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54 results for “open code”
Open dataset of 10% gelatin penetration experiments and simulation code for 4.5 and 6.35 mm caliber
<p>Paper 1:</p> <p>PENETRATION EXPERIMENTS AND 1D SIMULATIONS OF AIR RIFLE BB 4.5 MM STEEL SPHERE AND GAMO HUNTER 4.5 MM IMPACTING 10% GELATINE</p> <p><strong>Keywords:</strong> Penetration, experiments, simulations, gelatine, air rifle, projectiles, high-speed camera, chronograph, BB 4.5 mm, steel sphere, Gamo Hunter 4.5 mm, penetration depth, dynamic cavity size, drag force, shear force, shear rate effect</p> <p><strong>Abstract</strong>. Gelatine of 10% concentration is often used as projectile target to simulate soft tissues of animals and humans. The 10% gelatine used in the tests fulfills the FBI standard protocol for penetration depth of BB 4.5 mm steel sphere impacting with 180 m/s. Here, experiments were conducted with BB 4.5 mm steel sphere and diabolo Gamo Hunter 4.5 mm pellets with varying impact velocity. The impact velocity was measured by chronograph. The deceleration and dynamic cavity size was studied by using high-speed camera with 10000 frames per second. Final penetration depth was manually measured. A 1D simulation model, considering the retardation force generated by drag and shear, was used to predict penetration depth in both 10% and 20% gelatine for projectiles with spherical nose shape. From the simulations it becomes evident that the drag force and static shear force alone can’t explain the total retardation force to achieve the expected penetration depth. In the literature for experiments conducted on both 10% and 20% gelatine, there is confirmation that projectiles impacting with velocity above 100 m/s, the shear strength in the gelatine is increased by a factor 40 and 70 times for 10% and 20% gelatine, respectively. This concept was successfully used in the simulations by introducing a shear rate dependent shear force model which increases the static shear force of 10% and 20% gelatine. All experimental results, including high speed films and python simulation code of gelatine penetration, are publicly available, see [1], [2]. </p> <p>Paper 2:</p> <p>Penetration experiments of air rifle projectiles with caliber 6.35 mm and different nose shapes impacting 10% gelatinE</p> <p><strong>Keywords:</strong> Penetration, experiments, gelatine, air rifle, projectiles, high speed camera, chronograph, caliber 6.35 mm, H&N pellets, penetration depth, dynamic cavity size</p> <p><strong>Abstract</strong><em>. Air rifles in caliber 6.35 mm and more than 10 J in kinetic energy at impact can have a significant effect on soft tissues of animals and humans. To study the effect, experimental penetration studies have been conducted by using test specimens made of 10% gelatine substitute. The 10% gelatine used in the tests fulfilled the FBI standard protocol for penetration depth of 4.5 mm BB steel sphere impacting with 180 m/s. The deceleration, penetration depth, and dynamic penetration cavity size for the projectiles with different nose shapes impacting gelatine 10% have been studied. The impact velocity was measured by using a chronograph and the initial kinetic energy before impact of the projectiles was up to 84 J (330 m/s). The deceleration and dynamic cavity size was studied by using high-speed camera with 10000 frames per second. Final penetration depth was manually measured. The nose and projectile shape influenced the dynamic cavity size and final penetration depth in the gelatine to a large extent. All experimental results, including high speed films and python simulation code of gelatine penetration, are publicly available, see [1]. </em></p> <p>Any results used from this open access data shall be cited to paper 1 and paper 2, the papers are found in the proceedings of the 14<sup>th </sup>International Conference on Shock & Impact Loads on Structures, 30-31 March 2023, Singapore.</p> <p> </p>
Over and Under Sampled Data-sets of Code Issues in Java Open-Source Projects
<p>The dataset comprises code changes made to 15 Java Open-Source projects, classified with sentiment values (0 for negative and 1 for positive) based on developer reviews during various revision submissions. The dataset is available in 8 versions, each containing a sampled dataset using an over or under-sampling technique.</p>
QAnubis - open source coding tool
<p>QAnubis - open source coding tool</p> <p>The main activity of qualitative research is data analysis, which involves a process of collecting and analyzing narrative data, including texts, photos, audiovisual elements, and various digital file formats. Different approaches and methodologies are available for qualitative data investigation, with a greater emphasis on coding, where artifacts and their contents are categorized under a finite set of categories. Similar to other fields, efforts are made to develop and enhance computerized tools known as Computer-Assisted Qualitative Data Analysis Software (CAQDAS) that aim to increase the efficiency and effectiveness of the analysis process by providing means to assist in data identification, organization, interpretation, exploration, and integration.</p> <p>To contribute to the field of qualitative analysis, this work presents an open-source web tool called QAnubis for performing coding on PDF files while preserving their original content and formatting.</p>
Data and code from: Big trees burning: Divergent wildfire effects on large trees in open- vs. closed-canopy forests
Open the record for dataset details and reuse information.
Dataset and Code for "Mining and Predicting Micro-Process Patterns of Issue Resolution for Open Source Software Projects"
<p>Dataset and Code for "Mining and Predicting Micro-Process Patterns of Issue Resolution for Open Source Software Projects" with README included</p>
Open source physiological data and physiological-based kinetic model code for the chicken (Gallus gallus domesticus)
<p>This excel file and mode code (DOI:10.5281/zenodo.3603114) provides:</p> <p>1. Physiological parameters and associated inter-individual variability (sample size, mean, coefficient of variation,) for chicken (<em>Gallus gallus domesticus</em>). These physiological parameters were estimated based on the results of extensive literature searches and specific experimental data described in Lautz et al., (2020).</p> <p>2. An R code for the generic chicken physiologically based model as well as the “soboljansen” code to carry out sensitivity analysis using sobol plots. The code for the generic model allows to run:</p> <p>a. A deterministic PBK model which represents only a single animal.</p> <p>b. A probabilistic PBK model to simulate individual differences in physiological parameters within a population. Sensitivity analyses can be performed to identify which parameters have the most impact on the model’s outputs. Predictions can be compared with experimental data. The model can be used to assess the influence of physiological parameters on the kinetics of chemicals. For PBK modelling purposes, species and chemical specific kinetics (e.g clearance, absorption rate, etc…) should be provided by the user.</p> <p>The full data collection and implementation of the models using case studies are described in (Lautz et al., 2020).</p> <p><strong>The dataset providing the physiological parameters is available in Excel.<br> The R code is presented as meta data to be implemented in R.</strong></p>
Data and code for: The evolution of siphonophore tentilla for specialized prey capture in the open ocean
<p>Predator specialization has often been considered an evolutionary 'dead-end' due to the constraints associated with the evolution of morphological and functional optimizations throughout the organism. However, in some predators, these changes are localized in separate structures dedicated to prey capture. One of the most extreme cases of this modularity can be observed in siphonophores, a clade of pelagic colonial cnidarians that use tentilla (tentacle side branches armed with nematocysts) exclusively for prey capture. Here we study how siphonophore specialists and generalists evolve, and what morphological changes are associated with these transitions. To answer these questions, we: (1) measured 29 morphological characters of tentacles from 45 siphonophore species, (2) mapped these data to a phylogenetic tree, and (3) analyzed the evolutionary associations between morphological characters and prey type data from the literature. Instead of a dead-end, we found that siphonophore specialists can evolve into generalists, and that specialists on one prey type have directly evolved into specialists on other prey types. Our results show that siphonophore tentillum morphology has strong evolutionary associations with prey type, and suggest that shifts between prey types are linked to shifts in the morphology, mode of evolution, and genetic correlations of tentilla and their nematocysts. The evolutionary history of siphonophore specialization helps build a broader perspective on predatory niche diversification via morphological innovation and evolution. These findings contribute to understanding how specialization and morphological evolution have shaped present-day food webs.</p>
Refactoring Code Smells in Open Source Projects: A Hands-on Approach to Teaching Software Maintenance
<p>Code smells are suboptimal code structures that can undermine software quality and maintainability. On the one hand, software engineers commonly apply refactoring techniques to address these deficiencies and improve internal quality attributes. On the other hand, when performed manually and without discipline, refactoring can lead to code degradation. Despite its importance, refactoring and code smells are rarely explored in depth in undergraduate computing courses, which can be reflected in industry practices. To address this gap, this paper presents a hands-on approach to teaching code smell refactoring through contributions to Open Source Software (OSS) projects, an environment where developers with diverse skill levels collaborate, and maintaining code quality is particularly challenging. Code smells accumulate over time in such scenarios, hindering software evolution and collaboration. Our study in two undergraduate Software Quality and Software Maintenance courses expands on previous findings by incorporating an in-depth analysis of students’ learning experiences. The results indicate that: (i) students rec- ognized improvements in code quality after refactoring; (ii) they identified strong connections between refactoring, testing, and debugging; (iii) their confidence decreased when refactoring required changes across multiple files; (iv) code complexity posed a significant challenge to refactoring; (v) students’ choices of refactoring techniques were influenced by project structure and personal preferences, often combining multiple techniques to address a single smell; (vi) in some cases, refactoring introduced new code smells; (vii) the longest refactoring efforts were also the most likely to reintroduce code smells; (viii) contributing to OSS projects improved students’ programming skills and fostered a sense of professional growth; (ix) students faced challenges in understanding OSS contribution processes, particularly regarding issue resolution, adherence to contribution guidelines, and responding to maintainer feedback; (x) automated checks and review workflows varied across projects, affecting students’ ability to submit successful contributions; and (xi) despite these challenges, engagement with OSS enabled students to gain practical experience in collaborative software development. Our findings offer valuable insights for software engineering educators seeking to integrate refactoring practices into coursework while leveraging OSS contributions as an educational tool.</p>
Data and codes for figures in "Open water in sea ice causes high bias in polar low-level clouds in GFDL CM4"
Open the record for dataset details and reuse information.
Xacro ROS Answers Open Coding
<p>Open coding analysis of Xacro ROS Answers questions and their respective ROS Answer tags.</p>
Code Smells and their Collocations : A Large-scale Experiment on Open-source Systems
<p>This dataset includes classes with code smells, acquired from Qualitas Corpus (QC).<br> Folder 'all' contains data coming from the QC rev.20130901 (92 systems).<br> Folder 'domains' contains data coming from QC rev.20111026 (76 systems updated to their most recent releases from rev.20130901). <br> Folder 'pca' includes results of the PCA analysis, generated with the R prcomp() function for regular PCA, and logisticPCA() function for the binary data.</p> <p>Filenames include information about the base release of the QC, and a number (25, 50 or 75) that specifies the minimum number of detectors that identified a specific smell instance (25%, 50%, and 75%, respectively). For example, if a given code smell in a class X has been identified by 1 out of 4 available detecting tools, then the smell for the class X will be reported in the respective file 25, but not in 50 or 75. Please note, that for smells detected with only one tool, the values would be equal in all datasets (in that case, the smell was detected by 0% or 100% of tools)</p> <p>In all files, "1" denotes that the smell was identified (subject to the limitations with the number of detectors, described above), and “0” that the smell was not found in a given class.</p> <p>The filename also includes the domain abbreviation (app, css, dev, dgdv) or a keyword ALL, which indicates that the dataset includes data from all domains.</p> <p>The smells have been detected by 11 tools. Most of the tools detect more than one smell. <br> Information about the tool used to detect a given smell is given in headers of each file. Additionally, in 'smell detectors.csv' file we present the information about smells detected by a specific tool.</p>
Dataset and Code for "Mining Micro-Patterns of Issue Resolution Processes for Open Source Software Projects"
<p>Dataset and Code for "Mining Micro-Patterns of Issue Resolution Processes for Open Source Software Projects" with README included</p>
A Weighting Function Model for Unsteady Open Channel Friction: Raw code and output
<p>Matlab source code and Raw Origin Lab file (.opj) for a weighting function model simulating unsteady open channel friction slopes. This material is provided as is. Methods (formulas) and Figures are explained further in the related article "A Weighting Function Model for Unsteady Open Channel Friction" by Junwei Zhou; Weimin Bao; Geoffrey R. Tick; Qing Cao; and Fanghong Ye.</p>
Reproduction package for the paper "The open-source sunbather code: Modeling escaping planetary atmospheres and their transit spectra"
<p>This is a reproduction package for the paper "The open-source sunbather code: modeling escaping planetary atmospheres and their transit spectra" by Dion Linssen, Jim Shih, Morgan MacLeod & Antonija Oklopčić (2024). It provides a front-to-end reproduction script to reproduce the results and Figures 1-5 of the paper. Figures 6&7 can be reproduced with the example notebook found in the sunbather installation.</p>
Datasets and scripts related to the paper: "*Can Generative AI Help us in Open Coding of Software Engineering Data?*"
<p>This replication package contains datasets and scripts related to the paper: "<em>Can Generative AI Help us in Open Coding of Software Engineering Data?</em>"</p> <p>The replication package is organized into two directories:</p> <ul> <li> <p><code>manual_analysis</code>: This directory contains all sheets used to perform the manual analysis for RQ1, RQ2, and RQ3.</p> </li> <li> <p><code>stats</code>: This directory contains all datasets, scripts, and results metrics used for the quantitative analyses of RQ1 and RQ2.</p> </li> </ul> <p>In the following, we describe the content of each directory:</p> <h2>manual_analysis</h2> <ul> <li> <p><code>manual_analysis_rq1</code>: This directory contains all sheets used to perform manual analysis for RQ1 (independent and incremental coding).</p> <ul> <li> <p>The sub-directory <code>incremental_coding</code> contains .csv files for all datasets (<code>DL_Faults_COMMIT_incremental.csv</code>, <code>DL_Faults_ISSUE_incremental.csv</code>, <code>DL_Fault_SO_incremental.csv</code>, <code>DRL_Challenges_incremental.csv</code> and <code>Functional_incremental.csv</code>). All these .csv files contain the following columns:</p> <ul> <li><em>Link</em>: The link to the instances</li> <li><em>Prompt</em>: Prompt used as input to GPT-4-Turbo</li> <li><em>ID</em>: Instance ID</li> <li><em>FinalTag</em>: Tag assigned by the human in the original paper</li> <li><em>Chatgpt_output_memory</em>: Output of GPT-4-Turbo with incremental coding</li> <li><em>Chatgpt_output_memory_clean</em>: (only for the DL Faults datasets) output of GPT-4-Turbo considering only the label assigned, excluding the text</li> <li><em>Author1</em>: Label assigned by the first author</li> <li><em>Author2</em>: Label assigned by the second author</li> <li><em>FinalOutput</em>: Label assigned after the resolution of the conflicts</li> </ul> </li> <li> <p>The sub-directory <code>independent_coding</code> contains .csv files for all datasets (<code>DL_Faults_COMMIT_independent.csv</code>, <code>DL_Faults_ISSUE_ independent.csv</code>, <code>DL_Fault_SO_ independent.csv</code>, <code>DRL_Challenges_ independent.csv</code> and <code>Functional_ independent.csv</code>), containing the following columns:</p> <ul> <li><em>Link</em>: The link to the instances</li> <li><em>Prompt</em>: Prompt used as input to GPT-4-Turbo</li> <li><em>ID</em>: Specific ID for the instance</li> <li><em>FinalTag</em>: Tag assigned by the human in the original paper</li> <li><em>Chatgpt_output</em>: Output of GPT-4-Turbo with independent coding</li> <li><em>Chatgpt_output_clean</em>: (only for DL Faults datasets) output of GPT-4-Turbo considering only the label assigned, excluding the text</li> <li><em>Author1</em>: Label assigned by the first author</li> <li><em>Author2</em>: Label assigned by the second author</li> <li><em>FinalOutput</em>: Label assigned after the resolution of the conflicts.</li> </ul> </li> <li> <p>Also, the sub-directory contains sheets with inconsistencies after resolving conflicts. The directory <code>inconsistency_incremental_coding</code> contains .csv files with the following columns:</p> <ul> <li><em>Dataset</em>: The dataset considered</li> <li><em>Human</em>: The label assigned by the human in the original paper</li> <li><em>Machine</em>: The label assigned by GPT-4-Turbo</li> <li><em>Classification</em>: The final label assigned by the authors after resolving the conflicts. Multiple classifications for a single instance are separated by a comma “,”</li> <li><em>Final</em>: final label assigned after the resolution of the incompatibilities</li> </ul> </li> <li> <p>Similarly, the sub-directory <code>inconsistency_independent_coding</code> contains a .csv file with the same columns as before, but this is for the case of independent coding.</p> </li> </ul> </li> <li> <p><code>manual_analysis_rq2</code>: This directory contains .csv files for all datasets (<code>DL_Faults_redundant_tag.csv</code>, <code>DRL_Challenges_redundant_tag.csv</code>, <code>Functional_redundant_tag.csv</code>) to perform manual analysis for RQ2.</p> <ul> <li> <p>The <code>DL_Faults_redundant_tag.csv</code> file contains the following columns:</p> <ul> <li><em>Tags Redundant</em>: tags identified as redundant by GPT-4-Turbo</li> <li><em>Matched</em>: inspection by the authors to see if the tags are redundant matching or not</li> <li><em>FinalTag</em>: final tag assigned by the authors after the resolution of the conflict</li> </ul> </li> <li> <p>The <code>Functional_redundant_tag.csv</code> file contains the same columns as before</p> </li> <li> <p>The <code>DRL_Challenges_redundant_tag.csv</code> file is organized as follows:</p> <ul> <li><em>Tags Suggested</em>: The final tag suggested by GPT-4-Turbo</li> <li><em>Tags Redundant</em>: tags identified as redundant by GPT-4-Turbo</li> <li><em>Matched</em>: inspection by the authors to see if the tags redundant matching or not with the tags suggested</li> <li><em>FinalTag</em>: final tag assigned by the authors after the resolution of the conflict</li> </ul> </li> <li> <p>The sub-directory <code>code_consolidation_mapping_overview</code> contains .csv files (<code>DL_Faults_rq2_overview.csv</code>, <code>DRL_Challenges_rq2_overview.csv</code>, <code>Functional_rq2_overview.csv</code>) organized as follows:</p> <ul> <li><em>Initial_Tags</em>: list of the unique initial tags assigned by GPT-4-Turbo for each dataset</li> <li><em>Mapped_tags</em>: list of tags mapped by GPT-4-Turbo</li> <li><em>Unmatched_tags</em>: list of unmatched tags by GPT-4-Turbo</li> <li><em>Aggregating_tags</em>: list of consolidated tags</li> <li><em>Final_tags</em>: list of final tags after the consolidation task</li> </ul> </li> </ul> </li> <li> <p><code>prompt_for_each_rq</code>: This directory contains: - (i) the history of prompts used in each dataset (<code>prompts_history.txt</code>) -(ii) all final prompt used for the analysis of each dataset, prompt used for incremental coding, prompt used in rq2 to consolidate redundant codes, prompt used in rq3 to create taxonomy (<code>generic_prompt.txt</code>) -(iii) all .csv files in which there are indicate, for each dataset, the link and the prompt used (<code>prompt_DL_Faults_COMMIT.csv</code>, <code>prompt_DL_Faults_ISSUE.csv</code>, <code>prompt_DL_Faults_SO.csv</code>, <code>prompt_DRL_Challenges.csv</code>). For the Functional Dataset .csv file contains, instead, Question, Answer and Prompt used (<code>prompt_Functional.csv</code>)</p> </li> <li> <p><code>rq3</code>: This directory contains the taxonomies obtained from GPT-4-Turbo for the DL Faults and for the DRL Challenges (<code>taxonomy_DL_Faults.txt</code>,<code>taxonomy_DRL_Challenges.txt</code>)</p> </li> </ul> <h2>stats</h2> <ul> <li> <p><code>RQ1</code>: contains script and datasets used to perform metrics for RQ1. The analysis calculates all possible combinations between Matched, More Abstract, More Specific, and Unmatched.</p> <ul> <li><code>RQ1_Stats.ipynb</code> is a Python Jupyter nooteook to compute the RQ1 metrics. To use it, as explained in the notebook, it is necessary to change the values of variables contained in the first code block.</li> <li><code>independent-prompting</code>: Contains the datasets related to the independent prompting. Each line contains the following fields: <ul> <li><em>Link</em>: Link to the artifact being tagged</li> <li><em>Prompt</em>: Prompt sent to GPT-4-Turbo</li> <li><em>FinalTag</em>: Artifact coding from the replicated study</li> <li><em>chatgpt_output_text</em>: GPT-4-Turbo output</li> <li><em>chatgpt_output</em>: Codes parsed from the GPT-4-Turbo output</li> <li><em>Author1</em>: Annotator 1 evaluation of the coding</li> <li><em>Author2</em>: Annotator 2 evaluation of the coding</li> <li><em>FinalOutput</em>: Consolidated evaluation</li> </ul> </li> <li><code>incremental-prompting</code>: Contains the datasets related to the incremental prompting (same format as independent prompting)</li> <li><code>results</code>: contains files for the RQ1 quantitative results. The files are named <code>RQ1\_<<Dataset>>\_<<Prompt method>>\_<<ExcludingNegative>>\_<<MetricAggregation>>.csv</code>, where <em>Dataset</em> is the dataset name, <em>Prompt method</em> indicates whether results are for independent or incremental prompting, <em>Excluding Negatives</em> (for datasets where this applies) whether results have been obtained by excluding negative instances, and <em>MetricAggregation</em> (where it applies) how metrics have been aggregated (macro or weighted average). The files report columns indicating the <em>Dataset</em>, the <em>Matching type</em>, the <em>Accuracy</em>, <em>Precision</em>, <em>Recall</em>, <em>F1 Score</em>, and <em>Cohen's Kappa</em>.</li> </ul> </li> <li> <p><code>RQ2</code>: contains the script used to perform metrics for RQ2, the datasets it uses, and its output.</p> <ul> <li><code>RQ2_SetStats.ipynb</code> is the Python Jupyter notebook to perform the analyses. The scripts takes as input the following types of files, contained in the directory contains the script used to perform the metrics for RQ2. The script takes in input:</li> <li>RQ1 Data Files (<code>RQ1_DLFaults_Issues.csv</code>, <code>RQ1_DLFaults_Commits.csv</code>, and <code>RQ1_DLFaults_SO.csv</code>, joined in a single .csv <code>RQ1_DLFaults.csv</code>). These are the same files used in RQ1.</li> <li>Mapping Files (<code>RQ2_Mappings_DRL.csv</code>, <code>RQ2_Mappings_Functional.csv</code>, <code>RQ2_Mappings_DLFaults.csv</code>). These contain the mappings between human tags (<em>HumanTags</em>), GPT-4-Turbo tags (<em>Final Tags</em>), with indicated the type of matching (<em>MatchType</em>).</li> <li>Additional codes creating during the consolidation (<code>RQ2_newCodes_DRL.csv</code>, <code>RQ2_newCodes_Functional.csv</code>, <code>RQ2_newCodes_DLFaults.csv</code>), annotated with the matching: <em>new code</em>,<em>old code</em>,<em>human code</em>,<em>match type</em></li> <li>Set files (<code>RQ2_Sets_DRL.csv</code>, <code>RQ2_Sets_Functional.csv</code>, <code>RQ2_Sets_DLFaults.csv</code>). Each file contains the following columns: <ul> <li><em>HumanTags</em>: List of tags from the original dataset</li> <li><em>InitialTags</em>: Set of tags from RQ1,</li> <li><em>ConsolidatedTags</em>: Tags that have been consolidated,</li> <li><em>FinalTags</em>: Final set of tags (results of RQ2, used in RQ3)</li> <li><em>NewTags</em>: New tags created during consolidation</li> </ul> </li> <li><code>RQ2_Set_Metrics.csv</code>: Reports the RQ2 output metrics (Precision, Recall, F1-Score, Jaccard).</li> </ul> </li> </ul>
Code & Data for "Adoption of Transparency and Openness Promotion (TOP) guidelines across journals"
<p>This entry contains code and data that was used in the publication: "Adoption of Transparency and Openness Promotion (TOP) guidelines across journals" submitted in Publications journal.</p> <p>*It was version 2 when we added Fig_3_Tab2_Defining_science_disciplines_plus_plot.R script to version 1.</p> <p>*It was version 3 because we added script that calculates median and mean values of the stringency levels to version 2 data.</p> <p>*Latest version is version 4: we added supplementary data.</p> <p>#IDEA:</p> <p>This project was about analyzing policies of two thousand journals within the framework of eight TOP standards: <br> data citation, transparency of data, material, code and design and analysis, replication, plan and study pre-registration, <br> and two effective interventions: “Registered reports” and “Open science badges”. </p> <p># MATERIALS & METHODS<br> We downloaded the TOP Factor (v33, 2022-08-29 3:12 PM) metric from the https://osf.io/kgnva/files/osfstorage/5e13502257341901c3805317 <br> website and analyzed its content with an in-house R script (in this repo):<br> 1) SCRIPT: fig1_Analyzing_journals_policies_and_TOP_guidelines.R<br> 2) SCRIPT: Figure2a_b_TOP_impl_journal_statistist_0_1_piechart_barplot.R<br> In order to get statistics about implementation of the TOP guidelines across discipline-specific journals, <br> we extracted information about journal’s disciplines from the Scopus content database. <br> We downloaded SCOPUS content coverage from the https://www.elsevier.com/solutions/scopus/how-scopus-works/content?dgcid=RN_AGCM_Sourced_300005030 (existJuly2022.xlsx)<br> and used the first Sheet.<br> We identified match between those 2 tables: <br> 3) SCRIPT: Rscript_overlapping_TOP_dataframe_and_SCOPUS_db.R<br> And resulted in Overlap_SCOPUS_TOP.rds file<br> And performed visualization and statistics:<br> 4) SCRIPT: Fig_3_Tab2_Defining_science_disciplines_plus_plot.R</p> <p> </p> <p>#RESULTS Submitted to Publications 30.9.2022.</p> <p>Reviewed 2.11.2022.</p> <p>Latest version: 25.11.2022.</p>
Data and code for "An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model"
<p>Original data and code associated with the paper "An open-source alignment method for multichannel infinite-conjugate microscopes using a ray transfer matrix analysis model".<br> <br> Further details on the data are available in the readme.txt files.</p>
Data and code for: "An open-source GIS approach to understanding dunefield morphologic variability at Kati Thanda (Lake Eyre), central Australia"
<p>Data and reproducabel code</p>
Data and code for: The evolution of siphonophore tentilla for specialized prey capture in the open ocean
Open the record for dataset details and reuse information.
Open Wave Height Logger analysis code and data
<p>Analysis code (R language) and associated data files used to generate data and statistics for the manuscript <em>"Open Wave Height Logger: an open source pressure sensor data logger for wave measurement", </em>authored by Ted Lyman<sup>1</sup>, Kristen Elsmore<sup>2</sup>, Brian Gaylord<sup>2,3</sup>, Jarrett E. Byrnes<sup>1</sup>, Luke P. Miller<sup>4 </sup>(corresponding author, luke.miller@sdsu.edu). </p> <p> </p> <p><sup>1</sup> University of Massachusetts, Boston, 100 Morrisey Blvd, Boston, MA 02125</p> <p><sup>2</sup> Bodega Marine Laboratory, University of California Davis, 2099 Westshore Road, Bodega Bay, CA 94923</p> <p><sup>3</sup> Department of Evolution and Ecology, University of California Davis, One Shields Road, Davis, CA 95616</p> <p><sup>4</sup> Department of Biology, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182</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.