Dataset Search Results

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>

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

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

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.

Rising global temperatures and urban heat island effects challenge environmental health and energy systems at the city level, particularly in summer. Increased heatwaves raise energy demand for cooling, stressing power facilities, increasing costs, and risking blackouts. Heat impacts vary across cities due to differences in urban morphology, geography, land use, and land cover, highlighting vulnerable areas needing targeted heat mitigation. Urban tree canopies, a nature-based solution, effectively mitigate heat. Trees provide shade and cooling through evaporation, improving thermal comfort, reducing air conditioning energy consumption, and enhancing climate resilience. This report focused on the ComEd service area in the Chicago Metropolitan Region and assessed the impacts of population growth, urbanization, climate change, and an ambitious plan to plant 1 million trees. The report evaluated planting 1 million trees to quantify regional cooling effects projected for the 2030s. Afforestation locations were selected to avoid interference with existing infrastructure. Key findings include (i) extreme hot hours (>95°F) will increase from 30 to 200 per year, adding 420 Cooling Degree Days (CCD) by the 2030s, (ii) greener areas can be up to 10°F cooler than less vegetated neighborhoods in summer, (iii) tree canopies can create localized cooling, reducing temperatures by 0.7°F and lowering annual CCD by 60 to 65, and (iv) afforestation can reduce the region’s temperature by 0.7°F, saving 400 to 1100 Megawatt hours of daily power usage during summer. <b>Note: The data is available upon request from <a href="mailto:dpiclimate@uilliois.edu">dpiclimate@uilliois.edu</br>.

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 repository contains the data and computational analysis notebooks that were used in the following manuscript. For more information on the methods and contributing authors, please refer to the original manuscript. "Beyond A and B Compartments: how major nuclear locales define nuclear genome organization and function Omid Gholamalamdari et al. 2024"

The included files are the alignments of DNA or amino acid sequences used for phylogenetic analyses of Auchenorrhyncha (Insecta: Hemiptera) in the manuscript by Bin et al. submitted to the journal “Systematic Entomology.” The files are plain text in either FASTA (.fa or .fas suffix) or PHYLIP (.phy suffix) format. Matrix0 is the set of all loci after multiple sequence alignment and trimming (hereafter called). Matrix1 consists of loci having 75% average bootstrap support and 80% taxon completeness (hereafter called Matrix1). Matrix2 consists of loci having 75% average bootstrap support and 95% completeness. Matrix2_nt12 is the same as Matrix2 but with third codon positions excluded. More details on how the datasets were compiled is provided in the Methods section of the manuscript file, also included as a PDF. Supplemental figures for the submitted manuscript are also provided as a PDF for additional information.

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

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

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] ).

The materials used to provide Continuing Medical Education on ticks and tick-borne diseases in Illinois on February 1, 2023 at Carle Hospital, along with the pre- and post-quiz and deidentified data of the quiz takers. Files: "Ticks and Tick-borne Diseases of Illinois_Final_w_speaker_notes.pptx": Presentation slides used for CME course, with notes to indicate verbal commentary "CME assessment_final.docx": Pre- and post-CME quiz questions and answers, annotated to indicate correct answers and reasoning for incorrect answers "CME_prequiz_data_for_sharing.csv": De-identified data from pre-CME quiz "CME_postquiz_data_for_sharing.csv": De-identified data from post-CME quiz, including demographics "DataCleaning_forSharing.R": R file used to clean the raw data and calculate the scores "ReadMe.txt":

This repository contains the the data and code to recreate the simulations in "High Costs of GHG Abatement with Electrifying the Light-Duty Vehicle Fleet with Heterogeneous Preferences of Vehicle Consumers." The model can be run by calling the bash file in the SLURM environment with parameters set for different scenarios. BEPEAM-E model details: (1) the "Main.gms" file in GAMS format that contains the initiating stage settings with input and main optimization model (2) the "output.gms" file in GAMS format that prepare the output file from BEPAM model. (3) the rest are the intermediate input files for model to generate the input and output files for the model. (4) Four bash files are the script file that call the GAMS model on the HPC that includes both HPC environment and the scenario settings. Four bash files are uploaded corresponding to 4 scenarios

This dataset contains weather data taken at the University of Illinois Urbana-Champaign Energy Farm using automatic sensors and averaged every 15 minutes. Measurements include average air temperature, average relative humidity, average wind speed, maximum wind speed, average wind direction, average photosynthetically active radiation, total precipitation, and average air pressure.