Datasets

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.

These data and code enable replication of the findings and robustness checks in "No buzz for bees: Media coverage of pollinator decline," published in Proceedings of the National Academy of Sciences of the United States of America (2020)". In this paper, we find that although widespread declines in insect biomass and diversity are increasing concern within the scientific community, it remains unclear whether attention to pollinator declines has also increased within information sources serving the general public. Examining patterns of journalistic attention to the pollinator population crisis can also inform efforts to raise awareness about the importance of declines of insect species providing ecosystem services beyond pollination. We used the Global News Index developed by the Cline Center for Advanced Social Research at the University of Illinois at Urbana-Champaign to track news attention to pollinator topics in nearly 25 million news items published by two American national newspapers and four international wire services over the past four decades. We provide a link to documentation of the Global News Index in the "relationships with articles, code, o. We found vanishingly low levels of attention to pollinator population topics relative to coverage of climate change, which we use as a comparison topic. In the most recent subset of ~10 million stories published from 2007 to 2019, 1.39% (137,086 stories) refer to climate change/global warming, while only 0.02% (1,780) refer to pollinator populations in all contexts and just 0.007% (679) refer to pollinator declines. Substantial increases in news attention were detectable only in U.S. national newspapers. We also find that while climate change stories appear primarily in newspaper “front sections”, pollinator population stories remain largely marginalized in “science” and “back section” reports. At the same time, news reports about pollinator populations increasingly link the issue to climate change, which might ultimately help raise public awareness to effect needed policy changes.

Data sets from "Inferring Species Trees from Gene-Family with Duplication and Loss using Multi-Copy Gene-Family Tree Decomposition." It contains trees and sequences simulated with gene duplication and loss under a variety of different conditions. <b>Note:</b> - trees.tar.gz contains the simulated gene-family trees used in our experiments (both true trees from SimPhy as well as trees estimated from alignements). - sequences.tar.gz contains simulated sequence data used for estimating the gene-family trees as well as the concatenation analysis. - biological.tar.gz contains the gene trees used as inputs for the experiments we ran on empirical data sets as well as species trees outputted by the methods we tested on those data sets. - stats.txt list statistics (such as AD, MGTE, and average size) for our simulated model conditions.

Data associated with the manuscript "Do rusty crayfish invasions affect water clarity in north temperate lakes?" by Daniel K. Szydlowski, Melissa K. Daniels, and Eric R. lARSON

This dataset consists of the secondary ion mass spectrometry (SIMS) depth profiling data that was collected with a Cameca NanoSIMS 50 instrument from a 10 micron by 10 micron region on a Madin-Darby canine kidney (MDCK) cell that had been metabolically labeled so most of its sphingolipids and cholesterol contained the rare nitrogen-15 oxygen-18 isotopes, respectively.

Simulation trajectory data and scripts for Nature Nanotechnology manuscript "A DNA turbine powered by a transmembrane potential across a nanopore" that demonstrates a rationally designed nanoscale DNA-origami turbine with three chiral blades that uses a transmembrane electrochemical potential across a nanopore to drive a DNA bundle into sustained unidirectional rotations of up to 10 revolutions/s. Driven by the asymmetric mobility of a DNA duplex, the rotation direction of the turbine is set by its designed chirality and the salinity of the solvent.

VCF files used to analyze a novel filtering tool VEF, presented in the article "VEF: a Variant Filtering tool based on Ensemble methods".

This dataset contains the PartMC-MOSAIC simulations used in the article "Quantifying Errors in the Aerosol Mixing-State Index Based on Limited Particle Sample Size". The 1000 simulations of output data is organized into a series of archived folders, each containing 100 scenarios. Within each scenario directory are 25 NetCDF files, which are the hourly output of a PartMC-MOSAIC simulation containing all information regarding the environment, particle and gas state. This dataset was used to investigate the impact of sample size on determining aerosol mixing state. This data may be useful as a data set for applying different types of estimators.

This dataset containes the images of B73xMS71 RIL population used in QTL linkage mapping for maize epidermal traits in year 2016 and 2017. 2016RIL_all_mns.rar and 2017RIL_all_mns.rar: contain raw images produced by Nanofocus lsurf Explorer Optical Topometer (Oberhausen, Germany) at 20X magnification with 0.6 numerical aperture. Files were processed in Nanofocus μsurf analysis extended software (Oberhausen,Germany). 2016RIL_all_TIF.rar and 2017RIL_all_TIF.rar: contain images processed from the Topology layer in each nms file to strengthen the edges of cell outlines, and used in downstream cell detection. 2016RIL_all_detection_result.rar and 2017RIL_all_detection_result.rar: contain images with epidermal cells predicted using the Mask R-CNN model. training data.rar: contain images used for Mask R-CNN model training and validation.

Prepared by Vetle Torvik 2021-05-07 The dataset comes as a single tab-delimited Latin-1 encoded file (only the City column uses non-ASCII characters). • How was the dataset created? The dataset is based on a snapshot of PubMed (which includes Medline and PubMed-not-Medline records) taken in December, 2018. (NLMs baseline 2018 plus updates throughout 2018). Affiliations are linked to a particular author on a particular article. Prior to 2014, NLM recorded the affiliation of the first author only. However, MapAffil 2018 covers some PubMed records lacking affiliations that were harvested elsewhere, from PMC (e.g., PMID 22427989), NIH grants (e.g., 1838378), and Microsoft Academic Graph and ADS (e.g. 5833220). Affiliations are pre-processed (e.g., transliterated into ASCII from UTF-8 and html) so they may differ (sometimes a lot; see PMID 27487542) from PubMed records. All affiliation strings where processed using the MapAffil procedure, to identify and disambiguate the most specific place-name, as described in: Torvik VI. MapAffil: A bibliographic tool for mapping author affiliation strings to cities and their geocodes worldwide. D-Lib Magazine 2015; 21 (11/12). 10p • Look for Fig. 4 in the following article for coverage statistics over time: Palmblad, M., Torvik, V.I. Spatiotemporal analysis of tropical disease research combining Europe PMC and affiliation mapping web services. Trop Med Health 45, 33 (2017). <a href="https://doi.org/10.1186/s41182-017-0073-6">https://doi.org/10.1186/s41182-017-0073-6</a> Expect to see big upticks in coverage of PMIDs around 1988 and for non-first authors in 2014. • The code and back-end data is periodically updated and made available for query by PMID at http://abel.ischool.illinois.edu/cgi-bin/mapaffil/search.py • What is the format of the dataset? The dataset contains 52,931,957 rows (plus a header row). Each row (line) in the file has a unique PMID and author order, and contains the following eighteen columns, tab-delimited. All columns are ASCII, except city which contains Latin-1. 1. PMID: positive non-zero integer; int(10) unsigned 2. au_order: positive non-zero integer; smallint(4) 3. lastname: varchar(80) 4. firstname: varchar(80); NLM started including these in 2002 but many have been harvested from outside PubMed 5. initial_2: middle name initial 6. orcid: From 2019 ORCID Public Data File https://orcid.org/ and from PubMed XML 7. year: year of the publication 8. journal: name of journal that the publication is published 9. affiliation: author's affiliation?? 10. disciplines: extracted from departments, divisions, schools, laboratories, centers, etc. that occur on at least unique 100 affiliations across the dataset, some with standardization (e.g., 1770799), English translations (e.g., 2314876), or spelling corrections (e.g., 1291843) 11. grid: inferred using a high-recall technique focused on educational institutions (but, for experimental purposes, includes a few select hospitals, national institutes/centers, international companies, governmental agencies, and 200+ other IDs [RINGGOLD, Wikidata, ISNI, VIAF, http] for institutions not in GRID). Based on 2019 GRID version https://www.grid.ac/ 12. type: EDU, HOS, EDU-HOS, ORG, COM, GOV, MIL, UNK 13. city: varchar(200); typically 'city, state, country' but could include further subdivisions; unresolved ambiguities are concatenated by '|' 14. state: Australia, Canada and USA (which includes territories like PR, GU, AS, and post-codes like AE and AA) 15. country 16. lat: at most 3 decimals (only available when city is not a country or state) 17. lon: at most 3 decimals (only available when city is not a country or state) 18. fips: varchar(5); for USA only retrieved by lat-lon query to https://geo.fcc.gov/api/census/block/find

#### 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*--------------------------------------

This is the experimental data of isolated nanomagnet islands with or without the presence of large nanomagnet islands. The small islands are made of Permalloy materials with size of 170 nm by 470 nm by 2.5 nm. The systems are measured at a temperature where the small islands are fluctuating around room temperature. The data is recorded as photoemission electron microscopy intensity. More details about the data can be found in the note.txt and Spe_2016.xlsx file. Note: The raw data folders are stored in five volumes during the compression. All five volumes are needed in order to recover the original folder.

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.

These datasets are for the four-dimensional scanning transmission electron microscopy (4D-STEM) and electron energy loss spectroscopy (EELS) experiments for cathode nanoparticles at different cutoff voltages and in different electrolytes. The raw 4D-STEM experiment datasets were collected by TEM image & analysis software (FEI) and were saved as SER files. The raw 4D-STEM datasets of SER files can be opened and viewed in MATLAB using our analysis software package of imToolBox available at <a href="https://github.com/flysteven/imToolBox">https://github.com/flysteven/imToolBox</a>. The raw EELS datasets were collected by DigitalMicrograph software and were saved as DM4 files. The raw EELS datasets can be opened and viewed in DigitalMicrograph software or using our analysis codes available at <a href="https://github.com/chenlabUIUC/OrientedPhaseDomain">https://github.com/chenlabUIUC/OrientedPhaseDomain</a>. All the datasets are from the work "Formation and impact of nanoscopic oriented phase domains in electrochemical crystalline electrodes" (2022). The 4D-STEM experiment data include four example datasets for cathode nanoparticles collected at different cutoff voltages and in different electrolytes as described below. Each dataset contains a stack of diffraction patterns collected at different probe positions scanned across the cathode nanoparticle. 1. Pristine cathode particle: "Pristine particle 4D-STEM.ser" 2. Cathode particle at the cutoff voltage of 0.09V during discharge at C/10 in the aqueous electrolyte: "Intermediate cutoff0_09V discharge (aqueous) 4D-STEM.ser" 3. Fully discharged cathode particle at C/10 in the aqueous electrolyte: "Fully discharged particle 4D-STEM.ser" 4. Fully discharged cathode particle at C/10 in the dry organic electrolyte: "Fully discharge particle (dry organic electrolyte).ser" The EELS experiment data includes three example datasets for cathode nanoparticles collected at different cutoff voltages during discharge in the aqueous electrolyte (in "EELS datasets.zip") as described below. Each EELS dataset contains the zero-loss and core-loss EELS spectra collected at different probe positions scanned across the cathode nanoparticle. 1. Pristine cathode particle: "Pristine particle EELS.zip" 2. Cathode particle at the cutoff voltage of 0.09V during discharge at C/10 in the aqueous electrolyte: "intermediate discharge (aqueous) EELS.zip" 3. Fully discharged cathode particle at C/10 in the aqueous electrolyte: "fully discharge (aqueous) EELS.zip" The details of the software package and codes that can be used to analyze the 4D-STEM datasets and EELS datasets are available at: https://github.com/chenlabUIUC/OrientedPhaseDomain. Once our paper is formally published, we will update the relationship of these datasets with our paper.

This repository includes scripts and datasets for Chapter 6 of my PhD dissertation, " Supertree-like methods for genome-scale species tree estimation," that had not been published previously. This chapter is based on the article: Molloy, E.K. and Warnow, T. "FastMulRFS: Fast and accurate species tree estimation under generic gene duplication and loss models." Bioinformatics, In press. https://doi.org/10.1093/bioinformatics/btaa444. The results presented in my PhD dissertation differ from those in the Bioinformatics article, because I re-estimated species trees using FastMulRF and MulRF on the same datasets in the original repository (https://doi.org/10.13012/B2IDB-5721322_V1). To re-estimate species trees, (1) a seed was specified when running MulRF, and (2) a different script (specifically preprocess_multrees_v3.py from https://github.com/ekmolloy/fastmulrfs/releases/tag/v1.2.0) was used for preprocessing gene trees (which were then given as input to MulRF and FastMulRFS). Note that this preprocessing script is a re-implementation of the original algorithm for improved speed (a bug fix also was implemented). Finally, it was brought to my attention that the simulation in the Bioinformatics article differs from prior studies, because I scaled the species tree by 10 generations per year (instead of 0.9 years per generation, which is ~1.1 generations per year). I re-simulated datasets (true-trees-with-one-gen-per-year-psize-10000000.tar.gz and true-trees-with-one-gen-per-year-psize-50000000.tar.gz) using 0.9 years per generation to quantify the impact of this parameter change (see my PhD dissertation or the supplementary materials of Bioinformatics article for discussion).

This dataset consists of raw ADC readings from a 3 transmitter 4 receiver 77GHz FMCW radar, together with synchronized RGB camera and depth (active stereo) measurements. The data is grouped into 4 distinct radar configurations: - "indoor" configuration with range <14m - "30m" with range <38m - "50m" with range <63m - "high_res" with doppler resolution of 0.043m/s # Related code https://github.com/moodoki/radical_sdk # Hardware Project Page https://publish.illinois.edu/radicaldata

These data were used in the survival and cause-specific mortality analyses of translocated nuisance American black bear in Wisconsin published in Animal Conservation (Bauder, J.M., N.M. Roberts, D. Ruid, B. Kohn, and M.L. Allen. Accepted. Lower survival of nuisance American black bears (Ursus americanus) is not due to translocation. Animal Conservation). Included are CSV files including each bear's capture history and associated covariates and meta-data for each CSV file. Also included is an example R script of how to conduct the analyses (this R script is also included as supporting information with the published paper).

These are abundance dynamics data and simulations for the paper "Higher-order interaction between species inhibits bacterial invasion of a phototroph-predator microbial community". In this V2, data were converted in Python, in addition to MATLAB and more information on how to work with the data was included in the Readme.

The PhytoplasmasRef_Trivellone_etal.fas fasta file contains the original final sequence alignment used in the phylogenetic analyses of Trivellone et al. (Ecology and Evolution, in review). The 27 sequences (21 phytoplasma reference strains and 6 phytoplasmas strains from the present study) were aligned using the Muscle algorithm as implemented in MEGA 7.0 with default settings. The final dataset contains 952 positions of the F2n/R2 fragment of the 16S rRNA gene. The data analyses are further described in the cited original paper.

The compiled datasets include plot level observations of energy crops (miscanthus and switchgrass) from recent experimental field trials in the US including dry biomass yield, location, state, region, harvest year, growing season degree days (GDD), winter season heating degree days (HDD), growing season cumulative precipitation, annual nitrogen application rate, age of the pant when harvested, National Commodity Crop Productivity Index (NCCPI) values, and cultivar type (switchgrass) from various published and unpublished sources. The stata codes include estimation procedures for four different specifications, i.e., Model A includes deterministic effect without interaction terms; Model B includes deterministic effect with interaction terms (N2, age2, N × age, GDD2, precip2, N × NCCPI); Model C includes deterministic effect with interaction terms, study, and location random effect; Model D includes deterministic effect with interaction terms, harvest year augmented study, and location random effect.

This dataset consists of microclimatic temperature and vegetation structure maps at a 3-meter spatial resolution across the Great Smoky Mountains National Park. Included are raster models for sub-canopy, near-surface, minimum and maximum temperature averaged across the study period, season, and month during the growing season months of March through November from 2006-2010. Also available are the topographic and vegetation inputs developed for the microclimate models, including LiDAR-derived vegetation height, LiDAR-derived vegetation structure within four height strata, solar insolation, distance-to-stream, and topographic convergence index (TCI).

This dataset was developed as part of a study that assessed data reuse. Through bibliometric analysis, corresponding authors of highly cited papers published in 2015 at the University of Illinois at Urbana-Champaign in nine STEM disciplines were identified and then surveyed to determine if data were generated for their article and their knowledge of reuse by other researchers. Second, the corresponding authors who cited those 2015 articles were identified and surveyed to ascertain whether they reused data from the original article and how that data was obtained. The project goal was to better understand data reuse in practice and to explore if research data from an initial publication was reused in subsequent publications.

This dataset includes structural MRI head scans of 32 piglets, at 28 days of age, scanned at the University of Illinois. The dataset also includes manually drawn brain masks of each of the piglets. The dataset also includes brain masks that were generated automatically using Region-Based Convolutional Neural Networks (Mask R-CNN), trained on the manually drawn brain masks.

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.

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.