Find research datasets worth reusing

opencc-by-4.0Jun 2024View details →

Source data for the publication "Ultra-dispersive resonator readout of a quantum-dot qubit using longitudinal coupling"

<div> This repository contains data and source code for the publication "Ultra-dispersive resonator readout of a quantum-dot qubit using longitudinal coupling." </div>

opencc-by-4.0Nov 2024View details →

Data and code for the publication "Dieback of Calluna vulgaris in a coastal heathland after the 2018 extreme drought"

Original data and code for the publication "Dieback of Calluna vulgaris in a coastal heathland after the 2018 extreme drought"

opencc-by-4.0Sep 2024View details →

Raw data collection for the publication P. Pötschke, T. Villmow, B. Krause and B. Kretzschmar, Influence of Twin-screw Extrusion Conditions on MWCNT Length and Dispersion and Resulting Electrical and Mechanical Properties of Polycarbonate Composites

This data collection contains the raw data for the publication  Petra Pötschke, Tobias Villmow, Beate Krause and Bernd Kretzschmar, Influence of Twin-screw Extrusion Conditions on MWCNT Length and Dispersion and Resulting Electrical and Mechanical Properties of Polycarbonate Composites, polymers 2024, 16(19), 2694. <a href="https://doi.org/10.3390/polym16192694">https://doi.org/10.3390/polym16192694</a> The data are sorted according to the figures and tables in which they are used. The description in Table 1 is taken from the reference: Villmow, T.; Kretzschmar, B.; Pötschke, P. Influence of screw configuration, residence time,  and specific mechanical energy in twin-screw extrusion of polycaprolactone/multi-walled carbon nanotube composites. Compos. Sci. Technol. 2010, 70, 2045-2055. doi: https://doi.org/10.1016/j.compscitech.2010.07.021. Figure 15 is adapted from the references: Krause, B.; Boldt, R.; Pötschke, P. A method for determination of length distributions of multiwalled carbon nanotubes before and after melt processing. Carbon 2011, 49, 1243-1247, https://doi:10.1016/j.carbon.2010.11.042. and Liebscher, M.; Domurath, J.; Krause, B.; Saphiannikova, M.; Heinrich, G.; Pötschke, P. Electrical and melt rheological characterization of PC and co-continuous PC/SAN blends filled with CNTs: Relationship between melt-mixing parameters, filler dispersion, and filler aspect ratio. Journal of Polymer Science Part B: Polymer Physics 2018, 56, 79-88, https://doi:10.1002/polb.24515. The data are reused with permissions.  The raw data (TEM images) used for the calculation of the carbon nanotube length distributions and mean carbon nanotube length values shown in Figs. 4, 11, 13, 16, and 17 are publically available at: Krause, B. (2024). Transmission electron microscopy (TEM) images of multiwalled carbon nanotubes (MWCNT) detached from polycarbonate (PC) composites [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11400466 Version v2: Compared to the submitted figure, in the final version in Fig. 9 the sample using the side feeder (PC-H-05) was removed and two samples extruded at 15 kg/h and 750 rpm (PC-H-25) and 1000 rpm (PC-H-26) were added. In the text-file the unit of GPa for the elastic modulus was corrected to MPa.  Compared to the submitted Table, in the final version of Table 2 the electrical resistivity values were given in more detail including the standard deviation. The column title of sigma break was changed to sigma max, which is more correct for these stress-strain diagrams. Compared to the submitted Table, in the final version of Table 3 the column title of sigma break was changed to sigma max, which is more correct for these stress-strain diagrams.The title of the first column was set to "screw" instead of "feeding".

opencc-by-4.0Oct 2021View details →

Metadata matrix: publication type (papers) by authors, from webQDA.

Part of the thesis of Sonia Verdugo Castro (USAL, Spain).  Title of the article: "The gender gap in STEM higher education studies: visualisation of literature". Authors of the publication: Sonia Verdugo-Castro, Mª Cruz Sánchez-Gómez, Alicia García-Holgado, Francisco J. García-Peñalvo

opencc-by-4.0Oct 2021View details →

Metadata matrix: publication type by authors, from webQDA.

opencc-by-4.0Jul 2021View details →

Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution"

These datasets have been used to produce Figure 3, 4 and 5 of manuscript: "Coherent phase transfer for real-world twin-field quantum key distribution". The files contain two arrays: time in seconds and normalised intensity. Explanation for "Data_fig3_XXX.txt": the files contains the raw data used to produce Fig. 3. The following timespans have been used: Data_fig3_stabilised.txt: t_start= 0.29 s, t_stop=0.292 s Data_fig3_unstabilised.txt: t_start=0.00225 s, t_stop=0.00424 s Explanation for "Data_fig4_XXX.txt" files: the procedure to obtain the phase deviation from the normalised interference is detailed in the text (Methods section). Datasets with different sampling rate have been combined to obtain the phase deviation on the long and short term. Explanation for "Data_fig5.txt": the files contains the raw data used to produce Fig. 5.

Data and scripts for Journal of Geophysical Research – Earth Surface publication: Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina

This data source contains scripts and data associated with the JGR Earth Surface publication “Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina” by A. Mueting, B. Bookhagen, and M. R. Strecker. The Digital Elevation Model (DEM) of the lower part of the Quebrada del Toro and Río Capilla catchment in the NW Argentinian Andes was generated from SPOT-7 tri-stereo images using Ames Stereo Pipeline. The final dataset has a spatial resolution of 3 m. A full description of the DEM generation process and accuracy assessment can be found in the associated paper. The scripts are also available at https://github.com/UP-RS-ESP/DEM_ConnectedComponents.

opencc-by-4.0Jul 2021View details →

Datasets corresponding to publication: Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes

This repository contains all dataset that correspond to the publication "Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes". Furthermore, Python scripts are provided which allow to reproduce all analyses shown in the manuscript. Execution of the scripts requires a Python environment with packages as stated in the Methods section of the manuscript, or by using PyBox 0.1.0. PyBox is a readily installed Python environment containing all packages at the required version. PyBox is publicly available on GitHub: <a href="https://github.com/maikherbig/PyBox">https://github.com/maikherbig/PyBox</a>.

A Repeated Measures Dataset on Public Responses to the COVID-19 Pandemic: Social Norms, Attitudes, Behaviors, Conspiracy Thinking, and (Mis)Information

We present a quantitative repeated measures dataset from intensive longitudinal research (diary survey) conducted with participants drawn from a nationally representative sample of the German population. This diary survey addresses a wide range of variables relevant to the COVID-19 pandemic, including information seeking, attitudes towards science, public health, and related behaviors.  Respondents were asked about their thoughts, challenges, concerns, and experiences related to the pandemic. Survey responses were requested every two weeks from November 2020 and September 2021 for a total of 18 stages of data collection. The diary survey responses were all matched to the same individual. Using the unique identifier for each individual, the diary survey dataset can be analyzed in concert with a larger representative survey sample that was conducted three times within the same research project over this time period. The diary dataset we present here includes socio-demographic information and other potentially relevant variables, ready for secondary analysis. Initial sample frame: N = 1,480 Initial diary survey sub-sample: N = 133

Source Data for the publication: "A different perspective for nonphotochemical quenching in plant antenna complexes"

This dataset contains the raw data for all the results shown in the paper either in Excel or in csv format. The Excel file contains one Excel sheet for each figure panel: <ul> <li>Figure 2a</li> <li>Figure 2b</li> <li>Figure 3a</li> <li>Figure 3d</li> <li>Figure 4a</li> <li>Figure 4b</li> <li>Figure 4c</li> <li>Figure 5b</li> <li>Supplementary Figure 2</li> <li>Supplementary Figure 3</li> <li>Supplementary Figure 4</li> <li>Supplementary Table 1</li> <li>Supplementary Figure 5</li> <li>Supplementary Figure 6</li> <li>Supplementary Figure 7</li> <li>Supplementary Figure 11e</li> </ul> The zipped csv files contain the remaining figures (one per file) and are named accordingly

Data and code for the publication "Microplastics in water and sediments at the confluence of the Elbe and Mulde rivers in Germany"

Background The data set contains data on visual and chemical analysis of sediment and water samples taken at the confluence of the Elbe and the Mulde rivers in Germany. It was analysed in the paper by H. Laermanns, G. Reifferscheid, J. Kruse, C. Földi, G. Dierkes, D. Schaefer, C. Scherer, C. Bogner and F. Stock, 2021, “Microplastics in water and sediments at the confluence of the Elbe and Mulde rivers in Germany” (<a href="https://doi.org/10.3389/fenvs.2021.794895">https://doi.org/10.3389/fenvs.2021.794895</a>) published in Frontiers in Environmental Science, Research Topic: “Plastics in Aquatic Systems: from Transport and Fate to Impacts and Management Perspectives”.   Description of the dataset The data is available in the folder data_and_results.zip, and there in the folder data. The folder pyrolysis contains the file <ul> <li>pyr-gc-ms_raw.csv. This is the pyrolysis analysis (see description in the paper for technical details). The calibration data are part of the supplemental material of the paper and can be downloaded there.</li> </ul> The folder visual_analysis contains the files <ul> <li>elbe_filter_photos_complete_v2.csv: IDs of samples, number of the photographs of filters, shapes of particles located on the filter</li> <li>elbe_samples_v2.csv: IDs of samples, medium (water or sediment sample), location and side (left or right riverside) of sampling, sampling tool, sampled volume and mesh size.</li> </ul> The folder results contains the figures produced by the R code (see Description of the code).   Description of the code The file MP_visual_and_chemical_analysis.Rmd is the R notebook we wrote for the analysis. For information on the software version, see Information on software version below.   Disclaimer The data and code are provided as is without any warranty.   Acknowledgements We thank the staff of the BfG laboratory for their support during the pyr-GC-MS analysis. Julia Horn helped with geochemical and sedimentological measurements in the laboratory of the Institute of Geography at the University of Cologne.   Funding This study is embedded in the Project  “Micro- and macroplastic in federal water ways of Germany” (Project 4.1.12, Project No. M39620304028) that intends to analyse the occurrence, transport mechanism, ecological risks and management options. This project was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project Number 391977956, SFB 1357.   References Hannes Laermanns, Georg Reifferscheid, Jonas Kruse, Corinna Földi, Georg Dierkes, Dirk Schaefer, Christian Scherer, Christina Bogner and Friederike Stock, 2021, Microplastics in water and sediments at the confluence of Elbe and Mulde rivers in Germany, <a href="https://doi.org/10.3389/fenvs.2021.794895">https://doi.org/10.3389/fenvs.2021.794895</a> R Core Team, 2021, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, https://www.R-project.org/   Information on software version sessionInfo() R version 4.1.0 (2021-05-18) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 20.10 Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3 LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3 locale:  [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C                [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8      [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8     [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                   [9] LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C        attached base packages: [1] stats     graphics  grDevices utils     datasets [6] methods   base      other attached packages:  [1] lubridate_1.8.0    patchwork_1.1.1    ggforce_0.3.3       [4] gridExtra_2.3      RColorBrewer_1.1-2 forcats_0.5.1       [7] stringr_1.4.0      dplyr_1.0.7        purrr_0.3.4        [10] readr_2.0.2        tidyr_1.1.4        tibble_3.1.6       [13] ggplot2_3.3.5      tidyverse_1.3.1    loaded via a namespace (and not attached):  [1] httr_1.4.2        sass_0.4.0        bit64_4.0.5        [4] vroom_1.5.5       jsonlite_1.7.2    modelr_0.1.8       [7] bslib_0.3.1       assertthat_0.2.1  highr_0.9         [10] cellranger_1.1.0  yaml_2.2.1        sessioninfo_1.1.1 [13] pillar_1.6.4      backports_1.2.1   glue_1.5.0        [16] digest_0.6.28     polyclip_1.10-0   rvest_1.0.0       [19] colorspace_2.0-2  htmltools_0.5.2   pkgconfig_2.0.3   [22] broom_0.7.6       haven_2.4.1       scales_1.1.1      [25] tweenr_1.0.2      tzdb_0.2.0        generics_0.1.1    [28] farver_2.1.0      ellipsis_0.3.2    withr_2.4.2       [31] cli_3.1.0         magrittr_2.0.1    crayon_1.4.2      [34] readxl_1.3.1      evaluate_0.14     fs_1.5.0          [37] fansi_0.5.0       MASS_7.3-54       xml2_1.3.2        [40] tools_4.1.0       hms_1.1.1         lifecycle_1.0.1   [43] munsell_0.5.0     reprex_2.0.0      compiler_4.1.0    [46] jquerylib_0.1.4   rlang_0.99.0.9000 grid_4.1.0        [49] rstudioapi_0.13   labeling_0.4.2    rmarkdown_2.11    [52] gtable_0.3.0      DBI_1.1.1         R6_2.5.1          [55] knitr_1.36        fastmap_1.1.0     bit_4.0.4         [58] utf8_1.2.2        rprojroot_2.0.2   desc_1.3.0        [61] stringi_1.7.5     parallel_4.1.0    Rcpp_1.0.7        [64] vctrs_0.3.8       dbplyr_2.1.1      tidyselect_1.1.1 [67] xfun_0.28

opencc-by-sa-4.0Jul 2021View details →

Wilcoxon Rank Sum Test and Keyphrase Extraction Data Cited in "What Everyone Says: Public Perceptions of the Humanities in the Media"

This repository contains Wilcoxon rank sum test and keyphrase extraction data cited in the WhatEvery1Says (WE1S) Project's article  "What Everyone Says: Public Perceptions of the Humanities in the Media". The organization of the materials is discussed below. Wilcoxon Rank Sum Test All data and results from Wilcoxon rank sum testing can be found in the <code>wilcoxon-tests</code> folder of the extracted zip file <code>we1s_about_the_humanities.zip</code>. The Wilcoxon rank sum test identifies specific words that appear significantly more in one group of documents as compared to another, thus providing researchers with an understanding of what words are “distinctive” to each group. Further information on WE1S's use of Wilcoxon rank sum testing can be found at <a href="https://we1s.ucsb.edu/wp-content/uploads/M-15-Wilcoxon-Test.pdf">https://we1s.ucsb.edu/wp-content/uploads/M-15-Wilcoxon-Test.pdf</a>. Each subdirectory in the <code>wilcoxon-test</code> folder contains the data and results of a particular comparison experiment based on a metadata category such as whether the data contained articles published by public or private institutions. Each data file is a <code>.txt</code> file representing a sample of the overall data from the collection. The <code>README</code> file provides information on the collection used, the sample size, and the nature of the comparison. The results for the test are in a file called <code>results.csv</code>. The <code>results.csv</code> file for each test includes a row for each term included in the test. Each row displays the term, the term's raw count in each category compared (count 1 and count 2), the difference between the 2 counts (count 1 minus count 2), the percentage change in the counts, the Wilcoxon statistic, and the Wilcoxon p-value. Sorting the csv by the Wilcoxon stat from greatest to least will cause the terms most strongly associated with category 1 to come to the top (category 1 is the category listed first in the title field of the README.md file for each test), while sorting it by the Wilcoxon stat from least to greatest will cause the terms most strongly associated with category 2 to come to the top (category 2 is the category listed second). The p-value column provides you with information about how confident you can be about each comparison's significance. Keyphrase Extraction All data and results from Wilcoxon rank sum testing can be found in the <code>keyphrase-extraction</code> folder of the extracted zip file <code>we1s_about_the_humanities.zip</code>. Keyphrase extraction generates a list of the most significant words or phrases (1-6 words long) within individual documents. WE1S takes the top ten keyphrases in each document and ranks them according to their frequency across the collection. WE1S uses the SGRank algorithm for keyphrase extraction, and because this algorithm is computationally intensive, WE1S limits keyphrases to lemmatized nouns and proper nouns within a window of 70 words to either side of candidate keyphrases. Further information on WE1S's use of keyphrase extaction can be found at <a href="https://we1s.ucsb.edu/wp-content/uploads/M-14-Keyphrase-Extraction.pdf">https://we1s.ucsb.edu/wp-content/uploads/M-14-Keyphrase-Extraction.pdf</a>. Each subdirectory in the <code>keyphrase-extraction</code> folder contains the data and results of keyphrase extraction on a particular collection. Details of the collection and resulting files can be found in each subdirectory. Each list of keyphrases is in a file called <code>SGRank.csv</code>, which lists the keyphrases and their number of occurrences in the collection. The article additionally cites keyphrases that are shared with the terms in the public topic model produced by Andrew Goldstone and Ted Underwood, “The Quiet Transformations of Literary Studies: What Thirteen Thousand Scholars Could Tell Us,” New Literary History 45, no. 3 (2014): 359–84, <a href="https://doi.org/10.1353/nlh.2014.0025">https://doi.org/10.1353/nlh.2014.0025</a>. The list of terms is derived from the public visualization at <a href="https://www.sas.rutgers.edu/virtual/ag978/quiet/#/words">https://www.sas.rutgers.edu/virtual/ag978/quiet/#/words</a>. Keyphrases extracted from WE1S data were split into single-word terms and compared with the list of vocabulary in Goldstone and Underwood's word list (<code>quiet_transformations_wordlist.txt</code>) to compile lists of shared vocabulary. These lists are given in files called <code>shared_terms.txt</code>. Note that keyphrases were extracted for corpora produced using the Python <a href="https://textacy.readthedocs.io/en/latest/index.html">Textacy</a> library. Because these corpora contain the full text of articles with intellectual property restrictions they cannot be reproduced here.

Data and code for the publication "Tracking microplastics across the streambed interface: Using laser-induced fluorescence to quantitatively analyze microplastic transport in an experimental flume"

This archive contains datasets and codes that were used in the publication "Tracking microplastics across the streambed interface: Using laser-induced fluorescence to quantitatively analyse microplastic transport in an experimental flume".

Data used in the publication: Sensitivity of modeled microphysics to stochastically perturbed parameters

These data support the results presented in the manuscript titled "Sensitivity of modeled microphysics to stochastically perturbed parameters". They consist of results from an idealized single vertical column atmospheric model run for a number of experiments that explore methods of representing model uncertainty.

Supplementary materials for "Integration of public DNA methylation and expression networks via eQTMs improves prediction of functional gene–gene associations"

This repository contained supplementary materials in the study named: "Integration of public DNA methylation and expression networks via eQTMs improves prediction of functional gene–gene associations." For extracting all files from the downloaded tar.gz file, the following commanda could be used: <pre><code>tar -xf supplementary_materials.tar.gz </code></pre> The supplementary_materials/data diretcory contains the following sections: <ol> <li>eqtm_predictions: This contains the training and testing datasets for the eQTM prediction procedures</li> <li>public_methylation_data_and_pca: This contains the harmonized public DNA methylation dataset and its first 100 PCA components</li> <li>cca_data: This contains the CCA components for the public DNA methylation and gene expression datasets for the negative eQTMs, and the input datasets for the functional gene pair prediction analylsis.</li> <li>gene_enrichment_results_for_cca: This contains the gene enrichment results for the CCA components for negative and positive eQTMs</li> </ol> The supplementary_materials/model directory contains the following sections: <ol> <li>disease_tissue_predictions: This contains the models trained for tissue prediction and disease prediction based on the PCA components from the public DNA methylation data</li> <li>eqtm_predictions: This contains models trained for eQTM prediction</li> <li>cca_transformations: This contains CCA transformation models and models for STRING gene pair predictions  </li> </ol>

Data Set of Publication - Outcome of Scleral Rupture Primary Management Without Vitrectomy Jakarta Eye Trauma Study

This dataset was collected for research purposes. The dataset was about the scleral rupture we found in our teritories

A critical review of 'just transition' publications using Google and Google Scholar

This dataset offers the list of sources included in our critical review of the term 'just transition' (in English), from 1990 to 2021 in the Global North and South Africa, using Google and Google Scholar as search engines. The publications retrieved include both peer-reviewed literature and publicly available reports and documents. Results (with full citations) are categorized by actor group and type, location, and year of publication.

Lock-in and Standard DPC raw images associated to publication 10.1364/PRJ.445896

Raw images obtained with helicam C3 in a DPC microscope setup. The raw images were used for the figures in the publication Chiara Bonati, Damien Loterie, Timothé Laforest, Christophe Moser. Lock-in incoherent differential phase contrast imaging. Photonics Research, 2022, 10(1):237. Contents of the data set: <ol> <li>Contrast comparison: images used for Figure 4. Several USAF targets of different height, introducing varying phase differences. Samples were imaged under both standard and lock-in DPC. <ol> <li>Lock-in DPC: lock-in DPC image sets. Each image was collected over four cycles of 140 us each. <ol> <li>5_mrad: 5 mrad sample <ul> <li>Background: no sample image <ul> <li>I: Frame I</li> <li>Q: Frame Q</li> </ul> </li> <li>Sample: sample image <ul> <li>I: Frame I</li> <li>Q: Frame Q</li> </ul> </li> </ul> </li> <li>13dot8_mrad: 13.8 mrad sample</li> <li>15dot1_mrad: 15.1 mrad sample</li> <li>18dot9_mrad: 18.9 mrad sample</li> <li>35dot2_mrad: 35.2 mrad sample</li> <li>50dot5_mrad: 50.5 mrad sample</li> </ol> </li> <li>Standard DPC: Same set of images, under standard DPC conditions. Each illumination (left and right) was recorded for 1 us. <ol> <li>5_mrad: 5 mrad sample <ul> <li>Background: no sample image <ul> <li>L: Image under left illumination</li> <li>R: Image under right illumination</li> </ul> </li> <li>Sample: sample image <ul> <li>L: Image under left illumination</li> <li>R: Image under right illumination</li> </ul> </li> </ul> </li> <li>13dot8_mrad: 13.8 mrad sample</li> <li>15dot1_mrad: 15.1 mrad sample</li> <li>18dot9_mrad: 18.9 mrad sample</li> <li>35dot2_mrad: 35.2 mrad sample</li> <li>50dot5_mrad: 50.5 mrad sample</li> </ol> </li> </ol> </li> <li>Reconstruction: Image sets used for figures 5 and 6 <ol> <li>USAF_50dot5mrad: Image sets of a 50.5 mrad glass USAF target. <ol> <li>Illumination: Folder containing the back-focal plane characterisation of the imaging system, used to compute the transfer functions for phase reconstruction. <ul> <li>BFP: the back-focal plane aperture of the 0.4 NA objective</li> <li>P_1: reduced pupil used in the experiments</li> <li>s_L: left source</li> <li>s_R: right source</li> </ul> </li> <li>Lock-in DPC: lock-in DPC image set. The equivalent exposure time was 464 us.</li> <li>Standard DPC: standard DPC image set. Each illumination was exposed for 8 us.</li> </ol> </li> <li>Onion: Image sets of onion epidermal cells. <ol> <li>Lock-in DPC: lock-in DPC images for varying equivalent exposure times. <ul> <li>104 us</li> <li>120 us</li> <li>136 us</li> <li>152 us</li> <li>168 us</li> <li>184 us</li> <li>200 us</li> <li>216 us</li> <li>232 us</li> <li>248 us</li> </ul> </li> <li>Standard DPC: Standard DPC image set at 7 us exposure time</li> </ol> </li> </ol> </li> </ol>

opencc-zeroDec 2021View details →

dryad36/100

Data to support publication figures and animation scripts at GitHub: Modeling weather-driven long-distance dispersal of spruce budworm moths (Choristoneura fumiferana)

Long-term studies of insect populations in the North American boreal forest have shown the vital importance of long-distance dispersal to the maintenance and expansion of insect outbreaks. In this work, we extend several concepts established previously in an empirically-based dispersal flight model with recent work on the physiology and behavior of the adult eastern spruce budworm (SBW) moth, Choristoneura fumiferana (Clem.). An outbreak of defoliating SBW in Quebec, ongoing since the mid-2000s, already covers millions of hectares of forests in eastern Canada and threatens to spread into neighboring areas through annual summertime episodes of long-distance dispersal. Such flight events in favorable conditions frequently include billions of SBW moths dispersing in the warm atmospheric boundary layer, typically starting around sunset and often lasting through several hours of wind-driven transport over hundreds of kilometers. Successful SBW dispersal to possibly distant host forest areas depends acutely on the weather. Here we describe the components and results of SBW–pyATM, an open-source individual-based modeling framework developed in Python for the simulation of these weather-driven SBW dispersal events. Using seasonal SBW phenology results from BioSIM at known outbreak locations and high-resolution Weather Research and Forecasting (WRF) model output, we focus on modeling dispersal flights over two successive nights in July 2013 in southern Quebec. Our flight model closely reproduces the SBW spatial patterns and motions observed by weather surveillance radar over the St. Lawrence estuary. With SBW–pyATM we can estimate landing locations for both male and female SBW and the resulting spatial patterns of egg distribution, allowing us eventually to forecast future larval defoliation activity in new locations where immigration could help overcome local limitations on SBW populations. This information could then support forest management decisions where SBW outbreaks threaten valuable resources.