Dataset Search Results

We studied vegetation metric robustness to environmental (season, interannual, and regional) and methodological (observer) variables, as well as adequate sample size for vegetation metrics across four regions of the United States.

This dataset contains our bi-hourly temperature recordings from 40 rocket box style artificial roosts of 5 designs deployed in Indiana and Kentucky, USA from April through September 2019. This dataset also includes our endothermic and faculatively heterothermic daily energy expenditure datasets used in our bioenergetic analysis, which were calculated from the bi-hourly rocket box temperature data. Lastly, we include our overheating counts dataset which summarizes daily overheating events (i.e., temperatures > 40 Celsius) in each rocket box style bat box over the course of the study period, these daily summaries were also calculated from the bi-hourly rocket box temperature recordings.

This dataset is for characterizing chemical short-range-ordering in CrCoNi medium entropy alloys. It has three sub-folders: 1. code, 2. sample WQ, 3. sample HT. The software needed to run the files is Gatan Microscopy Suite® (GMS). Please follow the instruction on this page to install the DM3 GMS: <a href="https://www.gatan.com/installation-instructions#Step1">https://www.gatan.com/installation-instructions#Step1</a> 1. Code folder contains three DM scripts to be installed in Gatan DigitalMicrograph software to analyze scanning electron nanobeam diffraction (SEND) dataset: Cepstrum.s: need [EF-SEND_sampleWQ_cropped_aligned.dm3] in Sample WQ and the average image from [EF-SEND_sampleWQ_cropped_aligned.dm3]. Same for Sample HT folder. log_BraggRemoval.s: same as above. Patterson.s: Need refined diffuse patterns in Sample HT folder. 2. Sample WQ and 3. Sample HT folders both contain the SEND data (.ser) and the binned SEND data (.dm3) as well as our calculated strain maps as the strain measurement reference. The Sample WQ folder additionally has atomic resolution STEM images; the Sample HT folder additionally has three refined diffuse patterns as references for diffraction data processing. * Only .ser file is needed to perform the strain measurement using imToolBox as listed in the manuscript. .emi file contains the meta data of the microscope, which can be opened together with .ser file using FEI TIA software.

This dataset includes the blue water intensity by sector (41 industries and service sectors) for provinces in China, economic and virtual water network flow for China in 2017, and the corresponding network properties for these two networks.

Use of cellulosic biofuels from non-feedstocks are modeled using the BEPAM (Biofuel and Environmental Policy Analysis Model) model to quantifying the uncertainties about induced land use change effects, net greenhouse gas saving potential, and economic costs. The code is in GAMS, general algebraic modeling language. NOTE: Column 3 is titled "BAU" in "merged_BAU.gdx", "merged_RFS.gdx", and "merged_CEM.gdx", but contains "RFS" data in "merged_RFS.gdx" and "CEM" data in "merged_CEM.gdx".

Files in this dataset represent an investigation into use of the Library mobile app Minrva during the months of May 2015 through December 2015. During this time interval 45,975 API hits were recorded by the Minrva web server. The dataset included herein is an analysis of the following: 1) a delineation of API hits to mobile app modules use in the Minrva app by month, 2) a general analysis of Minrva app downloads to module use, and 3) the annotated data file providing associations from API hits to specific modules used, organized by month (May 2015 – December 2015).

The dataset contains the high-throughput matrix-assisted laser desorption/ionization mass spectrometry XmL files for the atrial gland and red hemiduct of Aplysia californica.

Data and code supporting the paper titled "Estimating the Electric Vehicle Charging Demand of Multi-Unit Dwelling Residents in the United States" by Xi Cheng and Eleftheria Kontou at the University of Illinois Urbana-Champaign. The data and the code enable analytics and assessment of multi-unit dwelling residents travel patterns and their electric vehicle charging demand.

Sentences and citation contexts identified from the PubMed Central open access articles ---------------------------------------------------------------------- The dataset is delivered as 24 tab-delimited text files. The files contain 720,649,608 sentences, 75,848,689 of which are citation contexts. The dataset is based on a snapshot of articles in the XML version of the PubMed Central open access subset (i.e., the PMCOA subset). The PMCOA subset was collected in May 2019. The dataset is created as described in: Hsiao TK., & Torvik V. I. (manuscript) OpCitance: Citation contexts identified from the PubMed Central open access articles. <b>Files</b>: • A_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with A. • B_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with B. • C_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with C. • D_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with D. • E_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with E. • F_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with F. • G_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with G. • H_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with H. • I_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with I. • J_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with J. • K_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with K. • L_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with L. • M_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with M. • N_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with N. • O_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with O. • P_p1_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with P (part 1). • P_p2_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with P (part 2). • Q_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with Q. • R_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with R. • S_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with S. • T_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with T. • UV_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with U or V. • W_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with W. • XYZ_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with X, Y or Z. Each row in the file is a sentence/citation context and contains the following columns: • pmcid: PMCID of the article • pmid: PMID of the article. If an article does not have a PMID, the value is NONE.  • location: The article component (abstract, main text, table, figure, etc.) to which the citation context/sentence belongs.  • IMRaD: The type of IMRaD section associated with the citation context/sentence. I, M, R, and D represent introduction/background, method, results, and conclusion/discussion, respectively; NoIMRaD indicates that the section type is not identifiable.  • sentence_id: The ID of the citation context/sentence in the article component • total_sentences: The number of sentences in the article component.  • intxt_id: The ID of the citation. • intxt_pmid: PMID of the citation (as tagged in the XML file). If a citation does not have a PMID tagged in the XML file, the value is "-". • intxt_pmid_source: The sources where the intxt_pmid can be identified. Xml represents that the PMID is only identified from the XML file; xml,pmc represents that the PMID is not only from the XML file, but also in the citation data collected from the NCBI Entrez Programming Utilities. If a citation does not have an intxt_pmid, the value is "-".  • intxt_mark: The citation marker associated with the inline citation. • best_id: The best source link ID (e.g., PMID) of the citation. • best_source: The sources that confirm the best ID. • best_id_diff: The comparison result between the best_id column and the intxt_pmid column. • citation: A citation context. If no citation is found in a sentence, the value is the sentence.  • progression: Text progression of the citation context/sentence.  <b>Supplementary Files</b> • PMC-OA-patci.tsv.gz – This file contains the best source link IDs for the references (e.g., PMID). Patci [1] was used to identify the best source link IDs. The best source link IDs are mapped to the citation contexts and displayed in the *_journal IntxtCit.tsv files as the best_id column. Each row in the PMC-OA-patci.tsv.gz file is a citation (i.e., a reference extracted from the XML file) and contains the following columns: • pmcid: PMCID of the citing article. • pos: The citation's position in the reference list. • fromPMID: PMID of the citing article. • toPMID: Source link ID (e.g., PMID) of the citation. This ID is identified by Patci. • SRC: The sources that confirm the toPMID. • MatchDB: The origin bibliographic database of the toPMID. • Probability: The match probability of the toPMID. • toPMID2: PMID of the citation (as tagged in the XML file). • SRC2: The sources that confirm the toPMID2. • intxt_id: The ID of the citation. • journal: The first letter of the journal title. This maps to the *_journal_IntxtCit.tsv files. • same_ref_string: Whether the citation string appears in the reference list more than once. • DIFF: The comparison result between the toPMID column and the toPMID2 column. • bestID: The best source link ID (e.g., PMID) of the citation. • bestSRC: The sources that confirm the best ID. • Match: Matching result produced by Patci. [1] Agarwal, S., Lincoln, M., Cai, H., & Torvik, V. (2014). Patci – a tool for identifying scientific articles cited by patents. GSLIS Research Showcase 2014. http://hdl.handle.net/2142/54885 • intxt_cit_license_fromPMC.tsv – This file contains the CC licensing information for each article. The licensing information is from PMC's file lists [2], retrieved on June 19, 2020, and March 9, 2023. It should be noted that the license information for 189,855 PMCIDs is <b>NO-CC CODE</b> in the file lists, and 521 PMCIDs are absent in the file lists. The absence of CC licensing information does not indicate that the article lacks a CC license. For example, PMCID: 6156294 (<b>NO-CC CODE</b>) and PMCID: 6118074 (absent in the PMC's file lists) are under CC-BY licenses according to their PDF versions of articles. The intxt_cit_license_fromPMC.tsv file has two columns: • pmcid: PMCID of the article. • license: The article’s CC license information provided in PMC’s file lists. The value is nan when an article is not present in the PMC’s file lists. [2] https://www.ncbi.nlm.nih.gov/pmc/tools/ftp/ • Supplementary_File_1.zip – This file contains the code for generating the dataset.

STEM images of kinks in α-In2Se3, DFT calculation of bending of α-In2Se3, PFM on as exfoliated and controllably bend α-In2Se3

These datasets contain modeling files and GIS data associated with a risk assessment study for the Cambrian-Ordovician sandstone aquifer system in Illinois from predevelopment (1863) to the year 2070. Modeling work was completed using the Illinois Groundwater Flow Model, a regional MODFLOW model developed for water supply planning in Illinois, as a base model. The model is run using the graphical user interface Groundwater Vistas 7.0. The development and technical details of the base Illinois Groundwater Flow Model, including hydraulic property zonation, boundary conditions, hydrostratigraphy, solver settings, and discretization, are described in Abrams et al. (2018). Modifications to this base model (the version presented here) are described in Mannix et al. (2018), Hadley et al. (2020) and Abrams and Cullen (2020). Modifications include removal of particular multi-aquifer wells to improve calibration, changing Sandwich Fault Zone properties to achieve calibration at production wells within and near the fault zone, and the incorporation of demand scenarios based on a participatory modeling project with the Southwest Water Planning Group. The zipped folder of model files contains MODFLOW input (package) files, Groundwater Vistas files, and a head file for the entire model run. The zipped folder of GIS data contains rasters of: simulated drawdown in the St. Peter sandstone from predevelopment to 2018, simulated drawdown in the Ironton-Galesville sandstone from predevelopment to 2018, simulated head difference between the St. Peter and Ironton-Galesville sandstone units in 2018, simulated head above the top of the St. Peter sandstone for the years 2029, 2050, and 2070, and simulated head above the top of the Ironton-Galesville sandstone for the years 2029, 2050, and 2070. Raster outputs were derived directly from the simulated heads in the Illinois Groundwater Flow Model. Rasters are clipped to the 8 county northeastern Illinois region (Cook, DuPage, Grundy, Kane, Kendall, Lake, McHenry, and Will counties). Well names, historic and current head targets, and spatial offsets for the Illinois Groundwater Flow Model are available upon request via a data license agreement. Please contact authors to set this up if needed.

The XSEDE program manages the database of allocation awards for the portfolio of advanced research computing resources funded by the National Science Foundation (NSF). The database holds data for allocation awards dating to the start of the TeraGrid program in 2004 through the XSEDE operational period, which ended August 31, 2022. The project data include lead researcher and affiliation, title and abstract, field of science, and the start and end dates. Along with the project information, the data set includes resource allocation and usage data for each award associated with the project. The data show the transition of resources over a fifteen year span along with the evolution of researchers, fields of science, and institutional representation. Because the XSEDE program has ended, the allocation_award_history file includes all allocations activity initiated via XSEDE processes through August 31, 2022. The Resource Providers and successor program to XSEDE agreed to honor all project allocations made during XSEDE. Thus, allocation awards that extend beyond the end of XSEDE may not reflect all activity that may ultimately be part of the project award. Similarly, allocation usage data only reflects usage reported through August 31, 2022, and may not reflect all activity that may ultimately be conducted by projects that were active beyond XSEDE.

This dataset provides estimates of total Irrigation Water Use (IWU) by crop, county, water source, and year for the Continental United States. Total irrigation from Surface Water Withdrawals (SWW), total Groundwater Withdrawals (GWW), and nonrenewable Groundwater Depletion (GWD) is provided for 20 crops and crop groups from 2008 to 2020 at the county spatial resolution. In total, there are nearly 2.5 million data points in this dataset (3,142 counties; 13 years; 3 water sources; and 20 crops). This dataset supports the paper by Ruess et al (2024) "Total irrigation by crop in the Continental United States from 2008 to 2020", Scientific Data, doi: 10.1038/s41597-024-03244-w When using, please cite as: Ruess, P.J., Konar, M., Wanders, N., and Bierkens, M.F.P. (2024) Total irrigation by crop in the Continental United States from 2008 to 2020, Scientific Data, doi: 10.1038/s41597-024-03244-w

This dataset contains leaf photosynthetic and biochemical traits, plant biomass, and yield in five C3 crops (chickpea, rice, snap bean, soybean, wheat) and four C4 crops (sorghum, maize, Miscanthus × giganteus, switchgrass) grown under ambient and elevated O3 concentration ([O3]) in the field at free-air O3 concentration enrichment (O3-FACE) facilities over the past 20 years.

The data files are for the paper entitled: Melting of the charge density wave by generation of pairs of topological defects in UTe2 to be published in Nature Physics. The data was obtained on a 300 mK custom designed Unisoku scanning tunneling microscope using the Nanonis module. All the data files have been named based on the Figure numbers that they represent.

This dataset was developed as part of an online survey study that investigates how phatic expressions—comments that are social rather than informative in nature—influence the perceived helpfulness of online peer help-giving replies in an asynchronous college course discussion forum. During the study, undergraduate students (N = 320) rated and described the helpfulness of examples of replies to online requests for help, both with and without four types of phatic expressions: greeting/parting tokens, other-oriented comments, self-oriented comments, and neutral comments.

This upload contains one additional set of datasets (RNASim10k, ten replicates) used in Experiment 2 of the EMMA paper (appeared in WABI 2023): Shen, Chengze, Baqiao Liu, Kelly P. Williams, and Tandy Warnow. "EMMA: A New Method for Computing Multiple Sequence Alignments given a Constraint Subset Alignment". The zipped file has the following structure: 10k |__R0 |__unaln.fas |__true.fas |__true.tre |__R1 ... # Alignment files: 1. `unaln.fas`: all unaligned sequences. 2. `true.fas`: the reference alignment of all sequences. 3. `true.tre`: the reference tree on all sequences. For other datasets that uniquely appeared in EMMA, please refer to the related dataset (which is linked below): Shen, Chengze; Liu, Baqiao; Williams, Kelly P.; Warnow, Tandy (2022): Datasets for EMMA: A New Method for Computing Multiple Sequence Alignments given a Constraint Subset Alignment. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2567453_V1

Total nitrogen leaching rates were calculated over the Mississippi Atchafalaya River Basin (MARB) using an integrated economic-biophysical modeling approach. Land allocation for corn production and total nitrogen application rates were calculated for crop reporting districts using the Biofuel and Environmental Policy Analysis Model (BEPAM) for 5 RFS2 policy scenarios. These were used as input in the Integrated BIosphere Simulator-Agricultural Version (Agro-IBIS) and the Terrestrial Hydrologic Model with Biogeochemistry (THMB) to calculate the nitrogen loss. Land allocation and total nitrogen application simulations were simulated for the period 2016-2030 for 303 crop reporting districts (https://www.nass.usda.gov/Data_and_Statistics/County_Data_Files/Frequently_Asked_Questions/county_list.txt). The final 2030 values are reported here. Both are stored in csv files. Units for land allocation are million ha and nitrogen application are million kg. The nitrogen leaching rates were modeled with a spatial resolution of 5' x 5' using the North American Datum of 1983 projection and stored in NetCDF files. The 30-year average is calculated over the last 30 years of the 45 years being simulated. Leaching rates are calculated in kg-N/ha.

This dataset contains the data for 16S and 23S rRNA alignments including their reference trees. The original alignments are from the Gutell Lab CRW, currently located at https://crw-site.chemistry.gatech.edu/DAT/3C/Alignment/.

We studied the effect of windstorm disturbance on forest invasive plants in southern Illinois. This data includes raw data on plant abundance at survey points, compiled data used in statistical analyses, and spatial data for surveyed plots and units. This file package also includes a readme.doc file that describes the data in detail, including attribute descriptions.

This dataset is associated with a larger manuscript published in 2022 in the Illinois Natural History Survey Bulletin to summarize all known records for nonindigenous aquatic mollusks in Illinois, and full sources are referenced within the manuscript. We examined museum holdings, literature accounts, publicly available databases sponsored by the U.S. Geological Survey (USGS) - Nonindigenous Aquatic Species program (http://nas.er.usgs.gov/.) and InvertEBase (invertebase.org). We also included sporadic field survey data of encounters of nonindigenous aquatic species from colleagues within the Illinois Natural History Survey, Illinois Department of Natural Resources, U.S. Fish and Wildlife Service, county forest preserve districts, and other natural resource agencies about their encounters with nonindigenous aquatic mollusk species. Lastly, we examined the role and utility of citizen-science data to document occurrences of nonindigenous aquatic mollusk species. We queried iNaturalist (www.inaturalist.org) for all available nonindigenous freshwater mollusk data for Illinois. Table heading descriptions (if not intuitive) are: “INHS verified” is whether an INHS staff member verified the record by observing vouchered specimen or photograph; “Source” is where a record was accessed or obtained; “individualCount” is number collected or observed in a record; “MuseumCode” is standard museum abbreviation or acronym; “Institution” is source that housed or reported a record, and this also includes the spelled-out museum code; “Collectors” typically indicates who collected the specimen or voucher; “Lat_Long determined by” denotes whether collection coordinates were stated by the collector or by a curator (using inference from data available); “fieldNumber” typically indicates a unique field number that a collector may have used in the field; “identifiedBy” typically explains who identified a specimen or verified a specimen identification.

This dataset contains the data for SATe-I. SATe-I data was used in the following article: K. Liu, S. Raghavan, S. Nelesen, C. R. Linder, T. Warnow, "Rapid and Accurate Large-Scale Coestimation of Sequence Alignments and Phylogenetic Trees," Science, vol. 324, no. 5934, pp. 1561-1564, 19 June 2009.

Data for the Appendices of Bush et al. article published in Ecology and Evolution. Contains genomic analysis information for a strain of Aspergillus flavus isolated from bee bread in East Central Illinois.

This is a collection of 31 quasi-linear convective system (QLCS) mesovortices (MVs) that were manually identified and analyzed using the lowest elevation scan of the nearest relevant Weather Surveillance Radar–1988 Doppler (WSR-88D) during the two years (springs of 2022 and 2023) of the Propagation, Evolution, and Rotation in Linear Storms (PERiLS) field campaign. Throughout the two years of PERiLS, a total of nine intensive observing periods (IOPs) occurred (see https://catalog.eol.ucar.edu/perils_2022/missions and https://catalog.eol.ucar.edu/perils_2023/missions for exact IOP dates/times). However, only six of these IOPs (specifically, IOPs 2, 3, and 4 from both years) are included in this dataset. The inclusion criteria were based on the presence of strictly QLCS MVs within the C-band On Wheels (COW) domain, one of the research radars deployed in the field for the PERiLS project. Further details on how MVs were identified are provided below. This analysis was completed using the Gibson Ridge radar-viewing software (GR2Analyst). Each MV had to be produced by a QLCS, defined as a continuous area of 35 dBZ radar reflectivity over at least 100 km when viewed from the lowest elevation scan. The MVs analyzed also had to pass through/near the COW’s domain at some point during their lifetimes to allow for additional analysis using the COW data. Tornadic (TOR), wind-damaging (WD), and non-damaging (ND) MVs were analyzed. ND MVs were ones that usually had a tornado warning placed on them but did not produce any damage and persisted for five or more radar scans; this was done to target the strongest MVs that forecasters thought could be tornadic. The QLCS MVs were identified using objective criteria, which included the existence of a circulation with a maximum differential velocity (dV; i.e., the difference between the maximum outbound and minimum inbound velocities at a constant range) of at least 20 kt over a distance ≤ 7 km. The following radar-based characteristics were catalogued for each QLCS MV at the lowest elevation angle of the nearest WSR-88D: latitude and longitude locations of the MV, the genesis to decay time of the MV, the maximum dV across the MV, the maximum rotational velocity (Vrot; i.e., dV divided by two), diameter of the MV, the range from the radar of the MV center, and the height above radar level of the MV center. In the Excel sheet, there are a total of 37 sheets. 32 of the 37 sheets are for each MV that was examined. One of those MVs (sheet titled 'EFU_tor_iop3') was not included in the final count of MVs (31). This MV produced an EFU tornado and only tornadoes that were given ratings were used to calculate MV statistics. The 31 MV sheets that were used to calculate MV statistics are labeled following the convention 'mv#_iop#_qlcs'. ‘mv#’ is the unique number that was assigned to each MV for clear identification, 'iop#' is the IOP in which the MV occurred, 'qlcs' denotes that the MV was produced by a QLCS, and the 2023 IOPs are denoted by ‘_2023’ after ‘qlcs’ in the sheet name. In these sheets, there are notes on what was visually seen in the radar data, damage associated with each MV (using the National Centers for Environmental Information (NCEI) database), and the characteristics of the MV at each time step of its lifetime. The yellow rows in each of the sheets indicate the last row of data included in the pretornadic, predamaging (wind damage), and pre-nondamaging statistics. The orange boxes in the notes column indicate any reports that were in NCEI but not in GR2Analyst. There are also sheets that examine pretornadic and predamaging diameter trends, box and whisker plot statistics of the overall characteristics of the different types of MVs, and the overall characteristics of each MV, with one Excel sheet (‘combined_qlcs_mvs’) examining the characteristics of each MV over its entire lifetime and one Excel sheet (‘combined_qlcs_mvs_before_report’) examining the characteristics of each MV before it first produced damage or had a tornado warning placed on it.