To date, most landscape evolution models exhibit "extreme memory" (paper: https://doi.org/10.1029/2019GL083305 and dataset: https://doi.org/10.13012/B2IDB-4484338_V1). Extreme memory in landscape evolution models causes initial conditions to be unrealistically preserved. This dataset contains simulations from a new landscape evolution model that incorporates a sub-model that allows bedrock channels to erode laterally. With this addition, the landscapes no longer exhibit extreme memory. Initial conditions are erased over time, and the landscapes tend towards a dynamic steady state instead of a static one. The model with lateral erosion is named LEM-wLE (Landscape Evolution Model with Lateral Erosion) and the model without lateral erosion is named LEM-woLE (Landscape Evolution Model without Lateral Erosion). There are 16 folders in total. Here are the descriptions: <i>>LEM-woLE_simulations:</i> This folder contains simulations using LEM-woLE. Inside the folder are 5 subfolders containing 100 elevation rasters, 100 drainage area rasters, and 100 plots showing the slope-area relationship. Elevation depicts the height of the landscape, and drainage area represents a contributing area that is upslope. Each folder corresponds to a different initial condition. Driver files and code for these simulations can be found at https://github.com/jeffskwang/LEM-wLE. <i>>MOVIE_S#_data:</i> There are 13 data folders that contain raster data for 13 simulations using LEM-wLE. Inside each folder are 1000 elevation rasters, 1000 drainage area rasters, and 1000 plots showing the slope-area relationship. Driver files and code for these simulations can be found at https://github.com/jeffskwang/LEM-wLE. <i>>movies_mp4_format:</i> For each data folder there are 3 movies generated that show elevation (a), drainage area (b), and erosion rates (c). These files are formatted in the mp4 format and are best viewed using VLC media player (https://www.videolan.org/vlc/index.html). <i>>movies_wmv_format:</i> This folder contains the same movies as the "movies_mp4_format" folder, but they are in a wmv format. These movies can be viewed using Windows media player or other Windows platform movie software. Here are the captions for the 13 movies: Movie S1. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Sinusoidal channel without randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 1. Movie S2. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Inclined with small, randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 1. Movie S3. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Inclined with large, randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 1. Movie S4. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: V-shaped valley with randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 1. Movie S5. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Sinusoidal channel with randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 1. Movie S6. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Sinusoidal channel without randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 0.25. Movie S7. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Sinusoidal channel without randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 0.5. Movie S8. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Sinusoidal channel without randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 0.75. Movie S9. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Flat with randomized perturbations. Boundary Condition: 1 open boundary at the bottom of the domain, and 3 closed boundaries elsewhere. KL/KV = 1. Movie S10. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Flat with randomized perturbations. Boundary Condition: 2 open boundaries at the top and bottom of the domain, and 2 closed boundaries on the left and right sides. KL/KV = 1. Movie S11. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Flat with randomized perturbations. Boundary Condition: 4 open boundaries. KL/KV = 1. Movie S12. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Flat with randomized perturbations. Boundary Condition: 4 open boundaries. KL/KV = 1. Compared to Movie S11, the length of the domain is 50% shorter, decreasing the total domain area. Movie S13. 200 MYR (1,000 RUs eroded) simulation showing elevation (a), logarithm of drainage area (b), and change in elevation (c). Initial Condition: Flat with randomized perturbations. Boundary Condition: 4 open boundaries. KL/KV = 1. Compared to Movie S11, the length of the domain is 50% longer, increasing the total domain area. The associated publication for this dataset has not yet been published, and we will update this description with a link when it is.

UAV-based high-resolution multispectral time-series orthophotos utilized to understand the relation between growth dynamics, imagery temporal resolution, and end-of-season biomass productivity of biomass sorghum as bioenergy crop. Sensor utilized is a RedEdge Micasense flown at 40 meters above ground level at the Energy Farm- UIUC in 2019.

This data comes from a scoping review associated with the project called Reducing the Inadvertent Spread of Retracted Science. The data summarizes the fields that have been explored by existing research on retraction, a list of studies comparing retraction in different fields, and a list of studies focused on retraction of COVID-19 articles.

Supplementary data tables for the dissertation "Hybridization dynamics and population genomics of a Manacus hybrid zone." This work focuses on the dynamics of hybridization over time in two species of tropical birds, the golden-collared manakin (Manacus vitellinus) and white-collared manakin (Manacus candei) comparing data from historical museum samples and contemporary wild-caught birds. Table A1 contains the sample metadata for the Manacus Restriction site-associated DNA sequencing dataset used in the dissertation with associated NCBI Biosample Accession numbers, Smithsonian Museum of Natural History number (where applicable), sample IDs, sampling site locations, and sample information of year the sample was taken, age, and sex. Table A6 contains phenotypic measurements of male plumage traits of manakins used in cline analyses to assess hybrid zone movement over time in historical and contemporary datasets, including beard length (mm), epaulet width (mm), tail length (mm), collar color (nm), and belly color (nm). Table A7 contains a summary of male plumage measurements across the hybrid zone. Table C1 contains a list of annotated protein coding genes in candidate regions of interest in Manacus genomes using outlier regions of genomic divergence, linkage disequilibrium, and enrichment of parental private alleles.