Dataset Search Results

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.

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

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] ).

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.

Data are provided that are relevant to the rare plant Phlox pilosa ssp. sangamonensis, or Sangamon phlox, and other members of the genus that occur in its native range. Sangamon phlox is a state-endangered subspecies that is only known to occur in two Illinois counties. Data provided come from all known Sangamon phlox populations, which we estimate as 10 separate populations. Data include genetic data from DNA microsatellite loci (allele sizes and basic summaries), flowering population size estimates, rates of fruit set, and rates of seed set. Additionally, genetic data (from microsatellites) are provided for Phlox divaricata ssp. laphamii (three populations), Phlox pilosa ssp. pilosa (two populations), and Phlox pilosa ssp. fulgida (two populations).

This dataset contains all the datasets used in the study conducted for the research publication titled "Mapping dynamic human sentiments of heat exposure with location-based social media data". This paper develops a cyberGIS framework to analyze and visualize human sentiments of heat exposure dynamically based on near real-time location-based social media (LBSM) data. Large volumes and low-cost LBSM data, together with a content analysis algorithm based on natural language processing are used effectively to generate heat exposure maps from human sentiments on social media. ## What’s inside - A quick explanation of the components of the zip file * US folder includes the shapefile corresponding to the United State with County as spatial unit
 * Census_tract folder includes the shapefile corresponding to the Cook County with census tract as spatial unit * data/data.txt includes instruction to retrieve the sample data either from Keeling or figshare * geo/data20000.txt is the heat dictionary created in this paper, please refer to the corresponding publication to see the data creation process Jupyter notebook and code attached to this publication can be found at: https://github.com/cybergis/real_time_heat_exposure_with_LBSMD

Data from manuscript Atomic-Scale Visualization of a Cascade of Magnetic Orders in the Layered Antiferromagnet GdTe3, to be published in npj Quantum Materials. Powerpoint file has details on how the data can be opened and how the data are labeled.

Optical, AFM, and PFM image of α-In2Se3; Short-circuit current and open circuit voltage maps, I-V curve for different intensities; Dependence of the short-circuit current density, open-circuit voltage, depolarization field, and efficiency on intensity and thickness; Benchmarking the performance.

This dataset contains five files. (i) open_citations_jan2024_pub_ids.csv.gz, open_citations_jan2024_iid_el.csv.gz, open_citations_jan2024_el.csv.gz, and open_citation_jan2024_pubs.csv.gz represent a conversion of Open Citations to an edge list using integer ids assigned by us. The integer ids can be mapped to omids, pmids, and dois using the open_citation_jan2024_pubs.csv and open_citations_jan2024_pub_ids.scv files. The network consists of 121,052,490 nodes and 1,962,840,983 edges. Code for generating these data can be found https://github.com/chackoge/ERNIE_Plus/tree/master/OpenCitations. (ii) The fifth file, baseline2024.csv.gz, provides information about the metadata of PubMed papers. A 2024 version of PubMed was downloaded using Entrez and parsed into a table restricted to records that contain a pmid, a doi, and has a title and an abstract. A value of 1 in columns indicates that the information exists in metadata and a zero indicates otherwise. Code for generating this data: https://github.com/illinois-or-research-analytics/pubmed_etl. If you use these data or code in your work, please cite https://doi.org/10.13012/B2IDB-5216575_V1.

This dataset contains the training results (model parameters, outputs), datasets for generalization testing, and 2-D implementation used in the article "Learned 1-D passive scalar advection to accelerate chemical transport modeling: a case study with GEOS-FP horizontal wind fields." The article will be submitted to Artificial Intelligence for Earth Systems. The datasets are saved as CSV for 1-D time-series data and *netCDF for 2-D time series dataset. The model parameters are saved in every training epoch tested in the study.

Impact assessment is an evolving area of research that aims at measuring and predicting the potential effects of projects or programs. Measuring the impact of scientific research is a vibrant subdomain, closely intertwined with impact assessment. A recurring obstacle pertains to the absence of an efficient framework which can facilitate the analysis of lengthy reports and text labeling. To address this issue, we propose a framework for automatically assessing the impact of scientific research projects by identifying pertinent sections in project reports that indicate the potential impacts. We leverage a mixed-method approach, combining manual annotations with supervised machine learning, to extract these passages from project reports. This is a repository to save datasets and codes related to this project. Please read and cite the following paper if you would like to use the data: Becker M., Han K., Werthmann A., Rezapour R., Lee H., Diesner J., and Witt A. (2024). Detecting Impact Relevant Sections in Scientific Research. The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING). This folder contains the following files: evaluation_20220927.ods: Annotated German passages (Artificial Intelligence, Linguistics, and Music) - training data annotated_data.big_set.corrected.txt: Annotated German passages (Mobility) - training data incl_translation_all.csv: Annotated English passages (Artificial Intelligence, Linguistics, and Music) - training data incl_translation_mobility.csv: Annotated German passages (Mobility) - training data ttparagraph_addmob.txt: German corpus (unannotated passages) model_result_extraction.csv: Extracted impact-relevant passages from the German corpus based on the model we trained rf_model.joblib: The random forest model we trained to extract impact-relevant passages Data processing codes can be found at: https://github.com/khan1792/texttransfer