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2,878 results for “code and data”
Size scalability of Monte Carlo simulations applied to oxidized polypyrrole systems: Data and Codes
<p>This work generalizes our recently proposed coarse grained force field (CGFF) for halogen oxidized PPy in the condensed phases and introduces a novel implementation of the Nettropolis Monte Carlo (MMC) simulation based on the CGFF that enables simulations of polymer systems with more than<br>100000 particles. The MMC implementation utilizes a combination of CPU and GPUs and exploits a numerical approximation based on polynomial piecewise interpolation for the calculation of the CGFF pairwise additive terms. Our simulations evidence that the oxidized PPy thermodynamic and structural properties are consistent as the system size is scaled up. Predicted properties include density, enthalpy, potential energy, heat capacity, coefficient of thermal expansion, caloric curve, glass transition temperature range, compressibility, bulk modulus, radial distribution functions, and polymer chain characteristics.</p>
Data and Code in support of Caterpillar abundance in a northern hardwood forest: exogenous effects, endogenous feedbacks, and multidecadal trends.
In this study, we analyzed caterpillar abundance and biomass measured over 50 years (1970 - 2021) in the Hubbard Brook Experimental Forest, New Hampshire, USA. We tested mechanisms for determination of caterpillar abundance that included weather, host plant quality, and predator abundance. This dataset includes data, R code, and spatial files supporting this study. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Data and Code Accompanying "Retrieving and Analyzing Taste Colexifications from Lexibank"
<p>Data and Code accompanying the study "Retrieving and analyzing taste colexifications from Lexibank" by Olena Shcherbakova and Johann-Mattis List (see <a href="https://calc.hypotheses.org/6398">https://calc.hypotheses.org/6398</a>).</p><p>Information on how to run the code can be found in the study itself.</p>
Raw data mzXML and MATLAB code for Variation in chemical composition of dissolved organic matter during the winter to spring transition in the northern Barents Sea
<p>MATLAB code and raw data mzXML for Variation in chemical composition of dissolved organic matter during the winter to spring transition in the northern Barents Sea.</p> <p>Seawater samples were collected during three distinct periods: early winter (December 2019), late winter (March 2021), and spring (May 2021). The sampling transect extended from the northern Barents Sea into the Nansen Basin (76°N – 83°N) as part of <em>The Nansen Legacy</em> project (Research Council of Norway, RCN #276730). The molecular composition of dissolved organic matter (DOM) was analyzed using an Orbitrap mass spectrometer.</p>
Data and code of the article: "Early Warning Signals of the Termination of the African Humid Period(s)"
<p>Data and MATLAB Code of the article Trauth, M.H., Asrat, A., Fischer, M.L., Hopcroft, P.O., Foerster, V., Kaboth-Bahr, S., Kindermann, K., Lamb, H.F., Marwan, N., Maslin, M.A., Schaebitz, F., Valdes, P.J. (2024) Early Warning Signals of the Termination of the African Humid Period(s), Nature Communications, https://doi.org/10.1038/s41467-024-47921-1. The individual directories contain the data and the MATLAB code used to generate Fig. 1 and 2 and Supplementary Fig. 1 to 7 published with the article.</p>
Data and code for: Rachel A Reeb, J Mason Heberling, & Sara E Kuebbing (2024). Cross-continental comparison of plant reproductive phenology shows high intraspecific variation in temperature sensitivity. AoB PLANTS, plae058
<p>Data and Analysis Code for: </p> <p>Rachel A Reeb, J Mason Heberling, Sara E Kuebbing (2024). Cross-continental comparison of plant reproductive phenology shows high intraspecific variation in temperature sensitivity. <em>AoB PLANTS</em>, plae058. <a href="https://doi.org/10.1093/aobpla/plae058">https://doi.org/10.1093/aobpla/plae058</a></p> <p>Includes two R markdown files ("climate_data_extraction_code.rmd" is the script for data extraction and cleaning and "Data_Analysis_V2.rmd" is the analysis script), the associated datasets (in .csv format), and the metadata file ("readme.txt").</p>
Code and data set for data analysis published as manuscript "Bacttle: a microbiology educational board game for lay public and schools"
<p>Code that processed raw data and plots the figures of the manuscript "Bacttle: a microbiology educational board game for lay public and schools"</p> <p>Below is a table with the original survey questions. The ID corresponds to the column displayed on the data set. When letters are followed by a number (1 or 2), it means that the question was answered before playing the game (1) and after playing the game (2).</p> <table> <tbody> <tr> <td> <p><em>ID<sup>1</sup></em></p> </td> <td> <p><em>Question text</em></p> </td> <td> <p><em>Possible answers<sup>2</sup></em></p> </td> </tr> <tr> <td> <p><em>A</em></p> </td> <td> <p>How old are you?</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>B</em></p> </td> <td> <p>Do you know what a bacterium is?</p> </td> <td> <p>y/n</p> </td> </tr> <tr> <td> <p><em>C</em></p> </td> <td> <p>Do you know what a bacterial capsule is?</p> </td> <td> <p>y/n</p> </td> </tr> <tr> <td> <p><em>D</em></p> </td> <td> <p>Do bacteria have tools to harm each other?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>E</em></p> </td> <td> <p>Do bacteria reproduce at the same pace?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>F</em></p> </td> <td> <p>What is sporulation?</p> </td> <td> <p>A resistant state that some bacteria can achieve under unfavorable conditions.</p> </td> </tr> <tr> <td> <p>The release of toxins by bacteria.</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>G</em></p> </td> <td> <p>What are flagella used for?</p> </td> <td> <p>Sticking to surfaces.</p> </td> </tr> <tr> <td> <p>Motility in liquid environments.</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>H</em></p> </td> <td> <p>What does it mean to be lithotrophic?</p> </td> <td> <p>A bacterium can get energy from minerals.</p> </td> </tr> <tr> <td> <p>A bacterium can get energy from the sunlight.</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>I</em></p> </td> <td> <p>Can bacteria be infected by viruses?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>J</em></p> </td> <td> <p>Are all bacteria harmful for humans?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>K</em></p> </td> <td> <p>How many bacteria are in a coffee spoon of yoghurt?</p> </td> <td> <p>Millions</p> </td> </tr> <tr> <td> <p>Hundreds</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>L</em></p> </td> <td> <p>How easy did you find the gameplay?</p> </td> <td> <p>VE/E/A/D/VD</p> </td> </tr> <tr> <td> <p><em>M</em></p> </td> <td> <p>Did you find the card content easy to understand?</p> </td> <td> <p>VE/E/A/D/VD</p> </td> </tr> <tr> <td> <p><em>N</em></p> </td> <td> <p>Did you like the setup of the game?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>O</em></p> </td> <td> <p>Would you like to play this game again?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>P</em></p> </td> <td> <p>What can we improve?</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p>1) Question A categorizes the player’s age; B and C assess the initial level of knowledge in microbiology (none -both questions are answered negatively-, basic -player knows what a bacterium is but not a bacterial capsule-, or advanced -both answers are positive-); questions D-I score knowledge acquisition; J and K are control questions; L-O evaluate the appreciation of the game; and P is an optional free text-entry answer for additional feedback. <br>2) y= yes, n=no, idk=I don’t know, VE=very easy, E=easy, A=adequate, D=difficult, VD=very difficult.</p>
Incriminations in the inquisition register of Bologna (1291-1310): network data and code
<p>Cross-sectional (synchronic) projection of network data on incriminations (nominations of people in the criminal context of heresy trials) in the medieval inquisition register of Bologna, 1291–1310 in TSV format (tabulator-separated values), and R code for the article: Zbíral, David, Katia Riccardo, Tomáš Hampejs, and Zoltán Brys. ‘Gender, Kinship, and Other Social Predictors of Incrimination in the Inquisition Register of Bologna (1291–1310): Results from an Exponential Random Graph Model’. PLOS One 20, no. 2 (11 February 2025): e0315467. https://doi.org/10.1371/journal.pone.0315467.</p> <p>The data and analysis are described in the related article.</p>
Data and code for: Little directional change in the timing of Arctic spring phenology over the past twenty-five years
<p>Data and code accompanying the publication: Little directional change in the timing of Arctic spring phenology over the past twenty-five years.</p> <p>This resource contains 1. R-scripts to calculate yearly phenologies from raw temporally explicit flowerin, arthropod observation and bird nesting data from Zackenberg. The raw data is openly accessible through the Greenland Ecosystem Monitoring database (https://data.g-e-m.dk/), as well as an R-script to carry out most of the analyses presented in the publication. To facilitate the use of the analysis script, pre-produced annual phenologies of focal taxa are included as csv-tables.</p>
Photosynthetic quotients in aquatic ecosystems: data and code supporting Trentman et al. 2023 manuscript in L&O Letters
This study provides a summary of the mismatch between our current knowledge and the application of the photosynthetic quotient (PQ). We use data from the Upper Clark Fork River (UCFR) as a case study example of how the PQ may vary in space and time based on environmental conditions. Surface water sample measurements of dissolved oxygen (DO), temperature (T), nutrients (NO3-N, NH4-N, SRP), and several metabolism indicators are represented in this data product. Figures represent data from two sites on the mainstem of the Upper Clark Fork River (UCFR) over a roughly two-year period, from 2019 to 2021. Some measurements are derived from existing data products or manuscripts, including DOT (Valett, et al., 2023); nutrients (H. M. Valett, Dec. 2, 2022, pers. comm); air pressure (Deer Lodge Weather Station, 2023); underlying data for Trentman et al. (2023) Figure 2 and Figure 4e and 4f (via Burris, 1981); and SI-Figure2 USGS gage data (USGS, 2023). Products unique to this data product include metabolism data (Trentman, et al., 2023 (Figure 5)), chamber data supporting Trentman, et al., (2023) Figure 6, and code simulations/data. All analytes and variables are documented in the project data dictionary. For details on data collection methods, see the methods section, the manuscript, and/or referenced data products.
NEON soil inorganic nitrogen measurements 2017-2020, derived data and code for Earth's Future manuscript
Nitrogen (N) is a key limiting nutrient in terrestrial ecosystems, but there remain critical gaps in our ability to predict and model controls on soil N cycling. This may be in part due to lack of standardized sampling across broad spatial-temporal scales. In a paper submitted for publication in Earth's future, we introduce a continentally distributed, publicly available dataset collected by the National Ecological Observatory Network (NEON) that can help fill these gaps. To overcome methodological challenges and generate a standardized dataset, we produced a derived data version of soil inorganic N pools and net N transformation rate tables, which accounts for nitrite contamination in blanks. This derived dataset is then used to evaluate sources of variation within the NEON sampling design with mixed effects models, and we also compare measured net N mineralization to simulated fluxes from the Community Earth System Model 2 (CESM2).
Data and code from Artificial light at night increases top-down pressure on caterpillars: experimental evidence from a light-naive forest - 2021-2022
This dataset has been prepared in support of a paper to be published in Proceedings of the Royal Society B: Biological Sciences. It includes both data files and R scripts used for the analysis in this publication: Deitch, J.F. and S.A. Kaiser. 2023. Artificial light at night increases top-down pressure on caterpillars: experimental evidence from a light-naive forest. Proceedings of the Royal Society B: Biological Sciences. (https://doi.org/10.1098/rspb.2023.0153) Artificial light at night (ALAN) is a globally widespread and expanding form of anthropogenic change that impacts arthropod biodiversity. ALAN alters interspecific interactions between arthropods, including predation and parasitism. Despite their ecological importance as prey and hosts, the impact of ALAN on larval arthropod stages, such as caterpillars, is poorly understood. We examined the hypothesis that ALAN increases top-down pressure on caterpillars from arthropod predators and parasitoids. We experimentally illuminated study plots with moderate levels (10-15 lux) of LED lighting at light-naive Hubbard Brook Experimental Forest, New Hampshire. We measured and compared between experimental and control plots: 1) predation on clay caterpillars and 2) abundance of arthropod predators and parasitoids. We found that predation rates on clay caterpillars and abundance of arthropod predators and parasitoids were significantly higher on ALAN treatment plots relative to control plots. These results suggest that moderate levels of ALAN increases top-down pressure on caterpillars. We did not test mechanisms, but sampling data indicates that increased abundance of predators near lights may play a role. This study highlights the importance of examining the effects of ALAN on both adult and larval life stages and suggests potential consequences of ALAN on arthropod populations and communities. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubba
Data set and code supporting Marshall et al. 2020. No room to roam: King Cobras reduce movement in agriculture.
<p>Data and code used in the publication:</p> <p>Marshall, B.M., Crane, M., Silva, I., Strine, C.T., Jones, M.D., Hodges, C.W., Suwanwaree, P., Artchawakom, T., Waengsothorn, S., Goode, M. (2020). No room to roam: King Cobras reduce movement in agriculture. <em>Mov Ecol</em> <strong>8, </strong>33 (2020). https://doi.org/10.1186/s40462-020-00219-5</p> <p>Marshall, B.M., Crane, M., Silva, I., Strine, C.T., Jones, M.D., Hodges, C.W., Suwanwaree, P., Artchawakom, T., Waengsothorn, S., Goode, M. (2020). No room to roam: King Cobras reduce movement in agriculture. bioRxiv 2020.03.24.006676; doi: https://doi.org/10.1101/2020.03.24.006676</p> <p>Including: telemetry data, habitat shapefile and derived rasters, ISSF and JAGS model specification and results, code to reproduce analysis and generate figures. </p>
Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (PNAS, 2020), "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates"
<p>This upload contains all replication material for "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates" (PNAS, 2020). Please note that previous versions of this upload provided data and code for the pre-print version of the article, which changed somewhat through the peer review process. </p> <p><strong>Authors:</strong> Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor.</p> <p><strong>Code is located within CCHMP_covid_climate_code_release.zip</strong>, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script (all other filepaths are relative).</p> <p>Please find the code needed to replicate the main findings of the paper described below:</p> <ul> <li>Plots of data: R and Stata scripts to make figures 1B, 2A/B/C, S1, S2, and S3, can be found within “code/analysis/data_plots/”.</li> <li>Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, 3C, S5, S6, S7, S8, S10, and S14, as well as Table S1, can be found within “code/analysis/regressions/”. R scripts for data analysis and plotting for figures 3A/B and S9 are also within "code/analysis/regressions/".</li> <li>Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 4, S4 and S11 can be found within “code/analysis/seasonal_sim/”.</li> <li>SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S12 and S13 can be found within “code/analysis/SEIR/”.</li> </ul> <p><strong>Data are located within CCHMP_covid_climate_data_release.zip.</strong></p>
Evolution of software code at the level of fine-grained elements: data files
<p>The data files available here (68GB uncompressed) have been used for studying the evolution of code at the level of fine-grained elements. The data are associated with the processing of the 89 open source software repositories hosted on GitHub. Details regarding each individual GitHub project are stored in the repos folder under directories matching the owner and project name used on GitHub. For example, the files under repos/KDE/kdevelop correspond to the project hosted on https://github.com/KDE/kdevelop. Data associated with the statistical analysis of the processed repositories are stored in the statistical-analysis folder. The file project_details.txt contains the data used for selecting the processed projects.</p>
Data set and code supporting Marshall et al., "An inventory of online reptile images"
<p>Data set and code supporting: MARSHALL, B.M., FREED, P., VITT, L.J., BERNARDO, P., VOGEL, G., LOTZKAT, S., FRANZEN, M., HALLERMANN, J., SAGE, R.D., BUSH, B. and DUARTE, M.R., 2020. An inventory of online reptile images. <em>Zootaxa</em>, <em>4896</em>(2), pp.251-264. DOI:<a href="https://doi.org/10.11646/zootaxa.4896.2.6">10.11646/zootaxa.4896.2.6</a></p> <p>Data includes: </p> <ul> <li>Supplementary Table 1. List of all species and the number of photos in each of the 6 repositories: "SuppData1_Species_Photo_Count_Table_2020-08-04_no_syn.csv"</li> <li>Supplementary Table 2. List of species without photo in any of the 6 repositories: "SuppData2_Species_no_photos.csv"</li> <li>Supplementary Table 3. Per country summary data of number of species present and number with images: "SuppData3_Country_species_counts.csv"</li> <li>Reptile Database species checklist: "reptile_checklist_2020_04.csv"</li> <li>Reptile Database species synonyms used in second Wikimedia search: "reptile names 2019 syno.csv"</li> </ul> <p>Code includes:</p> <ul> <li>R code used to retrieve Flickr photograph metadata: "SuppCode1_Flickr_search.R"</li> <li>R code used to retrieve Wikimedia photograph metadata: "SuppCode2_Wikimedia_query.R"</li> <li>R code used to retrieve HerpMapper photograph metadata: "SuppCode3_HerpMapper_search.R"</li> <li>R code used to generate figures: "SuppCode4_Figure Generation.R"</li> </ul> <p>Also includes Zootaxa supplementary table.</p> <p> </p>
Code, data and scripts to study wave dynamics in asymmetric material
<p>Supplementary data for [1] Vladislav A. Yastrebov. "Wave propagation through an elastically-asymmetric architected material", 2021, https://arxiv.org/abs/1712.06294v2</p> <p>See "Readme.md" and indivual "Readme.md" files in folders: "data", "src", "fig"</p>
Data and R code for Tansley review New Phytologist 2021: "An integrated framework of plant form and function: The belowground perspective"
<p>The files in this archive are related to the paper of Weigelt, Mommer, Andraczek et al. (2021) An integrated framework of plant form and function: The belowground perspective. Tansley Review New Phytologist. The paper developed and tested a new conceptual framework of plant form and function linking above and belowground traits of 2510 species. We found that an integrated, whole-plant trait space required as much as four axes. The two main axes represented the fast-slow ‘conservation’ gradient on which leaf and fine-root traits were well aligned, and the ‘collaboration’ gradient in roots. The two additional axes were separate, orthogonal plant size axes for height and rooting depth.</p> <p>This archives contains four files:</p> <ol> <li><strong>Weigelt et al.2021RCode.DataCleaning.txt</strong> - RCode for the complete data processing starting with the downloaded database files from the Plant Trait Database version 5.0 (TRY, Kattge et al. 2020), the Global Root Trait database (GRooT, Guerrero-Ramirez et al. 2020) and a small number of additional data files listed in Table S2 of the original paper. Additional information was later incorporated using FungalRoot Database (Soudzilovkaia et al. 2020), nodDB Database (Tedersoo et al. 2018) and a compiled dataset on rooting depth (Fan et al. 2017). The code processes, cleans and merges the data and produces a final table for PCA analysis of species specific mean traits. This final table is provided as a second file in this archive (Weigelt_et_al_2021_Main.PCA.Matrix.xlsx). A second part of the RCode.DataCleaning extracts species-specific individual trait data where root and shoot traits were measured on the same plant individual or plot. This data was compiled from 43 studies identified in Table S2 of the original publication. The final table for individual trait data is the third file in this archive (Weigelt_et_al_2021_Individual.PCA.Matrix.xlsx).</li> <li><strong>Weigelt_et_al_2021_Main.PCA.Matrix.xlsx</strong> – Datafile with species-specific global mean trait data for 17 traits of 2510 species with at least one root and one shoot trait available. Meta-data is provided in the data file.</li> <li><strong>Weigelt_et_al_2021_Individual.PCA.Matrix.xlsx</strong> – Datafile with species-specific trait data where root and shoot traits were measured on the same individual or plot for 6 traits of 455 species. Meta-data is provided in the data file.</li> <li><strong>Weigelt et al.2021RCode.Analysis.txt – </strong>RCode for all analyses and figures provided in the paper for both the species mean and individual based dataset. The Code is annotated to help reproducibility of the analysis.</li> </ol>
# Replication code and data for: Tracking green space along streets of world cities
<p># Replication code and data for: Tracking green space along streets of world cities<br>Falchetta, G., & Hammad, A. T. (2025). Tracking green space along streets of world cities. Environmental Research: Infrastructure and Sustainability. https://doi.org/10.1088/2634-4505/add9c4 </p> <p>The file "gvi_358cities_2016_2023_yearly_falchetta_hammad.csv" contains<strong> output data</strong>, reporting sampling-point level data on the yearly (2016-2023) values of the Green View Index for the 190 cities covered in the paper AND an additional number of world cities (for a total of 358 cities). The "README_gvi_358cities_2016_2023_yearly_falchetta_hammad.txt" file contains a dictionary of each column name and units. </p> <p>____<br><br></p> <p>To replicate the analysis, the results, and the figures of the paper:</p> <ul> <li>Download input data from this Zenodo repository and code from Github https://github.com/giacfalk/urban_green_space_mapping_and_tracking</li> <li><em>*Optional data extraction steps* </em>(processed output data are already available in the Zenodo repository):<br> <ul> <li>Adjust your working directory</li> <li>Run [lines 4-11] of workflow/sourcer.R</li> <li>Run the Javascript scripts written by the string_generator_training.R and string_generator_prediction.R files in Google Earth Engine (https://code.earthengine.google.com) and complete the export to Drive tasks to generate the output .csv files</li> </ul> </li> <li>Run workflow/sourcer.R [lines 15-46] to train the ML model and make predictions (including figures and tables replication)</li> </ul> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p dir="auto"> </p> <p dir="auto"> </p> </div> </div> </div> </div> </div> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p dir="auto"> </p> <p dir="auto"> </p> </div> </div> </div> </div> </div>
Strong sequence dependence in RNA/DNA hybrid strand displacement kinetics supplementary data and code
<p>Supplementary data and code needed to replicate figures and results for the paper: Strong sequence-dependence in RNA/DNA hybrid strand displacement kinetics - Francesca G. Smith, John P. Goertz, Molly M. Stevens and Thomas E. Ouldridge. README is included to explain each folder and file in the repository.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.