Dataset Search Results

The dataset is for a study conducted to understand genome-wide association (GWA) and genomic prediction of biomass yield and 14 yield-components traits in Miscanthus sacchariflorus. We evaluated a diversity panel with 590 accessions of M. sacchariflorus grown across four years in one subtropical and three temperate locations and genotyped with 268,109 single nucleotide polymorphisms (SNPs).

Data sets to reproduce the results provided by the tutorial in paper "Data-Driven and Machine Learning Based Framework for Image-Guided Single-Cell Mass Spectrometry"

Example data for https://github.com/chenlabUIUC/UsiNet The data contains computer simulated and experimental tilting series (or sinograms) of gold nanoparticles. Two training data examples are provided: 1. simulated_data.zip 2. experimental_data.zip In each zip folder, we include an image_data.zip and a training_data.zip. The former is for viewing and only the latter is needed for model training. For more details, please refer to our GitHub repository.

This data set contains data used for “Improved Net Carbon Budgets in the US Midwest through Direct Measured Impacts of Enhanced Weathering.” Data include biomass, soil bulk densities, soil respiration measurements, soil lanthanide element analysis, plant tissue analysis for major cations, and eddy covariance fluxes.

Data for Post-retraction citation: A review of scholarly research on the spread of retracted science Schneider, Jodi; Das, Susmita; Léveillé, Jacqueline; Proescholdt, Randi Contact: Jodi Schneider jodi@illinois.edu & jschneider@pobox.com ********** OVERVIEW ********** This dataset provides further analysis for an ongoing literature review about post-retraction citation. This ongoing work extends a poster presented as: Jodi Schneider, Jacqueline Léveillé, Randi Proescholdt, Susmita Das, and The RISRS Team. Characterization of Publications on Post-Retraction Citation of Retracted Articles. Presented at the Ninth International Congress on Peer Review and Scientific Publication, September 8-10, 2022 hybrid in Chicago. https://hdl.handle.net/2142/114477 (now also in https://peerreviewcongress.org/abstract/characterization-of-publications-on-post-retraction-citation-of-retracted-articles/ ) Items as of the poster version are listed in the bibliography 92-PRC-items.pdf. Note that following the poster, we made several changes to the dataset (see changes-since-PRC-poster.txt). For both the poster dataset and the current dataset, 5 items have 2 categories (see 5-items-have-2-categories.txt). Articles were selected from the Empirical Retraction Lit bibliography (https://infoqualitylab.org/projects/risrs2020/bibliography/ and https://doi.org/10.5281/zenodo.5498474 ). The current dataset includes 92 items; 91 items were selected from the 386 total items in Empirical Retraction Lit bibliography version v.2.15.0 (July 2021); 1 item was added because it is the final form publication of a grouping of 2 items from the bibliography: Yang (2022) Do retraction practices work effectively? Evidence from citations of psychological retracted articles http://doi.org/10.1177/01655515221097623 Items were classified into 7 topics; 2 of the 7 topics have been analyzed to date. ********************** OVERVIEW OF ANALYSIS ********************** DATA ANALYZED: 2 of the 7 topics have been analyzed to date: field-based case studies (n = 20) author-focused case studies of 1 or several authors with many retracted publications (n = 15) FUTURE DATA TO BE ANALYZED, NOT YET COVERED: 5 of the 7 topics have not yet been analyzed as of this release: database-focused analyses (n = 33) paper-focused case studies of 1 to 125 selected papers (n = 15) studies of retracted publications cited in review literature (n = 8) geographic case studies (n = 4) studies selecting retracted publications by method (n = 2) ************** FILE LISTING ************** ------------------ BIBLIOGRAPHY ------------------ 92-PRC-items.pdf ------------------ TEXT FILES ------------------ README.txt 5-items-have-2-categories.txt changes-since-PRC-poster.txt ------------------ CODEBOOKS ------------------ Codebook for authors.docx Codebook for authors.pdf Codebook for field.docx Codebook for field.pdf Codebook for KEY.docx Codebook for KEY.pdf ------------------ SPREADSHEETS ------------------ field.csv field.xlsx multipleauthors.csv multipleauthors.xlsx multipleauthors-not-named.csv multipleauthors-not-named.xlsx singleauthors.csv singleauthors.xlsx *************************** DESCRIPTION OF FILE TYPES *************************** BIBLIOGRAPHY (92-PRC-items.pdf) presents the items, as of the poster version. This has minor differences from the current data set. Consult changes-since-PRC-poster.txt for details on the differences. TEXT FILES provide notes for additional context. These files end in .txt. CODEBOOKS describe the data we collected. The same data is provided in both Word (.docx) and PDF format. There is one general codebook that is referred to in the other codebooks: Codebook for KEY lists fields assigned (e.g., for a journal or conference). Note that this is distinct from the overall analysis in the Empirical Retraction Lit bibliography of fields analyzed; for that analysis see Proescholdt, Randi (2021): RISRS Retraction Review - Field Variation Data. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2070560_V1 Other codebooks document specific information we entered on each column of a spreadsheet. SPREADSHEETS present the data collected. The same data is provided in both Excel (.xlsx) and CSV format. Each data row describes a publication or item (e.g., thesis, poster, preprint). For column header explainations, see the associated codebook. ***************************** DETAILS ON THE SPREADSHEETS ***************************** field-based case studies CODEBOOK: Codebook for field --REFERS TO: Codebook for KEY DATA SHEET: field REFERS TO: Codebook for KEY --NUMBER OF DATA ROWS: 20 NOTE: Each data row describes a publication/item. --NUMBER OF PUBLICATION GROUPINGS: 17 --GROUPED PUBLICATIONS: Rubbo (2019) - 2 items, Yang (2022) - 3 items author-focused case studies of 1 or several authors with many retracted publications CODEBOOK: Codebook for authors --REFERS TO: Codebook for KEY DATA SHEET 1: singleauthors (n = 9) --NUMBER OF DATA ROWS: 9 --NUMBER OF PUBLICATION GROUPINGS: 9 DATA SHEET 2: multipleauthors (n = 5 --NUMBER OF DATA ROWS: 5 --NUMBER OF PUBLICATION GROUPINGS: 5 DATA SHEET 3: multipleauthors-not-named (n = 1) --NUMBER OF DATA ROWS: 1 --NUMBER OF PUBLICATION GROUPINGS: 1 ********************************* CRediT <http://credit.niso.org> ********************************* Susmita Das: Conceptualization, Data curation, Investigation, Methodology Jaqueline Léveillé: Data curation, Investigation Randi Proescholdt: Conceptualization, Data curation, Investigation, Methodology Jodi Schneider: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Supervision

This dataset is about a topographic LIDAR survey (saved in “waxlake-lidar.img”) that was conducted over the Wax Lake delta, between longitudes −91.5848 to −91.292 degrees, and latitudes 29.3647 to 29.6466 degrees. Different from other elevation data, the positive value in the LIDAR data indicates land elevation, while the zero value implies riverbed without identifying specific water depth.

The data are original electron micrographs from the lab of the late Dr. Burt Endo of the USDA. These data were digitized from photographic prints and glass plate negatives at 600 DPI as 16 bit TIFF files. This fourth version added 6 new ZIP files from the Endo data collection. "Endo folder database.xlsx" is updated to reflect the addition. Information in "Readme_FileNameFormatting.docx" remains the same as in V3.

This is the first version of the dataset. This dataset contains anonymize data collected during the experiments mentioned in the publication: “I can show what I really like.”: Eliciting Preferences via Quadratic Voting that would appear in April 2021. Once the publication link is public, we would provide an update here. These data were collected through our open-source online systems that are available at (experiment1)[https://github.com/a2975667/QV-app] and (experiment 2)[https://github.com/a2975667/QV-buyback] There are two folders in this dataset. The first folder (exp1_data) contains data collected during experiment 1; the second folder (exp2_data) contains data collected during experiment 2.

This dataset accompanies an article published in the journal Bioacoustics: "Tradeoffs in sound quality and cost for passive acoustic devices", https://doi.org/10.1080/09524622.2023.2290715. The dataset contains measurements for acoustic call files for free-flying bats simultaneously recorded on both Audiomoth and Anabat Swift passive acoustic recording devices in a conservation area in northeastern Missouri, USA. We paired calls from the two devices and compared indicators of recording quality measured in a proprietary program (Bat Call Identification Software). The dataset also contains a file enumerating the proportions of calls classified as low frequency, mid frequency, or Myotis (three phonic groups) for each type of recording device. The data were used to compare the quality and sensitivity of the two devices. The scripts for modeling procedures and figures are included in the dataset.

A two-year field study was conducted to test the hypothesis that biochar application increases inorganic soil N availability during maize growth, leading to higher grain yields and N recovery efficiency while reducing the risk of N leaching following harvest. Four N fertilizer rates (0, 90, 179, and 269 kg ha-1 as urea ammonium nitrate solution) were applied with or without biochar (10 Mg ha-1) before maize planting each year. This dataset contains selected summary statistics (average and standard deviation) on soil and plant measurements. This file package also includes a readme.txt file that describes the data in detail, including attribute descriptions.

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.

Complete soils dataset for the La Planada forest dynamics plot associated with publication: John et al. (2007) "Soil nutrients influence the spatial distributions of tropical tree species" PNAS 104:864-869 www.pnas.org/cgi/doi/10.1073/pnas.0604666104

Simulation trajectory data and scripts for Nature manuscript "The structure and physical properties of a packaged bacteriophage particle" that reports the all-atom structure of a complete HK97 virion, including its entire 39,732 base pair genome, obtained through multi-resolution simulations.

The included files were used to reconstruct the phylogeny of Coelidiinae using combined morphological and molecular data, estimate divergence times and reconstruct ancestral biogeographic areas as described in the manuscript submitted for publication. The file “Coelidiinae_dna_morph_combined.nex” is a text file in standard NEXUS format used by various phylogenetic analysis programs. This file includes the aligned and concatenated nucleotide sequences or five gene regions (mitochondrial COI and 16S, and nuclear 28S D-2, histone H3, histone H2A and wingless) indicated by standard “ACGT” nucleotide symbols with missing data indicated by “?”, and morphological character data as defined in Table S3 used in the analyses. The data partitions are indicated toward the end of the file by ranges of numbers (“charset Subset 1 – 4” for the DNA data and “charset morph” for the morphological characters) followed by commands for the phylogenetic analysis program MrBayes that specify the model settings for each data partition. Detailed data on species included (as rows) in the dataset, including collection localities and GenBank accession numbers are provided in the Table_S1_Specimen_information.csv file. The file "TablesS2-S4.pdf" lists the primers used for polymerase chain reaction amplification, the list of morphological character definitions, and the morphological character matrix. The file “RASP_Distribution.csv” contains a list of the species included in the phylogenetic dataset (first column) and a code (second column) indicating their distributions as follows: (A) Oriental, (B) Palaearctic, (C) Australian, (D) Afrotropical, (E) Neotropical, and (F) Nearctic. More than one letter indicates that the species occurs in more than one region. The file "infile_for_BEAST.txt" is the input file in XML format used for the molecular divergence time analysis using the program BEAST (Bayesian Evolutionary Analysis by Sampling Trees) as described in the Methods section of the manuscript. This file includes comments that document the steps of the analysis.

Three datasets: species_abundance_data, species_traits, and environmental_data. The three datasets were collected in the Fortuna Forest Reserve (8°45′ N, 82°15′ W) and Palo Seco Protected Forest (8°45′ N, 82°13′ W) located in western Panama. The two reserves support humid to super-humid rainforests, according to Holdridge (1947). The species_abundance_data and species_traits datasets were collected across 15 subplots of 25 m2 in 12 one-hectare permanent plots distributed across the two reserves. The subplots were spaced 20 m apart along three 5 m wide transects, each 30 m apart. Please read Prada et al. (2017) for details on the environmental characteristics of the study area. Prada CM, Morris A, Andersen KM, et al (2017) Soils and rainfall drive landscape-scale changes in the diversity and functional composition of tree communities in a premontane tropical forest. J Veg Sci 28:859–870. https://doi.org/10.1111/jvs.12540

Trained models for multi-task multi-dataset learning for text classification in tweets. Classification tasks include sentiment prediction, abusive content, sarcasm, and veridictality. Models were trained using: <a href="https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification.py">https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification.py</a> See <a href="https://github.com/socialmediaie/SocialMediaIE">https://github.com/socialmediaie/SocialMediaIE</a> and <a href="https://socialmediaie.github.io">https://socialmediaie.github.io</a> for details. If you are using this data, please also cite the related article: Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929

Dataset associated with Jones and Ward JAE-2020-0031.R1 submission: Pre-to post-fledging carryover effects and the adaptive significance of variation in wing development for juvenile songbirds. Excel CSV files with data used in analyses and file with descriptions of each column. The flight ability variable in this dataset was derived from fledgling drop tests, examples of which can be found in the related dataset: Jones, Todd M.; Benson, Thomas J.; Ward, Michael P. (2019): Flight Ability of Juvenile Songbirds at Fledgling: Examples of Fledgling Drop Tests. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2044905_V1.

This dataset contains simulation results from PartMC-MOSAIC-CAPRAM used in the article ”Eval- uating the impacts of cloud processing on resuspended aerosol particles after cloud evaporation using a particle-resolved model”. In this V2, there are eight folders: one for urban plume simulation to provide the initial particle population for cloud processing, the other four folders are for the four cloud cycles simulated and the last two are for the coagulation cases. Within the urban plume simulation, there are 25 NetCDF files hourly output from PartMC-MOSAIC simulations containing the gas and particle information. Within the four cloud cycle folders, there are 25 subdirectories that contain the cloud processing results for aerosol population from urban plume environment. For each subdirectory, there are 31 NetCDF files out- put every minute from PartMC-MOSAIC-CAPRAM simulations containing aerosol and gas information after aqueous chemistry. Another two folders are for the cases considering Brownian coagulation and sedimentation coalescence. Each contained 93 NetCDF files, produced from repeating the 30-minutes simulations for three times to consider the coagulation randomness. The low polluted case folder includes the simulated cloud processing results for 25 urban plume cases with less aerosol number concentration. This dataset was used to investigate the effects of cloud processing on aerosol mixing state and CCN properties.

This dataset consists of all the files that are part of the manuscript titled "Evidence for a robust sign-changing s-wave order parameter in monolayer films of superconducting Fe(Se,Te)/Bi2Te3". For detailed information on the individual files refer to the readme file.

This dataset includes microclimate species distribution models at a ~3 m2 spatial resolution and free-air temperature species distribution models at ~0.85 km2 spatial resolution for three plethodontid salamander species (Demognathus wrighti, Desmognathus ocoee, and Plethodon jordani) across Great Smoky Mountains National Park. We also include heatmaps representing the differences between microclimate and free-air species distribution models and polygon layers representing the fragmented habitat for each species' predicted range. All datasets include predictions for 2010, 2030, and 2050.

Trained models for multi-task multi-dataset learning for sequence tagging in tweets. Sequence tagging tasks include POS, NER, Chunking, and SuperSenseTagging. Models were trained using: <a href="https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_experiment.py">https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_experiment.py</a> See <a href="https://github.com/socialmediaie/SocialMediaIE">https://github.com/socialmediaie/SocialMediaIE</a> and <a href="https://socialmediaie.github.io">https://socialmediaie.github.io</a> for details. If you are using this data, please also cite the related article: Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929

Traces created using DeathStarBench (https://github.com/delimitrou/DeathStarBench) benchmark of microservice applications with injected failures on containers. Failures consist of disk/CPU/memory failures.

The dataset includes the survey results about farmers’ perceptions of marginal land availability and the likelihood of a land pixel being marginal based on a machine learning model trained from the survey. Two spreadsheet files are the farmer and farm characteristics (marginal_land_survey_data_shared.xlsx), and the existing land use of marginal lands (land_use_info_sharing.xlsx). <b>Note:</b> the blank cells in these two spreadsheets mean missing values in the survey response. The GeoTiff file includes two bands, one the marginal land likelihood in the Midwestern states (0-1), the other the dominant reason of land marginality (0-5; 0 for farm size, 1 for growing season precipitation, 2 for root zone soil water capacity, 3 for average slope, 4 for growing season mean temperature, and 5 for growing season diurnal range of temperature). To read the data, please use a GIS software such as ArcGIS or QGIS.

This is the complete dataset for the "Anomalous density fluctuations in a strange metal" Proceedings of the National Academy of Sciences publication (https://doi.org/10.1073/pnas.1721495115). This is an integration of the Zenodo dataset which includes raw M-EELS data. <b>METHODOLOGICAL INFORMATION</b> 1. Description of methods used for collection/generation of data: Data have been collected with a M-EELS instrument and according to the data acquisition protocol described in the original PNAS publication and in SciPost Phys. 3, 026 (2017) (doi: 10.21468/SciPostPhys.3.4.026) 2. Methods for processing the data: Raw data were collected with a channeltron-based M-EELS apparatus described in the reference PNAS publication and analyzed according to the procedure outlined both in the PNAS paper and in SciPost Phys. 3, 026 (2017) (doi: 10.21468/SciPostPhys.3.4.026). The raw M-EELS spectra at each momentum have been subject to minor data processing involving: (a) averaging of different acquisitions at the same conditions, (b) energy binning, (c) division of an effective Coulomb matrix element (which yields a structure factor S(q,\omega)), (d) antisymmetrization (which yields the imaginary chi) All these procedures are described in the PNAS paper. 3. Instrument- or software-specific information needed to interpret the data: These data are simple .txt or .dat files which can be read with any standard data analysis software, notably Python notebooks, MatLab, Origin, IgorPro, and others. We do not include scripts in order to provide maximum flexibility. 4. Relationship between files, if important: We divided in different folders raw data, structure factors and imaginary chi. <b>DATA-SPECIFIC INFORMATION</b> There are 8 folders within the Data_public_deposition_v1.zip. Each folder contain data needed to create the corresponding figure in the publication. <b>1. Fig1:</b> This folder contains 21 DAT files needed to plot the theory data in panels C and D, following this naming conventions: [chiA]or[chiB]or[Pi]_q_number.dat With chiA is the imaginary RPA charge susceptibility with a Coulomb interaction of electronically weakly coupled layers chiB is the imaginary RPA charge susceptibility with the usual 4\pi e^2/q^2 Coulomb interaction. Pi is the imaginary Lindhard polarizability. q is momentum in reciprocal lattice units Number is the numerical momentum value in reciprocal lattice units <b>2. Fig2:</b> Files needed to plot Fig. 2 of the PNAS paper. Contains 3 folders as listed below. The files in this folder are named following this convention: Bi2212_295K_(1,-1)_50eV_161107_q_number_2.16_avg.dat, 295K is the sample temperature (1,-1) is the momentum direction in reciprocal lattice units 50 eV is the incident e beam energy 161107 is the start date of the experiment in yymmdd format Q is the momentum Number is the momentum in reciprocal lattice units 2.16 is the energy range covered by the data in eV Avg identifies averaged data ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra <b>3. Fig3:</b> Files needed to plot Fig. 3 of the PNAS paper. OP/ OD prefix identifies optimally doped or overdosed sample data, respectively. ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra <b>4. Fig4:</b> Files needed to plot Fig. 4 of the PNAS paper. The _fit_parameters.dat file contains the fit parameters extracted according to the fit procedure described in the manuscript and at all momenta. ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra <b>5. FigS1:</b> Files needed to plot Fig. S1 of the PNAS paper. There are 5 files in this folder. DAT files are M-EELS data following the prior naming convention, while the two .txt files are digitized data from N. Nücker, U. Eckern, J. Fink, and P. Müller, Long-Wavelength Collective Excitations of Charge Carriers in High-Tc Superconductors, Phys. Rev. B 44, 7155(R) (1991), and K. H. G. Schulte, The interplay of Spectroscopy and Correlated Materials, Ph.D. thesis, University of Groningen (2002). <b>6. FigS2:</b> Files needed to plot Fig. S2 of the PNAS paper. ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra <b>7. FigS3:</b> Files needed to plot Fig. S3 of the PNAS paper. There are 2 files in this folder: 20K_phi_0_q_0.dat: is a M-EELS raw intensity at zero momentum transfer on Bi2212 at 20 K 295K_phi_0_q_0.dat: is a M-EELS raw intensity at zero momentum transfer on Bi2212 at 295 K <b>8. FigS4:</b> Files needed to plot Fig. S4 of the PNAS paper. The _fit_parameters.dat file contains the fit parameters extracted according to the fit procedure described in the manuscript and at all momenta. ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra