University Library, University of Illinois at Urbana-Champaign

This dataset contains all data used in the paper "Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids". The dataset includes genotypes and phenotypic data from two autotetraploid species Miscanthus sacchariflorus and Vaccinium corymbosum that was used used for genome wide association studies and genomic prediction and the scripts used in the analysis.

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.

This dataset contains the full data used in the paper titled "Enabling High Precision Gradient Index Control in Subsurface Multiphoton Lithography," available at https://doi.org/10.1021/acsphotonics.2c01950 . The data used for Table 1 can be found in the dataset for the related Figure 8. Some supplemental figures' data can be found in the main figures data: Figure S2's data is contained in Figure 6. Figure S4 and Table S1 data is derived from Figure 6. Figure S9 is derived from Figure 7. Figure S10 is contained in Figure 7. Figure S12 is derived from Figure 6 and the Python code prism-fringe-analysis. Figures without a data file named after them do not have any data affiliated with them and are purely graphical representations.

This datasets provide basis of our analysis in the paper - Potential Impacts on Ozone and Climate from a Proposed Fleet of Supersonic Aircraft. All datasets here can be categorized into emission data and model output data (WACCM). All the model simulations (background and perturbation) were run to steady-state and only the datasets used in analysis are archived here.

Curated networks and clustering output from the manuscript: Well-Connected Communities in Real-World Networks https://arxiv.org/abs/2303.02813

This dataset consists of all the figure files that are part of the main text of the manuscript titled "Magnetic-field sensitive charge density waves in the superconductor UTe2". For detailed information on the individual files refer to the readme file.

This data set is related to the SoyFACE experiments, which are open-air agricultural climate change experiments that have been conducted since 2001. The fumigation experiments take place at the SoyFACE farm and facility in Champaign County, Illinois during the growing season of each year, typically between June and October. - The <i>"SoyFACE Plot Information 2001 to 2021"</i> file contains information about each year of the SoyFACE experiments, including the fumigation treatment type (CO2, O3, or a combination treatment), the crop species, the plots (also referred to as 'rings' and labeled with numbers between 2 and 31) used in each experiment, important experiment dates, and the target concentration levels or 'setpoints' for CO2 and O3 in each experiment. - This data set includes files with minute readings of the fumigation levels (<i>"SoyFACE 1-Minute Fumigation Data Files"</i> folder) from the SoyFACE experiments. The <i>"Soyface 1-Minute Fumigation Data Files"</i> folder contains sub-folders for each year of the experiments, each of which contains sub-folders for each ring used in that year's experiments. This data set also includes hourly data files for the fumigation experiments (<i>"SoyFACE Hourly Fumigation Data Files"</i> folder) created from the 1-minute files, and hourly ambient/weather data files for each year of the experiments (<i>"Hourly Weather and Ambient Data Files"</i> folder). The ambient CO2 and O3 data are collected at SoyFACE, and the weather data are collected from the SURFRAD and WARM weather stations located near the SoyFACE farm. - The <i>"Fumigation Target Percentages"</i> file shows how much of the time the CO2 and O3 fumigation levels are within a 10 or 20 percent margin of the target levels when the fumigation system is turned on. - The <i>"Matlab Files"</i> folder contains custom code (Aspray, E.K.) that was used to clean the <i>"SoyFACE 1-Minute Fumigation Data"</i> files and to generate the <i>"SoyFACE Hourly Fumigation Data"</i> and <i>"Fumigation Target Percentages"</i> files. Code information can be found in the <i>"SoyFACE Hourly Fumigation Data Explanation"</i> file. - Finally, the <i>" * Explanation"</i> files contain information about the column names, units of measurement, and other pertinent information for each data file. *<b>NOTE:</b> We have identified some files in the “SoyFACE 1-Minute Fumigation Data Files” folder in our SoyFACE data set submission that were not downloaded properly - the files were present in the folder, but the actual files were empty. V3 ensures that there are no longer any empty files in the data set.

This dataset contains the historical and future (SSP3 and RCP7.0) CESM climate simulations used in the article "Large humidity effects on urban heat exposure and cooling challenges under climate change" (upcoming). Further details about these simulations can be found in the article. This dataset documents the monthly mean projections of air temperature, wet-bulb temperature, precipitation, relative humidity, and numerous other climatic variables for 2000-2009 (for the historical run) and for 2015-2100 (for the future projection under SSP3-RCP7). This dataset may be useful for urban planners, climate scientists, and decision-makers interested in changes in urban and rural climate under climate change.

A stochastic domination analysis model was developed to examine the effect that emerging carbon markets can have on the spatially varying returns and risk profiles of bioenergy crops relative to conventional crops. The code is written in MATLAB, and includes the calculated output. See the README file for instructions to run the code.

These data represent the raw data from the paper “Evaluating the ability of wetland mitigation banks to replace plant species lost from destroyed wetlands” published in Journal of Applied Ecology in 2023 by Stephen C. Tillman and Jeffrey W. Matthews.

This dataset includes mass spectrometry, library screening, and gas chromatography data used for creating a high-throughput screening in metabolic engineering.

Coups d'État are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d'État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e. realized or successful coups, unrealized coup attempts, or thwarted conspiracies) the type of actor(s) who initiated the coup (i.e. military, rebels, etc.), as well as the fate of the deposed leader. This current version, Version 2.1.2, adds 6 additional coup events that occurred in 2022 and updates the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrects a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixes this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removes two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and adds executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Changes from the previously released data (v2.0.0) also include: 1. Adding additional events and expanding the period covered to 1945-2022 2. Filling in missing actor information 3. Filling in missing information on the outcomes for the incumbent executive 4. Dropping events that were incorrectly coded as coup events <br> <b>Items in this Dataset</b> 1. <i>Cline Center Coup d'État Codebook v.2.1.2 Codebook.pdf</i> - This 16-page document provides a description of the Cline Center Coup d’État Project Dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d’État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. <i>Revised February 2023</i> 2. <i>Coup Data v2.1.2.csv</i> - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 981 observations. <i>Revised February 2023</i> 3. <i>Source Document v2.1.2.pdf</i> - This 315-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. <i>Revised February 2023</i> 4. <i>README.md</i> - This file contains useful information for the user about the dataset. It is a text file written in markdown language. <i>Revised February 2023</i> <br> <b> Citation Guidelines</b> 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2023. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.2. February 23. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V6 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2023. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.2. February 23. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V6

Data and documentation for Ornithological Applications manuscript “Integrating multiple data sources improves prediction and inference for upland game bird occupancy models” by Robert L. Emmet, Thomas J. Benson, Maximilian L. Allen, and Kirk W. Stodola We combined data from the North American Breeding Bird Survey and eBird with a targeted survey (IDNR upland game) to estimate habitat use of northern bobwhite and ring-necked pheasant in Illinois and to document the efficiency and overlap among the various data sources. Data include, eBird, USGS Breeding Bird Survey, National Land Cover Database, Upland game bird surveys, stream data)

Data sets from "DISCO+QR: Rooting Species Trees in the Presence of GDL and ILS." It contains trees and sequences simulated with gene duplication and loss under a variety of different conditions. Note: - trees.tar.gz contains the simulated gene-family trees used in our experiments (both true trees from SimPhy as well as trees estimated from alignments). - alignments.tar.gz contains simulated sequence data used for estimating the gene-family trees

This dataset includes supporting data for our article 'Assessing long-term impacts of cover crops on soil organic carbon in the central U.S. Midwestern agroecosystems'. The dataset contains carbon fluxes and SOC benefits from cover crops at six cover crop experiment sites in Illinois with three rotation systems: (1) without-cover-crop (maize-soybean rotations), (2) non-legume-preceding-maize (maize-annual ryegrass-soybean-annual ryegrass rotations), and (3) legume-preceding-maize (maize-cereal rye-soybean-hairy vetch rotations). <b>*NOTE:</b> there should be 13 files + 1 readme file, instead of 15 files as mentioned in readme.

Data files and R code to replicate the econometric analysis in the journal article: B Chen, BM Gramig and SD Yun. “Conservation Tillage Mitigates Drought Induced Soybean Yield Losses in the US Corn Belt.” Q Open. https://doi.org/10.1093/qopen/qoab007

This dataset is provided to support the statements in Kim, H., and R.Y. Makhnenko. 2022. "Evaluation of CO2 sealing potential of heterogeneous Eau Claire shale". Journal of the Geological Society. In geologic carbon dioxide (CO2) storage in deep saline aquifers, buoyant CO2 tends to float upwards in the reservoirs overlaid by low permeable formations called caprocks. Caprocks should serve as barriers to potential CO2 leakage that can happen through a diffusion loss and permeation through faults, fractures, or pore spaces. The leakage through intact caprock would mainly depend on its permeability and CO2 breakthrough pressure, and is affected by the heterogeneities in the material. Here, we study the sealing potential of a caprock from Illinois Basin - Eau Claire shale, with sandy and shaly fractions distinguished via electron microscopy and grain/pore size and surface area characterization. The direct measurements of permeability of sandy shale provides the values ~ 10-15 m2, while clayey specimens are three orders of magnitude less permeable. The CO2 breakthrough pressure under in-situ stress conditions is 0.1 MPa for the sandy shale and 0.4 MPa for the clayey counterpart – these values are higher than those predicted by the porosimetry methods performed on the unconfined specimens. Sandy Eau Claire shale would allow penetration of large CO2 volumes at low overpressures, while the clayey formation can serve as a caprock in the absence of faults and fractures in it.

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 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.

The following files were used to reconstruct the phylogeny of the leafhopper subfamily Typhlocybinae, using IQ-TREE v1.6.12 and ASTRAL v 4.10.5. <b>1) Taxon_sampling.csv:</b> contains the sample IDs (1st column) and the taxonomic information (2nd column). Sample IDs were used in the alignment files and partition files. <b>2) concatenated_nt_complete.phy:</b> a complete concatenated nucleotide dataset used for the maximum likelihood analysis by IQ-TREE v1.6.12. The file lists the sequences of 248 samples with 154,992 nucleotide positions (intron included) from 665 loci. Hyphens are used to represent gaps. <b>3) concatenated_nt_complete_partition.nex:</b> the partitioning schemes for concatenated_nt_complete.phy. The file partitions the 154,992 nucleotide characters into 426 character sets, and defines the best substitution model for each character set. <b>4) concatenated_cds_complete.phy:</b> a complete concatenated coding DNA sequence dataset used for the maximum likelihood analysis by IQ-TREE v1.6.12. The file lists the sequences of 248 samples with 153,525 nucleotide positions (intron excluded) from 665 loci. Hyphens are used to represent gaps. <b>5) concatenated_cds_complete_partition.nex:</b> the partitioning schemes for concatenated_cds_complete.phy. The file partitions the 153,525 nucleotide characters into 426 character sets, and defines the best substitution model for each character set. <b>6) concatenated_nt_reduced.phy:</b> a reduced concatenated nucleotide dataset used for the maximum likelihood analysis by IQ-TREE v1.6.12. The file lists the sequences of 248 samples with 95,076 nucleotide positions (intron included) from 374 loci. Hyphens are used to represent gaps. <b>7) concatenated_nt_reduced_partition.nex:</b> the partitioning schemes for concatenated_nt_reduced.phy. The file partitions the 95,076 nucleotide characters into 312 character sets, and defines the best substitution model for each character set. <b>8) concatenated_aa_complete.phy:</b> a complete concatenated amino acid dataset used for the maximum likelihood analysis by IQ-TREE v1.6.12, corresponding to concatenated_cds_complete.phy. The file lists the sequences of 248 samples with 51,175 amino acid positions from 665 loci. Hyphens are used to represent gaps. <b>9) concatenated_aa_complete_partition.nex:</b> the partitioning schemes for concatenated_aa_complete.phy. The file partitions the 51,175 amino acid characters into 426 character sets, and defines the best substitution model for each character set. <b>10) concatenated_aa_reduced.phy:</b> a reduced concatenated amino acid dataset used for the maximum likelihood analysis by IQ-TREE v1.6.12, corresponding to concatenated_nt_reduced.phy. The file lists the sequences of 248 samples with 31,384 amino acid positions from 374 loci. Hyphens are used to represent gaps. <b>11) concatenated_aa_reduced_partition.nex:</b> the partitioning schemes for concatenated_aa_reduced.phy. The file partitions the 31,384 amino acid characters into 312 character sets, and defines the best substitution model for each character set. <b>12) Individual_gene_alignment.zip:</b> contains 426 FASTA files, each one is an alignment for a gene. Hyphens are used to represent gaps. These files were used to construct gene trees using IQ-TREE v1.6.12, followed by multispecies coalescent analysis using ASTRAL v 4.10.5 based the consensus trees with a minimum average bootstrap value of 70.

Airglow images and Meteor radar data used in the paper "Mesospheric gravity wave activity estimated via airglow imagery, multistatic meteor radar, and SABER data taken during the SIMONe–2018 campaign".

Video recorded by Louise Barker using a Cannon Powershot camera documents late-season combat behavior in Agkistrodon contortrix. Recorded in Beaufort County, North Carolina, 11.1 km SE of downtown Washington on 21 October 2020.

This data contains bam files and transcripts in the simulated instances generated for the paper 'JUMPER: Discontinuous Transcript Assembly in SARS-CoV-2' submitted for RECOMB 2021. The folder 'bam' contained the simulated bam files aligned using STAR wile the reads were generated using the method polyester Note: in the readme file, close to the end of the document, please ignore this sentence: 'Those files can be opened by using [name of software].'

Supporting information for "Urinary Phthalate Metabolite Concentrations and Hot Flashes in Pre- and Perimenopausal Women from the Midlife Women’s Health Study." This file contains tables of the results of stratified analyses of the associations of hot flash outcomes with urinary phthalates metabolites by menopause status, race/ethnicity, body mass index, and depressive status. This file also contains supplementary HPLC methods for the analysis of phthalate metabolites.

This version 2 dataset contains 34 files in total with one (1) additional file, called "Culture-dependent Isolate table with taxonomic determination and sequence data.csv". The remaining files (33) are identical to version 1. The following is the information about the new file and its variables: <b>Culture-dependent Isolate table with taxonomic determination and sequence data.csv</b>: Culture table with assigned taxonomy from NCBI. Single direction sequence for each isolate is include if one could be obtained. Sequence is derived from ITS1F-ITS4 PCR amplicons, with Sanger sequencing in one direction using ITS5. The files contains 20 variables with explanation as below: IsolateNumber : unique number identify each isolate cultured Time: season in which the sample was collected Location: the specific name of the location Habitat: type of habitat : either stream or peatland State: state in the USA in which the specific location is located Incubation_pH ID: pH of the medium during isolation of fungal cultures Genus: phylogenetic genus of the fungal isolates (determined by sequence similarity) Sequence_quality: base call quality of the entire sequence used for blast analysis, if known %_coverage: sequence coverage reported from GenBank %_ID: sequence similarity reported from GenBank Life_style : ecological life style if known Phylum: phylogenetic phylum as indicated by Index Fungorum Subphylum: phylogenetic subphylum as indicated by Index Fungorum Class: phylogenetic class as indicated by Index Fungorum Subclass: phylogenetic subclass as indicated by Index Fungorum Order: phylogenetic order as indicated by Index Fungorum Family: phylogenetic Family as indicated by Index Fungorum ITS5_Sequence: single direction sequence used for sequence similarity match using blastn. Primer ITS5 Fasta: sequence with nomenclature in a fasta format for easy cut and paste into phylogenetic software Note: blank cells mean no data is available or unknown.