Dataset Search Results

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. This V4 contains new experimental data files, hourly fumigation files, and weather/ambient files for 2022 and 2023, since the original dataset only included files for 2001-2021. The MATLAB code has also been updated for efficiency, and explanatory files have been updated accordingly. Below are new changes in V4: - The "SoyFACE Plot Information 2001 to 2021" file is renamed to “SoyFACE ring information 2001 to 2023.xlsx”. Data for 2022 and 2023 were added. 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. - The "SoyFACE 1-Minute Fumigation Data Files" were updated to contain sub-folders for each year of the experiments (2001-2023), 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 ("SoyFACE Hourly Fumigation Data Files" folder) created from the 1-minute files, and hourly ambient/weather data files for each year of the experiments ("Hourly Weather and Ambient Data Files" folder which has also been updated to include 2022 and 2023 data). 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. - “Rings.xlsx” is new in this version. This file lists the rings and treatments used in each year of the SoyFACE experiments between 2001 and 2023 and is used in several of the MATLAB codes. - “CMI Weather Data Explanation.docx” is newly added. This file contains specific information about the processing of raw weather data, which is used in the hourly weather and ambient data files. - Files that were in RAR format in V3 are now updated and saved as ZIP format, including: Hourly Weather and Ambient Data Files.zip , SoyFACE 1-Minute Fumigation Data Files.zip , SoyFACE Hourly Fumigation Data Files.zip, and Matlab Files.zip. - The "Fumigation Target Percentages" file was updated to add data for 2022 and 2023. This 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 "Matlab Files" folder contains custom code (Aspray, E.K.) that was used to clean the "SoyFACE 1-Minute Fumigation Data" files and to generate the "SoyFACE Hourly Fumigation Data" and "Fumigation Target Percentages" files. Code information can be found in the various "Explanation" files. The Matlab code changes are as follows: 1. “Data_Issues_Finder.m” code was changed to use the “Ring.xlsx” file to gather ring and treatment information based on the contents of the file rather than being hardcoded in the Matlab code itself. 2. “Data_Issues_Finder_all.m” code is new. This code is the same as the “Data_Issues_Finder.m” code except that it identifies all CO2 and O3 repeats. In contrast, the “Data_Issues_Finder.m” code only identifies CO2 and O3 repeats that occur when the fumigation system is turned on. 3. “Target_Yearly.m” code was changed to use the “Ring.xlsx” file to gather ring and treatment information based on the contents of the file rather than being hardcoded in the Matlab code itself. 4. “HourlyFumCode.m” code is new. This code uses the “Rings.xlsx” file to gather ring and treatment information based on the contents of the file instead of the user needing to define these values explicitly. This code also defines a list of all ring folders for the year selected and runs the hourly code for each ring, instead of the user having to run the hourly code for each ring individually. Finally, the code generates two dialog boxes for the user, one which allows user to specify whether they want the hourly code to be run for 1-minute fumigation files or 1-minute ambient files, and another which allows user to specify whether they would like the hourly fumigation averages to be replaced with hourly ambient averages when the fumigation system is turned off. 5. “HourlyDataFun.m” code was changed to run either “HourlyData.m” code or “HourlyDataAmb.m” code, depending on user input in the first dialog box. 6. “HourlyData.m” code was changed to replace hourly fumigation averages with hourly ambient averages when the fumigation system is turned off, depending on user input in the second dialog box. 7. “HourlyDataAmb.m” code is new. This code is similar to “HourlyData.m” code but is used to calculate hourly averages for 1-minute ambient files instead 1-minute fumigation files. 8. “batch.m” code was changed to account for new function input variables in “HourlyDataFun.m” code, along with adding header columns for “FumOutput.xlsx” and “AmbOutput.xlsx” output files generated by “HourlyData.m” and “HourlyDataAmb.m” code. - Finally, the " * Explanation" files contain information about the column names, units of measurement, steps needed to use Matlab code, and other pertinent information for each data file. Some of them have been updated to reflect the current change of data.

Data associated with the manuscript "Stable isotopes and diet metabarcoding reveal trophic overlap between native and invasive Banded Killifish (Fundulus diaphanus) subspecies." by Jordan H. Hartman, Mark A. Davis, Nicholas J. Iacaruso, Jeremy S. Tiemann, Eric R. Larson. For this project, we sampled six locations in Michigan and Illinois for Eastern and Western Banded Killifish and primary consumers. Using stable isotope analysis we found that Eastern Banded Killifish had higher variance in littoral dependence and trophic position than Western Banded Killifish, but both stable isotope and gut content metabarcoding analyses revealed an overlap in the diet composition and trophic position between the subspecies. This dataset provides the sampling locations, accession numbers for gut content metabarcoding data from the National Center for Biotechnology Information Sequence Read Archive, the assignment of each family used in the gut content metabarcoding analysis as littoral, pelagic, terrestrial, or parasite. and the raw stable isotope data from University of California Davis.

This data and code accompany the manuscript "Small population size and possible extirpation of the threatened Malagasy poison frog Mantella cowanii". The data were collected using photograph capture-recapture at three sites in the central highlands of Madagascar. In Part 1, the script implements robust design capture-mark-recapture models in program MARK through the RMark interface to estimate population sizes and annual survival probabilities. In Part 2, it estimates the number of surveys needed to infer absence at sites where we did not detect the frog.

This dataset is associated with the manuscript "Transcriptional responses of detoxification genes to coumaphos in a nontarget species, Galleria mellonella (greater wax moth) (Lepidoptera: Pyralidae), in the beehive environment" This dataset includes 2 Excel files: 1) raw_data_bioassay.xlsx: this file contains the raw data for waxworm bioassay. There are 2 worksheets within this file: - LC50: raw data for measuring LC50 in the laboratory and field strain of Galleria mellonella. - RGR: Relative Growth Rate, raw data for measuring body weight of field strain of Galleria mellonella . 2) raw-data_RT-qPCR.xlsx: this file contains raw data (Ct value) of RT-qPCR.

The use of potentially beneficial microorganisms in agriculture (microbial inoculants) has rapidly accelerated in recent years. For microbial inoculants to be effective as agricultural tools, these organisms must be able to survive and persist in novel environments while not destabilizing the resident community or spilling over into adjacent natural ecosystems. Here, we adapt a macroecological propagule pressure model to a microbial scale and present an experimental approach for testing the role of propagule pressure in microbial inoculant introductions. We experimentally determined the risk-release relationship for an IAA-expressing Pseudomonas simiae inoculant in a model monocot system. We then used this relationship to simulate establishment outcomes under a range of application frequencies (propagule number) and inoculant concentrations (propagule size). Our simulations show that repeated inoculant applications may increase establishment, even when increased inoculant concentration does not alter establishment probabilities. The dataset filed here includes the experimemtal datafile, and a RMarkdown file that includes all the code used in in both the modeling and anaylsis.

Scripts used to computationally estimate the current through a DNA nanopore, starting from an equilibrated oxDNA configuration, in association with the manuscript "A lumen-tunable triangular DNA nanopore for molecular sensing and cross-membrane transport".

Dataset associated with Jones et al. GCB-23-1273.R1 submission: Phenotypic signatures of urbanization? Resident, but not migratory, songbird eye size varies with urban-associated light pollution levels. Excel CSV file with all of the data used in analyses and file with descriptions of each column.

This dataset includes the RT-PCR shedding data and primers used for whole genome sequencing of Influenza A virus in swine. It also includes the GenBank accession numbers for all segments generated by Influenza A virus sequencing from nasal swab samples. Additionally, all nucleotide changes are listed by sample.

This dataset contains all material required to produce the figures found within the manuscript submitted to Geoscientific Model Development entitled “Explicit stochastic advection algorithms for the regional scale particle-resolved atmospheric aerosol model WRF-PartMC (v1.0)”. The dataset consists of Python Jupyter notebooks and any applicable WRF-PartMC output. This dataset covers the three numerical examples of the manuscript, 1D advection by a uniform constant wind, a 2D rotational flow and a 3D time-evolving WRF simulated flow.

This dataset contains all data and supplementary materials from "Improving precision and accuracy of genetic mapping with genotyping-by-sequencing data in outcrossing species". An Excel file a list of all QTLs and linkage group length (in cM) obtained with two different SNP-calling methods (Tassel-Uneak and Tassel-GBS), genetic map-construction method (linkage-only and reference order-corrected) and depth filters (12x, 20x, 30x and 40x) for genetic mapping of 18 biomass yield traits in a biparental Miscanthus sinensis population using RAD-Seq SNPs is provided as "Supplementary file 1". A Perl script with the code for filtering VCF and HapMap-formatted data files is provided as “Supplementary file 2”. Phenotype data used for QTL mapping is provided as “Supplementary File 3”. A Perl script with the code for the simulation study is provided as “Supplementary file 4”.

Raw data for the results presented in Ferguson et al 2024.

Data for each figure of the article "Greater aperture counteracts effects of reduced stomatal density on WUE: a case study on sugarcane and meta-analysis" published in J. Ex. Bot.

This dataset contains data used to generate figures and tables in the corresponding paper.

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

The dataset contains trajectories of Pt nanoparticles in 1.98 mM NaBH4 and NaCl, tracked under liquid-phase TEM. The coordinates (x, y) of nanoparticles are provided, together with the conversion factor that translates pixel size to actual distance. In the file, ∆t denotes the time interval and NaN indicates the absence of a value when the nanoparticle has not emerged or been tracked. The labeling of nanoparticles in the paper is also noted in the second row of the file.

This dataset includes the gas exchange and TDL (tunable diode laser) files between 4 accessions of Glycine soja and 1 elite accession of Glycine max (soybean) during light induction. In this V2, code files for Matlab and R are also included to calculate mesophyll conductance and calculate the limitation on photosynthesis, respectively.

This dataset includes the RT-PCR results, RT-LAMP results, and the minutes to positive ROC curve calculations. This dataset includes data for the synthetic gBlock, cell culture, and clinical sample assays (nasal swabs and nasal wipes). Also included is a list of FDA approved point of care tests for influenza A virus to date (2-16-2024). MIQE guidelines are also included.

A defining feature of the Anthropocene is the distortion of the biosphere phosphorus (P) cycle. A relatively sudden acceleration of input fluxes without a concomitant increase in output fluxes has led to net accumulation of P in the terrestrial-aquatic continuum. Over the past century, P has been mined from geological deposits to produce crop fertilizers. When P inputs are not fully removed with harvest of crop biomass, the remaining P accumulates in soils. This residual P is a uniquely anthropogenic pool of P, and its management is critical for agronomic and environmental sustainability. This dataset includes data for us to quantify residual P from different long-term managed systems. The following is the desccription of the dataset. There are 7 sheets in total. 1. P_balance: From Morrow Plots maize-maize rotaiton (1888-2021), L: Low estimation; M: medium estimation; H: high estimation; 2. M3P: From Morrow Plots selected plots (selected years), M3P_sur: Mehlich III P concentration in surface 17cm soils; M3P_sub: Mehlich III P concentration in 17-34cm subsoils; P_balance: the difference between P inputs and P outputs; TP_sur: total P stocks in surface 17cm soils; TP_sub: total P stocks in 17-34cm subsoils; 3. Morrow_Plot_P_pool_all: Group: a - labile P; b - Fe/Al-P; c - Ca-P; d - total organic P; e - non-extractable P; Fertilized: P stocks in the fertilized plot; Unfertilized: P stocks in the unfertilized plot; F-U: difference between P stocks in ther fertilized and unfertilized plots; dif%: percent difference in total P; 4. Rothamsted_P_pool_all: Treatment: Unfertilized: no fertilization; FYM: farmyard manure; PK: synthetic P and K fertilizer; Group: a - labile P; b - Fe/Al-P; c - Ca-P; d - total organic P; e - non-extractable P; P_change: differnce in P stocks over time; dif%: percent difference in total P; 5. L'Acadie_P_pool_all: Treatment: MP_LowP: moldboard plow with low rate of P fertilizer; MP_HighP: moldboard plow with high rate of P fertilizer; NT_LowP: no till with low rate of P fertilizer; NT_HighP: no till with high rate of P fertilizer; Group: a - labile P; b - Fe/Al-P; c - Ca-P; d - total organic P; e - non-extractable P; P_change: differnce in P stocks over time; dif%: percent difference in total P; 6. Rothamsted_P_pool_duration: Treatment: Unfertilized: no fertilization; FYM: farmyard manure; PK: synthetic P and K fertilizer; Duration: from a year to another year; Group: a - labile P; b - Fe/Al-P; c - Ca-P; d - total organic P; e - non-extractable P; P_change: differnce in P stocks over time; dif%: percent difference in total P; 7. L'Acadie_P_pool_duration: Treatment: MP_LowP: moldboard plow with low rate of P fertilizer; MP_HighP: moldboard plow with high rate of P fertilizer; NT_LowP: no till with low rate of P fertilizer; NT_HighP: no till with high rate of P fertilizer; Duration: from a year to another year; Group: a - labile P; b - Fe/Al-P; c - Ca-P; d - total organic P; e - non-extractable P; P_change: differnce in P stocks over time; dif%: percent difference in total P;

U-2 OS MERFISH data set prepared by the Han lab at UIUC based off of procedures developed in Moffitt et al. Proc. Natl. Acad. Sci. USA 113 (39), 11046–11051. Data is comprised of ~2 million spots from 130 genes with x,y,z location, cell assignment, and correction status.

Supplemental data for the paper titled 'Environmental modulators of algae-bacteria interactions at scale'. Each of the excel workbooks corresponding to datasets 1, 2, and 3 contain a README sheet explaining the reported data. Dataset 4 comprising microscopy data contains a README text file describing the image files.

This page contains the data for the publication "Revisiting bacterial cytolethal distending toxin structure and function" published in Frontiers in Cellular and Infection Microbiology in 2023.

This page contains the data for the manuscript "Autophagy suppression in DNA damaged cells occurs through a newly identified p53-proteasome-LC3 axis" currently available in preprint on bioRxiv

Data includes carbon mineralization rates, potential denitrification rates, net nitrous oxide fluxes, and soil chemical properties from a laboratory incubation of soil samples collected from 20 locations across an Illinois maize field.

This dataset includes five files. Descriptions of the files are given as follows: <b>FILENAME: PubMed_retracted_publication_full_v3.tsv</b> - Bibliographic data of retracted papers indexed in PubMed (retrieved on August 20, 2020, searched with the query "retracted publication" [PT] ). - Except for the information in the "cited_by" column, all the data is from PubMed. - PMIDs in the "cited_by" column that meet either of the two conditions below have been excluded from analyses: [1] PMIDs of the citing papers are from retraction notices (i.e., those in the “retraction_notice_PMID.csv” file). [2] Citing paper and the cited retracted paper have the same PMID. ROW EXPLANATIONS - Each row is a retracted paper. There are 7,813 retracted papers. COLUMN HEADER EXPLANATIONS 1) PMID - PubMed ID 2) Title - Paper title 3) Authors - Author names 4) Citation - Bibliographic information of the paper 5) First Author - First author's name 6) Journal/Book - Publication name 7) Publication Year 8) Create Date - The date the record was added to the PubMed database 9) PMCID - PubMed Central ID (if applicable, otherwise blank) 10) NIHMS ID - NIH Manuscript Submission ID (if applicable, otherwise blank) 11) DOI - Digital object identifier (if applicable, otherwise blank) 12) retracted_in - Information of retraction notice (given by PubMed) 13) retracted_yr - Retraction year identified from "retracted_in" (if applicable, otherwise blank) 14) cited_by - PMIDs of the citing papers. (if applicable, otherwise blank) Data collected from iCite. 15) retraction_notice_pmid - PMID of the retraction notice (if applicable, otherwise blank) <b>FILENAME: PubMed_retracted_publication_CitCntxt_withYR_v3.tsv</b> - This file contains citation contexts (i.e., citing sentences) where the retracted papers were cited. The citation contexts were identified from the XML version of PubMed Central open access (PMCOA) articles. - This is part of the data from: Hsiao, T.-K., & Torvik, V. I. (manuscript in preparation). Citation contexts identified from PubMed Central open access articles: A resource for text mining and citation analysis. - Citation contexts that meet either of the two conditions below have been excluded from analyses: [1] PMIDs of the citing papers are from retraction notices (i.e., those in the “retraction_notice_PMID.csv” file). [2] Citing paper and the cited retracted paper have the same PMID. ROW EXPLANATIONS - Each row is a citation context associated with one retracted paper that's cited. - In the manuscript, we count each citation context once, even if it cites multiple retracted papers. COLUMN HEADER EXPLANATIONS 1) pmcid - PubMed Central ID of the citing paper 2) pmid - PubMed ID of the citing paper 3) year - Publication year of the citing paper 4) location - Location of the citation context (abstract = abstract, body = main text, back = supporting material, tbl_fig_caption = tables and table/figure captions) 5) IMRaD - IMRaD section of the citation context (I = Introduction, M = Methods, R = Results, D = Discussions/Conclusion, NoIMRaD = not identified) 6) sentence_id - The ID of the citation context in a given location. For location information, please see column 4. The first sentence in the location gets the ID 1, and subsequent sentences are numbered consecutively. 7) total_sentences - Total number of sentences in a given location 8) intxt_id - Identifier of a cited paper. Here, a cited paper is the retracted paper. 9) intxt_pmid - PubMed ID of a cited paper. Here, a cited paper is the retracted paper. 10) citation - The citation context 11) progression - Position of a citation context by centile within the citing paper. 12) retracted_yr - Retraction year of the retracted paper 13) post_retraction - 0 = not post-retraction citation; 1 = post-retraction citation. A post-retraction citation is a citation made after the calendar year of retraction. <b>FILENAME: 724_knowingly_post_retraction_cit.csv</b> (updated) - The 724 post-retraction citation contexts that we determined knowingly cited the 7,813 retracted papers in "PubMed_retracted_publication_full_v3.tsv". - Two citation contexts from retraction notices have been excluded from analyses. ROW EXPLANATIONS - Each row is a citation context. COLUMN HEADER EXPLANATIONS 1) pmcid - PubMed Central ID of the citing paper 2) pmid - PubMed ID of the citing paper 3) pub_type - Publication type collected from the metadata in the PMCOA XML files. 4) pub_type2 - Specific article types. Please see the manuscript for explanations. 5) year - Publication year of the citing paper 6) location - Location of the citation context (abstract = abstract, body = main text, back = supporting material, table_or_figure_caption = tables and table/figure captions) 7) intxt_id - Identifier of a cited paper. Here, a cited paper is the retracted paper. 8) intxt_pmid - PubMed ID of a cited paper. Here, a cited paper is the retracted paper. 9) citation - The citation context 10) retracted_yr - Retraction year of the retracted paper 11) cit_purpose - Purpose of citing the retracted paper. This is from human annotations. Please see the manuscript for further information about annotation. 12) longer_context - A extended version of the citation context. (if applicable, otherwise blank) Manually pulled from the full-texts in the process of annotation. <b>FILENAME: Annotation manual.pdf</b> - The manual for annotating the citation purposes in column 11) of the 724_knowingly_post_retraction_cit.tsv. <b>FILENAME: retraction_notice_PMID.csv</b> (new file added for this version) - A list of 8,346 PMIDs of retraction notices indexed in PubMed (retrieved on August 20, 2020, searched with the query "retraction of publication" [PT] ).