Datasets

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."

Data in this publication were used to analyze the factors that influence the abundance of eastern whip-poor-wills in the Midwest and to describe the diet of this species. These data were collected in Illinois in 2019 and 2020. Procedures were approved by the Illinois Institutional Animal Care and Use Committee (IACUC), protocol no. 19006

This dataset contains Raman spectra, each acquired from an individual, living, primary murine cell belonging to one of the six most immature hematopoietic cell populations found in the body: hematopoietic stem cell (HSC), mutipotent progenitor 1 (MPP1), multipotent progenitor 2 (MPP2), multipotent progenitor 3 (MPP3), common lymphoid progenitor, common myeloid progenitor (CLP). These spectra are useful for identifying spectral signatures that are characteristic of each hematopoietic stem or early progenitor cell population. *NOTE: __MACOSX folder and files start with “._[file name]” found in "Raman spectra of single cells text files.zip" were created by the computer operation system, in unreadable format, which are not part of the data and can be removed/ignored when using the data.

This dataset contains soil chemical properties used to variation in soil fungal communities beneath Oreomunnea mexicana trees in the manuscript "Watershed-scale variation in potential fungal community contributions to ectomycorrhizal biogeochemical syndromes"

Dataset associated with Jones and Ward BEAS-D-21-00106R2 submission: Parasitic cowbird development up to fledging and subsequent post-fledging survival reflect life history variation found across host species. Excel CSV files and .inp file with data used in nest survival and Brown-headed Cowbird post-fledging analyses and file with descriptions of each column. The CSV file is setup for logistic exposure models in SAS or R and the .inp file is setup to be uploaded into program MARK for multi-state recaptures only analysis. Species included in the analyses: American Robin, Blue Grosbeak, Brown Thrasher, Blue-winged Warbler, Carolina Chickadee, Chipping Sparrow, Common Yellowthroat, Dickcissel, Eastern Bluebird, Eastern Phoebe, Eastern Towhee, Field Sparrow, Gray Catbird, House Wren, Indigo Bunting, Northern Cardinal, Red-winged Blackbird, Tree Swallow, Yellow-breasted Chat, and Yellow Warbler.

This upload includes the 16S.B.ALL in 100-HF condition (referred to as 16S.B.ALL-100-HF) used in Experiment 3 of the WITCH paper (currently accepted in principle by the Journal of Computational Biology). 100-HF condition refers to making sequences fragmentary with an average length of 100 bp and a standard deviation of 60 bp. Additionally, we enforced that all fragmentary sequences to have lengths > 50 bp. Thus, the final average length of the fragments is slightly higher than 100 bp (~120 bp). In this case (i.e., 16S.B.ALL-100-HF), 1,000 sequences with lengths 25% around the median length are retained as "backbone sequences", while the remaining sequences are considered "query sequences" and made fragmentary using the "100-HF" procedure. Backbone sequences are aligned using MAGUS (or we extract their reference alignment). Then, the fragmentary versions of the query sequences are added back to the backbone alignment using either MAGUS+UPP or WITCH. More details of the tar.gz file are described in README.txt.

An online knowledge, attitudes, and practices survey on ticks and tick-borne diseases was distributed to medical professionals in Illinois during summer 2020 to fall 2021. These are the raw data associated with that survey and the survey questions used. Age, gender, and county of practice have been removed for identifiability. We have added calculated values (columns 165 to end), 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; region, which is determined from the county of practice; TBD relevant practice, which separates the practice variable into TBD primary, secondary, and non-responders; and several variables which group categories.

This dataset consists of all the files and codes that are part of the manuscript (main text and supplement) titled "Spin-selective tunneling from nanowires of the candidate topological Kondo insulator SmB6". For detailed information on the individual files refer to the specific readme files.

This data set documents bat activity (counts per detector-night per phonic group) and bat diversity (number of bat species per detector-night) in relation to distance to the nearest forested corridor in a row crop agriculture dominated landscape and in relation to relative crop pest abundance. This data set was used to assess if bats were homogeneously distributed over a near-uninterrupted agricultural landscape and to assess the importance of forested corridors and the presence of pest species on their distribution across the landscape. Data was collected with 50 AudioMoth bat detectors along 10 transects, with each transect having 5 detectors. The transects started at a forest corridor and extended out for 4 km into uninterrupted row crop agriculture. Pest abundance was extrapolated from data collected in the same county during the same time as the study. Potentially important weather covariates were extracted from the nearest operational weather station.

Datasets that accompany Shearer and Beilke 2022 publication (Title: Playing it by ear: gregarious sparrows recognize and respond to isolated wingbeat sounds and predator-based cues.; Journal: Animal Cognition)

Related to the raw entity mentions, this dataset represents the effects of the data cleaning process and collates all of the entity mentions which were too ambiguous to successfully link to the NCBI's taxonomy identifier system.

A set of species entity mentions derived from an NERC dataset analyzing 900 synthetic biology articles published by the ACS. This data is associated with the Synthetic Biology Knowledge System repository (https://web.synbioks.org/). The data in this dataset are raw mentions from the NERC data.

This dataset represents the results of manual cleaning and annotation of the entity mentions contained in the raw dataset (https://doi.org/10.13012/B2IDB-4950847_V1). Each mention has been consolidated and linked to an identifier for a matching concept from the NCBI's taxonomy database.

This dataset is derived from the raw dataset (https://doi.org/10.13012/B2IDB-4950847_V1) and collects entity mentions that were manually determined to be noisy, non-species entities.

This dataset is derived from the raw entity mention dataset (https://doi.org/10.13012/B2IDB-4950847_V1) for species entities and represents those that were determined to be species (i.e., were not noisy entities) but for which no corresponding concept could be found in the NCBI taxonomy database.

Related to the raw entity mentions (https://doi.org/10.13012/B2IDB-4163883_V1), this dataset represents the effects of the data cleaning process and collates all of the entity mentions which were too ambiguous to successfully link to the ChEBI ontology.

A set of chemical entity mentions derived from an NERC dataset analyzing 900 synthetic biology articles published by the ACS. This data is associated with the Synthetic Biology Knowledge System repository (https://web.synbioks.org/). The data in this dataset are raw mentions from the NERC data.

This dataset represents the results of manual cleaning and annotation of the entity mentions contained in the raw dataset (https://doi.org/10.13012/B2IDB-4163883_V1). Each mention has been consolidated and linked to an identifier for a matching concept from the NCBI's taxonomy database.

This dataset is derived from the raw dataset (https://doi.org/10.13012/B2IDB-4163883_V1) and collects entity mentions that were manually determined to be noisy, non-chemical entities.

This dataset is derived from the raw entity mention dataset (https://doi.org/10.13012/B2IDB-4163883_V1) for checmical entities and represents those that were determined to be chemicals (i.e., were not noisy entities) but for which no corresponding concept could be found in the ChEBI ontology.

A set of gene and gene-related entity mentions derived from an NERC dataset analyzing 900 synthetic biology articles published by the ACS. This data is associated with the Synthetic Biology Knowledge System repository (https://web.synbioks.org/). The data in this dataset are raw mentions from the NERC data.

This dataset contains data used in publication "Institutional Data Repository Development, a Moving Target" submitted to Code4Lib Journal. It is a tabular data file describing attributes of data files in datasets published in Illinois Data Bank 2016-04-01 to 2021-04-01.

A set of cell-line entity mentions derived from an NERC dataset analyzing 900 synthetic biology articles published by the ACS. This data is associated with the Synthetic Biology Knowledge System repository (https://web.synbioks.org/). The data in this dataset are raw mentions from the NERC data.

This dataset was developed as part of an online survey study that explores student characteristics that may predict what one finds helpful in replies to requests for help posted to an online college course discussion forum. 223 college students enrolled in an introductory statistics course were surveyed on their sense of belonging to their course community, as well as how helpful they found 20 examples of replies to requests for help posted to a statistics course discussion forum.

#### Details of Pseudomonas aeruginosa biofilm dataset #### ----------------*Folder Structure*------------------------------------- This dataset contains peak intensity tables extracted from mass spectrometry imaging (MSI) data using tools, SCiLS and MSI reader. There are 2 folders in "MSI-Data-Paeruginosa-biofilms-UIUC-DP-JVS-July2022.zip", each folder contains 3 sub-folders as listed below. 1. PellicleBiofilms-and-Supernatant [Pellicle biofilms collected from air-liquid interface and spend supernatant medium after 96 h incubation period]: (1) Full-Scan-Data-96h; (2) MSMS-data-from-C7-Quinolones-96h; and (3) MSMS-data-from-C9-Quinolones-96h 2. StaticBiofilms [Static biofilms grown on mucin surface]: (1) Full-Scan-Data; (2) MSMS-data-from-C7-Quinolones; and (3) MSMS-data-from-C9-Quinolones ----------------*File name*---------------------------------------------- Sample information is included in the file names for easy identification and processing. Attributes covered in file names are explained in the example below. *Example file name "Rep1-Stat-FRD1-mPat-48-FS"* ~ Each unit of information is separated by "-" ~Unit 1 - "Rep1" - Biological replicate ( Rep1, Rep2, and Rep3) ~Unit 2 - "Stat" - Sample type (Stat = Static Biofilm, Pel = Pellicle biofilm, Sup = Supernatant) ~Unit 3 - "FRD1" - Strain (FRD1 = Mucoid strain, PAO1C = Non-mucoid strain) ~Unit 4 - "mPat" - Type of mucin surface used (mPat = patterned mucin surface, mUni = uniform mucin surface) ~Unit 5 - "48" - Sample time point (hours = 48, 72, 96) ~Unit 6 - "FS" - Scan type used in MSI (FS = high resolution full-scan, 260 = targeted MS/MS of C7 quinolones (m/z 260), 288 = targeted MS/MS of C9 quinolones (m/z 288)) ----------------*File structure*------------------------------------------ All MSI data has been exported to CSV format. Each CSV files contains information about scan number, Coordinates (x,y,z), m/z values, extraction window (absolute), and corresponding intensities in the form of a matrix. ----------------*End of Information*--------------------------------------