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466 results for “code analysis”
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>
Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text
<p>Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.</p>
A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
<p>There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff’s alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.</p>
Dataset and Analysis Code for an Experiment on Phosphorus Fertilizers
<p>Link to GitHub repository: <a href="https://github.com/jmalonso55/fosfatos">https://github.com/jmalonso55/fosfatos</a></p> <p>Link to analysis code and results: <a href="https://github.com/jmalonso55/fosfatos/blob/main/An%C3%A1lise_fosfatos_g.md">https://github.com/jmalonso55/fosfatos/blob/main/An%C3%A1lise_fosfatos_g.md</a></p> <p> </p> <h1><strong>About</strong></h1> <p>This repository contains the data and R code for the statistical analysis and results visualization of the paper: Ramos, J. F. K., Alves, B. J. R., Alonso, J. M., Teixeira, P. C., & Benites, V. D. M. (2025). Characterization and agronomic efficiency of natural and recovered phosphates in tropical soil with corrected acidity. <em>Rev. Bras. Ciênc. Solo</em>, <em>49</em>(spe1). Available in: <a href="https://dx.doi.org/10.36783/18069657rbcs20240099">https://dx.doi.org/10.36783/18069657rbcs20240099</a></p> <p>The study is part of the Master’s Dissertation of Ramos, J.F.K. (Ramos, J.F.K. (2023). Caracterização química, mineralógica e eficiência agronômica de diferentes fosfatos [Dissertation]. Universidade Federal Rural do Rio de Janeiro, Seropédica, Brasil). Available in: <a href="https://rima.ufrrj.br/jspui/handle/20.500.14407/18645">https://rima.ufrrj.br/jspui/handle/20.500.14407/18645</a>.</p> <h2>Methodological aspects</h2> <p>This study examined eleven phosphate fertilizer samples, encompassing Brazilian and imported products, as well as residue-recovered and soluble phosphates. The phosphate rocks of igneous origin were exclusively sourced from Brazil, specifically Catalão (Goiás) and Registro (São Paulo). Sedimentary sources included samples from Brazil (Arraias in Tocantins, Bonito in Mato Grosso do Sul, and Pratápolis in Minas Gerais) and from Morocco, Algeria, and Peru (Bayóvar). These phosphates are referred to as Catalão, Registro, Arraias, Bonito, Pratápolis, Morocco, Algeria, and Bayóvar, respectively.</p> <p>The experiment was carried out in a greenhouse, using plastic pots as experimental units, each containing 2 kg of a Ferralsol sample. The soil, initially identified as acidic (pH 4.68), was subjected to a correction process prior to the experiment. The study employed a completely randomized design with 12 treatments and four replicates, resulting in a total of 48 experimental units. The treatments included phosphate rocks (Catalão, Registro, Bonito, Pratápolis, Arraias, Morocco, Algeria, and Bayóvar), two animal-origin phosphates (Bonechar and ERCP), triple superphosphate (TSP) as a reference, and a control treatment without a phosphorus source.</p> <p>Each treatment received a single application of 320 mg P per pot (equivalent to 160 mg P per kg of soil), which was thoroughly incorporated into the soil before planting. The experiment spanned two successive cropping cycles, each lasting 45 days. At the end of each cycle, the aboveground parts of the plants were harvested, dried in a forced-air oven at 65°C until a constant weight was achieved, and their shoot dry mass (SDM) was recorded. The dried samples were finely ground in a Wiley mill and further processed in a ball mill for phosphorus content analysis. To evaluate the agronomic efficiency of the phosphate sources, the study calculated the Relative Agronomic Efficiency Index (RAE) and Phosphorus Efficiency (PE).</p>
Code Analysis Tables for Developers Interviews on Dependencies Paper
<p>Code Analysis Tables for the ACM CCS 2020 paper "A qualitative study of dependency management and its security implications"</p>
Data and code for the analysis in "Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland"
<p>Data and code used for the analysis in <em>Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland</em> (Lemaitre et al., Swiss Medial Weekly 2020).</p>
Kin selection explains the evolution of cooperation in the gut microbiota, by Simonet & McNally, 2020, Dataset S1 and codes for statistical analysis and figures production
<p>Dataset S1 contains all raw and processed material referred to in the published article "Kin selection explains the evolution of cooperation in the gut microbiota". R codes files provide all codes to replicate the analysis. Please refer to the README file for a description of all code files. The manifest files are those obtained by accessing the HMP portal on April 2020 under Project > HMP, Body Site > feces, Studies>WGS-PP1, File Type > WGS raw sequences set, File format > FASTQ.</p> <p>We also provide access to these data and codes at our GitHub (https://github.com/CamilleAnna/HamiltonRuleMicrobiome gitRepos.git) which can be cloned to directly re-run this analysis. </p> <p><strong>Legends for Dataset S1:</strong></p> <ul> <li>Sheet 1: Metagenomic samples used and access links.</li> <li>Sheet 2: Reference on bacterial cooperation retrieved from Web of Science search: TI¯((microb* OR bacter* OR microorganis* OR micro-organis*) AND (coop* OR social*)</li> <li>Sheet 3: Retained bacteria cooperation keywords</li> <li>Sheet 4: GOs identified by annotating all MIDAS database genomes (5944 genomes) with PANNZER2.</li> <li>Sheet 5: Full list of potential bacterial cooperation GO terms and description of manual curation decisions.</li> <li>Sheet 6: Final list of bacterial cooperation GO used for the analysis</li> <li>Sheet 7: Genomic diversity of the bacterial population within and across host. Computed from MIDAS snp_diversity.py pipeline.</li> <li>Sheet 8: final dataset for statistical analysis.</li> <li>Sheet 9: per-gene annotation of cooperation.</li> </ul>
Detection of Functionally Similar Code Clones: Data, Analysis Software, Benchmark
<p>We analysed 2,800 programs in Java and C for which we knew they are functionally similar. We checked if existing clone detection tools are able to find these functional similarities and classified the non-detected differences. We make all used data, the analysis software as well as the resulting benchmark available here.</p>
Empirical data, qualitative codes, analysis: Schuur J.S. et al. Identifying levers of urban neighbourhood transformation. npj Urban Sustainability (2023)
<p>Please refer to the stand-alone "2023_SchuurJS_UrbanSustainabilityfinal.html" file where the analysis and results corresponding to the article titled: "Identifying levers of urban neighbourhood transformation using serious games" is presented. The underlying data sets and Rmarkdown script used for the analysis can be used to re-run the analysis. Ensure to read the "0_README.txt" file to build the appropriate folder structure to do so.</p>
Research Data and Code for "Interdisciplinarity in the 17th Century? A Co-Occurrence Analysis of Early Modern German Dissertation Titles"
<p>This dataset documents results and code for the paper "Interdisciplinarity in the 17th Century? A Co-Occurrence Analysis of Early Modern German Dissertation Titles" by Stefan Heßbrüggen-Walter, forthcoming in *Synthese*. The data to be processed are contained in four files, derived from a larger dataset related to German dissertations and sourced from the national bibliography of 17th century German prints *VD 17* that will be released at a later date. More information can be found in the file `README.md`. </p>
Dataset and code to reproduce analysis on the impact of indoor residual spraying (IRS) on malaria at Illovo Nchalo, Malawi
<p><strong>V3 edit: </strong>The latest R file contains extra lines of code to produce prediction intervals. </p> <p> </p> <p><strong>The repository contains:</strong></p> <p>- Excel sheets for each round of indoor residual spraying from 2014 - 2018 for villages based on the Illovo Nchalo Estate (provided by public health officer)</p> <p>- Weather data for 1999 - 2019 downloaded from Sasri Weather web for Malawi - Illovo Nchalo (Open access after signing up)</p> <p>- Explanation of variables downloaded from Sasri Weather Web</p> <p>- Expected population: number of residents living in Illovo clinic's catchment areas based on 2016 and 2019 census. Linear interpolation for the other years</p> <p>- Malaria data per month per clinic from the public health officer's records at Illovo Nchalo for 7 clinics for 2014 - 2018</p> <p>- Malaria data downloaded and selected from DHIS2 (access upon request and approval)</p> <p>- R file to reproduce figures, tables, and results for the paper under submission for PLOS GPH</p> <p>- Geopackages of data that is not open-source already to reproduce the map in figure 1</p> <p> </p> <p><strong>Description of IRS data:</strong></p> <p>- Village: Name of the villages based at Illovo being targeted for IRS</p> <p>- Target_spray: Number of structures within the village targeted for spraying</p> <p>- Sprayed: Number of structures actually sprayed</p> <p>- Date_start: Start date of the IRS campaign in a village</p> <p>- Date_end: End date of the IRS campaign in that village</p> <p>- Coverage_p: Percentage of structures sprayed calculated from "target_spray" and "sprayed"</p> <p> </p> <p><strong>Notes on reconciling the different years of IRS:</strong></p> <p>1. Post office and D. compound have been added to Nkombedzi</p> <p>2. B compound has been added to Riverside/Mess</p> <p>3. The following villages attend the following clinics</p> <p> </p> <p><strong>The following villages attend the assigned clinics:</strong><br>- Mess and Bonksville -> Factory<br>- Mlambe and Paxman -> Mangulenje<br>- Sande Ranch -> Lengwe<br>- Mechanical Pool -> Mwanza</p> <p> </p> <p><strong>Description of the malaria data:</strong></p> <p>- Date, month, year</p> <p>- Time_dummy: 1 to 48, over the study period</p> <p>- Village: The name of the village the clinic is based in. In further analyses, this is referred to as "clinic" instead to avoid confusion.</p> <p>- Total_cases: total number of cases testing positive for malaria by RDT, or in a very small percentage of cases microscopy (only used when RDT gives inconclusive or conflicting results, or when symptoms persist with negative RDT). Cases_on + cases_off = total_cases</p> <p>- Cases_on: Number of malaria cases from residents of villages located within the boundaries of the Illovo estate</p> <p>- Cases_off: Number of malaria cases from residents of villages located (just) outside the boundaries of the Illovo estate</p> <p>- Total_patients: Total number of patients attending the clinic that month</p> <p> </p> <p>From the selected control clinics only "WHO NMCP P Confirmed malaria cases" was used to indicate the number of malaria cases and "CMED Total Population" to indicate the clinic catchment population. Further info on DHIS2 website. </p> <p> </p> <p>For further information don't hesitate to contact Remy Hoek Spaans. </p> <p> </p> <p> </p> <p> </p>
R Code and Re-analyzed Datasets for: Robust approaches for the quantitative analysis of genome formula variation in multipartite and segmented viruses
<p>This submission includes all the scripts and data analyzed in the manuscript "Robust approaches for the quantitative analysis of genome formula variation in multipartite and segmented viruses". This manuscript is a technical note on how genome formula data can be analyzed. There are no new experimental data in the manuscript, as published datasets are re-analyzed. Here we reproduce those datasets as formatted for our analysis, for the convenience of the reader. Please consult the README.txt file first.</p> <p>The corresponding paper was published in Viruses <em>16</em>(2): 270. (<a href="https://doi.org/10.3390/v16020270">https://doi.org/10.3390/v16020270</a>).</p> <p>This is the second version of the code, corresponding to the final version of the paper. The intial restricted version for review had a DOI 10.5281/zenodo.10355273.</p> <p> </p>
ACCESS-AM2 Southern Ocean cloud and radiation data and code for SHAP analysis
<p>The ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) and SHAP analysis code and data used for the study described in Fiddes et al. (2024) '<em>A machine learning approach for evaluating Southern Ocean cloud-radiative biases over the Southern Ocean in a global atmosphere model</em>' accepted in Geoscientific Model Development</p> <p>Included files: </p> <p>- code.zip, inc: </p> <p> - pre-process_modis.ipynb: process the modis data, described in Fiddes et al. 2022 (https://doi.org/10.5194/acp-22-14603-2022)<br> - pre-process.ipynb: organises model and modis data for analysis. Produces the files: COSP_vars_MODIS_2015-2019.nc, COSP_vars_cg207_2015-2019.nc and COSP_vars_bx400_2015-2019.nc<br> - run_XGBoost+SHAP_control.ipynb: run the XGBoost model and SHAP analysis for the control run (bx400). Produces the files: SHAP_values_SWCRE_2015-2019_bx4002.nc, XGBoost_predicted_SWCRE_2015-2019_bx4002.nc, SHAP_interactions_bx400.nc<br> - run_XGBoost+SHAP_ice.ipynb: run the XGBoost model and SHAP analysis for the ice experiment run (cg207). Produces the files: SHAP_values_SWCRE_2015-2019_cg2072.nc, XGBoost_predicted_SWCRE_2015-2019_cg2072.nc<br> - analysis+plots_ML.ipynb: plots and stats presented in paper </p> <p>- COSP_vars_MODIS_2015-2019.nc</p> <p>- COSP_vars_cg207_2015-2019.nc</p> <p>- COSP_vars_bx400_2015-2019.nc</p> <p>- SHAP_values_SWCRE_2015-2019_bx4002.nc</p> <p>- SHAP_values_SWCRE_2015-2019_cg2072.nc</p> <p>- XGBoost_predicted_SWCRE_2015-2019_cg2072.nc</p> <p>- XGBoost_predicted_SWCRE_2015-2019_bx4002.nc</p> <p>- SHAP_interaction_bx400.nc</p> <p>The cloud types used in this work can be found at https://doi.org/10.5281/zenodo.6004061 </p>
Perception and evaluation of (modified) wood by older adults from Slovenia and Norway (Datasets, R analysis code, and supplementary tables)
<p>This entry contains datasets, R analysis code, and supplementary tables for the article <em>Perception and evaluation of (modified) wood by older adults from Slovenia and Norway.</em></p> <p>The article investigates human perception and evaluation of handrails made of different materials. Our goal was to identify how older adults perceive handrails made of unmodified wood, modified wood, and steel. We examined if certain materials are more preferred than others, which material properties might be associated with differences in human preference, and what are the roles of tactile and tactile-visual domains in material perception. Our analysis is based on the results from an 11-item rating scale and a ranking task.</p>
Data analysis source code and measurement data of chemosensor salt-responsiveness
<p>Dataset with measurement data of salt-responsiveness of macrocyclic chemosensors and the Python source code for data analysis.</p>
Dataset and code from the ATLAS phone survey to replicate analysis of HIVST positivity rates and linkage to confirmatory testing
<p>This dataset and code allow to replicate the analysis presented in the paper entitled "HIV self-testing positivity rate and linkage to confirmatory testing and care: a telephone survey in Côte d'Ivoire, Mali and Senegal" by Arsène Kra Kouassi et al. Preprint available at <a href="https://doi.org/10.1101/2023.06.10.23291206">https://doi.org/10.1101/2023.06.10.23291206</a></p><p>The data was collected within the ATLAS project, funded by Unitaid and coordinated by Solthis and IRD.</p><p>ATLAS website: <a href="https://atlas.solthis.org/">https://atlas.solthis.org/</a></p><p>ATLAS presentation on Ceped website: <a href="https://www.ceped.org/atlas">https://www.ceped.org/atlas</a></p><p>ATLAS publications portal: <a href="https://hal.science/ATLAS_ADVIH/">https://hal.science/ATLAS_ADVIH/</a></p><p> </p><p> </p>
Dataset and Source Code for the Paper: A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms
<p>Here are the data set and source code related to the paper: "A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms"</p> <p>1- aptnotes-downloader.zip : contains source code that downloads all APT reports listed in https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections</p> <p>2- apt-groups.zip : contains all APT group names gathered from https://docs.google.com/spreadsheets/d/1H9_xaxQHpWaa4O_Son4Gx0YOIzlcBWMsdvePFX68EKU/edit?gid=1864660085#gid=1864660085 and https://malpedia.caad.fkie.fraunhofer.de/actors and https://malpedia.caad.fkie.fraunhofer.de/actors</p> <p>3- apt-reports.zip : contains all deduplicated APT reports gathered from https://github.com/aptnotes/data and https://github.com/CyberMonitor/APT_CyberCriminal_Campagin_Collections</p> <p>4- countries.zip : contains country name list.</p> <p>5- ttps.zip : contains all MITRE techniques gathered from https://attack.mitre.org/resources/attack-data-and-tools/</p> <p>6- malware-families.zip : contains all malware family names gathered from https://malpedia.caad.fkie.fraunhofer.de/families</p> <p>7- ioc-searcher-app.zip : contains source code that extracts IoCs from APT reports. Extracted IoC files are provided in report-analyser.zip. Original code repo can be found at https://github.com/malicialab/iocsearcher</p> <p>8- extracted-iocs.zip : contains extracted IoCs by ioc-searcher-app.zip</p> <p>9- report-analyser.zip : contains source code that searchs APT reports, malware families, countries and TTPs. I case of a match, it updates files in extracted-iocs.zip.</p> <p>10- cti-transformation-app.zip : contains source code that transforms files in extracted-iocs.zip to CTI triples and saves into Neo4j graph database.</p> <p>11- graph-db-backup.zip : contains volume folder of Neo4j Docker container. When it is mounted to a Docker container, all CTI database becomes reachable from Neo4j web interface. Here is how to run a Neo4j Docker container that mounts folder in the zip:</p> <p>docker run -d --publish=7474:7474 --publish=7687:7687 --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/data:/data --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/plugins:/plugins --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/logs:/logs --volume={PATH_TO_VOLUME}/DEVIL_NEO4J_VOLUME/neo4j/conf:/conf --env 'NEO4J_PLUGINS=["apoc","graph-data-science"]' --env NEO4J_apoc_export_file_enabled=true --env NEO4J_apoc_import_file_enabled=true --env NEO4J_apoc_import_file_use__neo4j__config=true --env=NEO4J_AUTH=none neo4j:5.13.0</p> <h4><strong>web interface: http://localhost:7474</strong></h4> <h4><strong>username: neo4j</strong></h4> <h4><strong>password: neo4j</strong></h4> <p> </p>
Data and analysis code for a toxin induction study on two species of Dinophysis dinoflagellates
<h3>General description</h3> <p>This repository contains the datasets, analysis code and output generated and used in the manuscript "Effects of copepod chemical cues on intra- and extracellular toxins in two species of <em>Dinophysis</em>", which has been published as a research article in Harmful Algae (https://doi.org/10.1016/j.hal.2024.102793).</p> <h3>Files</h3> <p><strong>HRMS_data_Dinophysis_induction_experiment.zip </strong>contains the source high-resolution mass-spectroscopy endometabolomics data in open file formats.</p> <p><strong>Put_annot_sign_affect_feat_metabol_data.xlsx</strong> (corresponds to <strong>Supplementary spreadsheet 1</strong> in the main manuscript) contains putative annotations of significantly affected features from the metabolomics data, for each <em>Dinophysis </em>species (<em>D.</em> <em>sacculus </em>and <em>D. acuminata</em>) and each mode of ionization. Notably, results obtained from GNPS (Global Natural Products Social Molecular Networking, <a href="https://gnps.ucsd.edu/" target="_blank" rel="noopener noreferrer">https://gnps.ucsd.edu/</a>), and SIRIUS (<a href="https://bio.informatik.uni-jena.de/sirius/" target="_blank" rel="noopener noreferrer">https://bio.informatik.uni-jena.de/sirius/</a>) were mentionned. When available, MS/MS spectra were also provided. </p> <p><strong>Tabl_sign_affect_feat.xlsx</strong> (corresponds to <strong>Supplementary spreadsheet 3</strong> in the main manuscript) contains tables of significantly affected features (ANOVA, Tukey’s post hoc test, adjusted p-value cut-offs of 0.001 or 0.01) from the metabolomics data, for each <em>Dinophysis </em>species (<em>sacculus </em>and <em>acuminata</em>) and each mode of ionization. A visual representation (heatmaps) of these significant fetures are available as Figs S3-S6 in the supplementary information of the main mauscript. </p> <p><strong>Toxin_analysis_code_output.Rmd</strong> is the R-markdown file that produces the interactive analysis output output (<strong>Toxin_analysis_code_output.html</strong>, corresponds to <strong>Supplementary code & output </strong>in the main manuscript) of the toxin analysis, and uses the datasets <strong>Toxin_analysis_data.csv</strong>,<strong> pca_score_sacculus.csv</strong>,<strong> </strong>and<strong> pca_score_acuminata.csv</strong> source datasets to perform the statistical analyses and generate figures (details for each dataset below).</p> <p><strong>Toxin_analysis_data.csv</strong> (corresponds to <strong>Supplementary spreadsheet 2</strong> in the main manuscript) contains the main data used to statistically analyse the toxin and growth dynamics of both <em>Dinophysis</em> species in response to different grazer treatments, and to produce the majority of the figures in the main manuscript (Figs. 2-6) and supplementary information (Figs. S1-S2). </p> <p><strong>pca_score_sacculus.csv</strong> & <strong>pca_score_acuminata.csv</strong> contain the scores of the first two principal components of the PCA performed on LC-HRMS derived metabolomic profiles of <em>D. sacculus </em>and <em>D. acuminata</em> respectively, in both positive and negative ion mode. These are used to produce PCA score plots (Fig. 6 in the main manuscript and Fig. S2 in the supplementary information).</p> <p><strong>custom.css</strong> is a custom html style sheet file that formats the <strong>Toxin_analysis_code_output.html</strong> to display scrollable tables correctly. It is used by <strong>Toxin_analysis_code_output.Rmd </strong>and is necessary for true reproduction of the output (<strong>.html</strong>) file.</p>
Derived data and analysis code accompanying Deines et al. 2019, Environmental Research Letters
<p>This codebase accompanies the paper:</p> <p>Deines, JM, AD Kendall, JJ Butler, Jr., & DW Hyndman. 2019. Quantifying irrigation adaptation strategies in response to stakeholder-driven groundwater management in the US High Plains Aquifer. Environmental Research Letters. DOI: <a href="https://doi.org/10.1088/1748-9326/aafe39">https://doi.org/10.1088/1748-9326/aafe39</a></p> <p>Data and code at time of publication.</p>
Gut Analysis Toolbox: Data and code associated with JCS manuscript
<p>The data and python code in jupyter notebooks are associated with the manuscript: <strong><em>Sorensen et al. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950. doi: <a href="https://doi.org/10.1242/jcs.261950" target="_blank" rel="noopener">https://doi.org/10.1242/jcs.261950</a></em></strong></p> <ul> <li><strong>FigS1_analysis.zip</strong>: Data files (csv) and jupyter notebooks (ipynb) pertaining to Fig. S1D,E.</li> <li><strong>Fig3_analysis.zip</strong>: Data files (csv) and jupyter notebooks (ipynb) pertaining to Fig. 3D-N. <ul> <li>The images and analysis files associated with analysis in GAT are also uploaded: CalR_CalB_GAT_analysis.zip</li> <li>The images used in this analysis are from EXP174 in this dataset: <a href="https://zenodo.org/records/7236748">https://zenodo.org/records/7236748</a></li> </ul> </li> </ul>
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.