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Figs 1–3 in Big-Eyed Bugs Of The Malagasy Region (Hemiptera: Heteroptera: Lygaeoidea: Geocoridae)
Figs 1–3. Geocoris (Piocoris) petofii sp. n. (holotype, female, MNHN): 1 = dorsal habitus, 2 = lateral view, 3 = labels. Scale bar = 0.5 mm for Figs 1 and 2, Fig. 3 not to scale
Figs 4–11 in Big-Eyed Bugs Of The Malagasy Region (Hemiptera: Heteroptera: Lygaeoidea: Geocoridae)
Figs 4–11. Selected morphological characteristics of Geocoris (Piocoris) petofii sp. n.: 4 = furrows of vertex and clypeus (red arrows indicate transversal furrow anteriad to ocelli and median longitudinal furrow); 5 = venter of head [red arrow indicates labial trough (lt)]; 6 = hemelytron [red arrows indicate apex of reduced R-M and venation of membrane (mv)]; 7 = exoskeletal structures of MTSEA [red arrow indicates peritreme (pe)]; 8 = abdominal dorsum (red arrow indicates inclined sutures of tergites IV/V–V/VI); 9 = female ovipositor;
Figs 15–20 in Big-Eyed Bugs Of The Malagasy Region (Hemiptera: Heteroptera: Lygaeoidea: Geocoridae)
Figs 15–20. Geocorine true bugs of the Malagasy region (dorsal habitus): 15 = Geocoris pallidipennis mauritii Stål, 1854 (male, MNHN); 16 = Germalus banari Kondorosy et Kóbor, 2016 (paratype, male, MNHN); 17 = Germalus benyovszkyi Kondorosy et Kóbor, 2016 (holotype, male, MNHN); 18 = Germalus kinbergi Stål, 1860 (lectotype, female, NHRS); 19 = Hypogeocoris alluaudi (Montandon, 1908) (male, MNHN); 20 = Hypogeocoris violaceus (Signoret, 1881)
Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code
<p>Artifact repository for the paper <a href="http://arxiv.org/abs/2308.03109" rel="nofollow"><em>Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code</em></a>, accepted at <em>ICSE 2024</em>, Lisbon, Portugal. Authors are <a href="https://rangeetpan.github.io/" rel="nofollow">Rangeet Pan</a>* <a href="https://alirezai.cs.illinois.edu/" rel="nofollow">Ali Reza Ibrahimzada</a>*, <a href="http://rkrsn.us/" rel="nofollow">Rahul Krishna</a>, Divya Sankar, Lambert Pougeum Wassi, Michele Merler, Boris Sobolev, Raju Pavuluri, Saurabh Sinha, and <a href="https://reyhaneh.cs.illinois.edu/index.htm" rel="nofollow">Reyhaneh Jabbarvand</a>.</p> <h3>Install</h3> <p>This repository contains the source code for reproducing the results in our paper. Please start by cloning this repository:</p> <div> <pre><code>git clone https://github.com/Intelligent-CAT-Lab/PLTranslationEmpirical </code></pre> </div> <p>We recommend using a virtual environment for running the scripts. Please download <code>conda 23.11.0</code> from this <a href="https://docs.conda.io/projects/miniconda/en/latest/miniconda-other-installer-links.html" rel="nofollow">link</a>. You can create a virtual environment using the following command:</p> <div> <pre><code>conda create -n plempirical python=3.10.13 </code></pre> </div> <p>After creating the virtual environment, you can activate it using the following command:</p> <div> <pre><code>conda activate plempirical </code></pre> </div> <p>You can run the following command to make sure that you are using the correct version of Python:</p> <div> <pre><code>python3 --version && pip3 --version </code></pre> </div> <h3>Dependencies</h3> <p>To install all software dependencies, please execute the following command:</p> <div> <pre><code>pip3 install -r requirements.txt </code></pre> </div> <p>As for hardware dependencies, we used 16 NVIDIA A100 GPUs with 80GBs of memory for inferencing models. The models can be inferenced on any combination of GPUs as long as the reader can properly distribute the model weights across the GPUs. We did not perform weight distribution since we had enough memory (80 GB) per GPU.</p> <p>Moreover, for compiling and testing the generated translations, we used Python 3.10, g++ 11, GCC Clang 14.0, Java 11, Go 1.20, Rust 1.73, and .Net 7.0.14 for Python, C++, C, Java, Go, Rust, and C#, respectively. Overall, we recommend using a machine with Linux OS and at least 32GB of RAM for running the scripts.</p> <p>For running scripts of alternative approaches, you need to make sure you have installed <a href="https://github.com/immunant/c2rust">C2Rust</a>, <a href="https://github.com/gotranspile/cxgo">CxGO</a>, and <a href="https://github.com/paulirwin/JavaToCSharp">Java2C#</a> on your machine. Please refer to their repositories for installation instructions. For Java2C#, you need to create a <code>.csproj</code> file like below:</p> <div> <pre><code><Project Sdk="Microsoft.NET.Sdk"> <PropertyGroup> <OutputType>Exe</OutputType> <TargetFramework>net7.0</TargetFramework> <ImplicitUsings>enable</ImplicitUsings> <Nullable>enable</Nullable> </PropertyGroup> </Project> </code></pre> </div> <h3>Dataset</h3> <p>We uploaded the dataset we used in our empirical study to <a href="../doi/10.5281/zenodo.8190051" rel="nofollow">Zenodo</a>. The dataset is organized as follows:</p> <ol> <li><a href="https://github.com/IBM/Project_CodeNet">CodeNet</a></li> <li><a href="https://github.com/wasiahmad/AVATAR">AVATAR</a></li> <li><a href="https://github.com/evalplus/evalplus">Evalplus</a></li> <li><a href="https://github.com/apache/commons-cli">Apache Commons-CLI</a></li> <li><a href="https://github.com/pallets/click">Click</a></li> </ol> <p>Please download and unzip the <code>dataset.zip</code> file from Zenodo. After unzipping, you should see the following directory structure:</p> <div> <pre><code>PLTranslationEmpirical ├── dataset ├── codenet ├── avatar ├── evalplus ├── real-life-cli ├── ... </code></pre> </div> <p>The structure of each dataset is as follows:</p> <p>1. CodeNet & Avatar: Each directory in these datasets correspond to a source language where each include two directories <code>Code</code> and <code>TestCases</code> for code snippets and test cases, respectively. Each code snippet has an <code>id</code> in the filename, where the <code>id</code> is used as a prefix for test I/O files.</p> <p>2. Evalplus: The source language code snippets follow a similar structure as CodeNet and Avatar. However, as a one time effort, we manually created the test cases in the target Java language inside a maven project, <code>evalplus_java</code>. To evaluate the translations from an LLM, we recommend moving the generated Java code snippets to the <code>src/main/java</code> directory of the maven project and then running the command <code>mvn clean test surefire-report:report -Dmaven.test.failure.ignore=true</code> to compile, test, and generate reports for the translations.</p> <p>3. Real-life Projects: The <code>real-life-cli</code> directory represents two real-life CLI projects from Java and Python. These datasets only contain code snippets as files and no test cases. As mentioned in the paper, the authors manually evaluated the translations for these datasets.</p> <h3>Scripts</h3> <p>We provide bash scripts for reproducing our results in this work. First, we discuss the translation script. For doing translation with a model and dataset, first you need to create a <code>.env</code> file in the repository and add the following:</p> <div> <pre><code>OPENAI_API_KEY=<your openai api key> LLAMA2_AUTH_TOKEN=<your llama2 auth token from huggingface> STARCODER_AUTH_TOKEN=<your starcoder auth token from huggingface> </code></pre> </div> <p>1. Translation with GPT-4: You can run the following command to translate all <code>Python -> Java</code> code snippets in <code>codenet</code> dataset with the <code>GPT-4</code> while top-k sampling is <code>k=50</code>, top-p sampling is <code>p=0.95</code>, and <code>temperature=0.7</code>:</p> <div> <pre><code>bash scripts/translate.sh GPT-4 codenet Python Java 50 0.95 0.7 0 </code></pre> </div> <p>2. Translation with CodeGeeX: Prior to running the script, you need to clone the CodeGeeX repository from <a href="https://github.com/THUDM/CodeGeeX">here</a> and use the instructions from their artifacts to download their model weights. After cloning it inside <code>PLTranslationEmpirical</code> and downloading the model weights, your directory structure should be like the following:</p> <div> <pre><code>PLTranslationEmpirical ├── dataset ├── codenet ├── avatar ├── evalplus ├── real-life-cli ├── CodeGeeX ├── codegeex ├── codegeex_13b.pt # this file is the model weight ├── ... ├── ... </code></pre> </div> <p>You can run the following command to translate all <code>Python -> Java</code> code snippets in <code>codenet</code> dataset with the <code>CodeGeeX</code> while top-k sampling is <code>k=50</code>, top-p sampling is <code>p=0.95</code>, and <code>temperature=0.2</code> on GPU <code>gpu_id=0</code>:</p> <div> <pre><code>bash scripts/translate.sh CodeGeeX codenet Python Java 50 0.95 0.2 0 </code></pre> </div> <p>3. For all other models (StarCoder, CodeGen, LLaMa, TB-Airoboros, TB-Vicuna), you can execute the following command to translate all <code>Python -> Java</code> code snippets in <code>codenet</code> dataset with the <code>StarCoder|CodeGen|LLaMa|TB-Airoboros|TB-Vicuna</code> while top-k sampling is <code>k=50</code>, top-p sampling is <code>p=0.95</code>, and <code>temperature=0.2</code> on GPU <code>gpu_id=0</code>:</p> <div> <pre><code>bash scripts/translate.sh StarCoder codenet Python Java 50 0.95 0.2 0 </code></pre> </div> <p>4. For translating and testing pairs with traditional techniques (i.e., C2Rust, CxGO, Java2C#), you can run the following commands:</p> <div> <pre><code>bash scripts/translate_transpiler.sh codenet C Rust c2rust fix_report bash scripts/translate_transpiler.sh codenet C Go cxgo fix_reports bash scripts/translate_transpiler.sh codenet Java C# java2c# fix_reports bash scripts/translate_transpiler.sh avatar Java C# java2c# fix_reports </code></pre> </div> <p>5. For compile and testing of CodeNet, AVATAR, and Evalplus (Python to Java) translations from GPT-4, and generating fix reports, you can run the following commands:</p> <div> <pre><code>bash scripts/test_avatar.sh Python Java GPT-4 fix_reports 1 bash scripts/test_codenet.sh Python Java GPT-4 fix_reports 1 bash scripts/test_evalplus.sh Python Java GPT-4 fix_reports 1 </code></pre> </div> <p>6. For repairing unsuccessful translations of Java -> Python in CodeNet dataset with GPT-4, you can run the following commands:</p> <div> <pre><code>bash scripts/repair.sh GPT-4 codenet Python Java 50 0.95 0.7 0 1 compile bash scripts/repair.sh GPT-4 codenet Python Java 50 0.95 0.7 0 1 runtime bash scripts/repair.sh GPT-4 codenet Python Java 50 0.95 0.7 0 1 incorrect </code></pre> </div> <p>7. For cleaning translations of open-source LLMs (i.e., StarCoder) in codenet, you can run the following command:</p> <div> <pre><code>bash scripts/clean_generations.sh StarCoder codenet </code></pre> </div> <p>Please note that for the above commands, you can change the dataset and model name to execute the same thing for other datasets and models. Moreover, you can refer to <a href="https://github.com/Intelligent-CAT-Lab/PLTranslationEmpirical/blob/main/prompts/README.md"><code>/prompts</code></a> for different vanilla and repair prompts used in our study.</p> <h3>Artifacts</h3> <p>Please download the <code>artifacts.zip</code> file from our <a href="../doi/10.5281/zenodo.8190051" rel="nofollow">Zenodo</a> repository. We have organized the artifacts as follows:</p> <ol> <li>RQ1 - Translations: This directory contains the translations from all LLMs and for all datasets. We have added an excel file to show a detailed breakdown of the translation results.</li> <li>RQ2 - Manual Labeling: This directory contains an excel file which includes the manual labeling results for all translation bugs.</li> <li>RQ3 - Alternative Approaches: This directory contains the translations from all alternative approaches (i.e., C2Rust, CxGO, Java2C#). We have added an excel file to show a detailed breakdown of the translation results.</li> <li>RQ4 - Mitigating Translation Bugs: This directory contains the fix results of GPT-4, StarCoder, CodeGen, and Llama 2. We have added an excel file to show a detailed breakdown of the fix results.</li> </ol> <h3>Contact</h3> <p>We look forward to hearing your feedback. Please contact <a href="mailto:rangeet.pan@ibm.com">Rangeet Pan</a> or <a href="mailto:alirezai@illinois.edu">Ali Reza Ibrahimzada</a> for any questions or comments 🙏.</p>
Data from figures - PUBLIC AND MEDIA INTEREST IN BED BUGS- EUROPE 2023
<p>Data from Goodle trends and media sources used to generate figures.</p>
Fig. 4 in New species of mirid insects and their importance for the higher classification of plant bugs
Fig. 4. Photographs (A1, A3) and drawings (A2, A4) of femoral trichobothria in mirid insect Metoisops akingbohungbei Herczek and Popov, 2014, holotype male, CEHI BB M HE 4, from the Baltic Amber (unknown locality on Baltic Sea Coast), mid-Eocene. Five mesofemoral trichobothria (A1, A2); six metafemoral trichobothria (A3, A4). Scale bars 0.1 mm.
Fig. 3 in New species of mirid insects and their importance for the higher classification of plant bugs
Fig. 3. Mirid insect Metoisops popovi Kim, Taszakowski, and Jung, sp. nov., holotype female, CNU CNUHHMF005, from the Baltic Amber (unknown locality on Baltic Sea Coast), mid-Eocene. Dorsal habitus. Arrow points to deep incision between calli (A1), lateral habitus (A2), head in dorsal view (A3), head in lateral view (A4), scutellum (A5), hindfemur with trichobothria (A6).
Fig. 2 in New species of mirid insects and their importance for the higher classification of plant bugs
Fig. 2. Mirid insect Metoisops michalskii Kim, Taszakowski, and Herczek sp. nov., holotype male, DZUS HE44-451-1-001, from the Baltic Amber Gdańsk Bay, Poland), mid-Eocene. Dorsal habitus (A1), lateral habitus (A2), head and thorax in lateral view and antennal structure (A3), abdomen and legs in lateral view (A4), hindtarsus (A5), genital segment with parameres (A6). Abbreviations: i, first antennal segment; ii, second antennal segment; iii, third antennal segment; iv, fourth antennal segment; iv-1, first subsegment of fourth antennal segment; iv-2, second subsegment of fourth antennal segment.
Fig. 1 in New species of mirid insects and their importance for the higher classification of plant bugs
Fig. 1. Baltic ambers with specimens of mirid insects. A. Metoisops michalskii Kim, Taszakowski, and Herczek sp. nov., holotype male, DZUS HE44- 451-1-001, from the Baltic Amber (Vistula Spit, Gdańsk Bay, Poland), mid-Eocene. B. Metoisops popovi Kim, Taszakowski, and Jung sp. nov., holotype female, CNU CNUHHMF005, from the Baltic Amber (unknown locality on Baltic Sea Coast), mid-Eocene.
Data and scripts for the publication "A case for open communication of bugs in climate models"
<p>Primary data and scripts for the publication "A case for open communication of bugs in climate models" (submitted to GMDD as EGUSPHERE-2024-3493)</p>
Data and results for the paper: "From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC"
<div> <div>We provide the following files used in the study "From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC".</div> <div>The paper has been accepted for presentation in the research track of the IEEE/ACM International Conference on Software Engineering (ICSE) 2025 and will be included in the conference proceedings.</div> <div>The preprint is available on <a href="http://arxiv.org/abs/2501.15181" target="_blank" rel="noopener">arXiv</a>.</div> <br> <div><strong>User stories e-commerce.xlsx</strong></div> <div>307 real-world user stories from 3 different eCommerce projects.</div> <br> <div><em>Project A</em> defines a complete set of requirements for a B2C focused onlineshop of a publishing house who aims do sell his own publications directly.</div> <div><em>Project B</em> contains a partial set of requirements for a B2C focused onlineshop of a bookseller.</div> <div><em>Project C</em> includes a subset of B2C and B2B requirements for an online bookstore, supplemented by an eProcurement module designed to provide information and automation for industrial customers.</div> <div>Most of the user stories come with additional acceptance criteria, written in unstructured natural language.</div> <br> <div>The user stories have been anonymized and the merchant's real names were replaced with neutral terms.</div> <br> <div>Columns</div> <div>- ID: a unique ID we assigned across all projects</div> <div>- Project: user story belongs to project A, B or C</div> <div>- Connextra: user story in connextra pattern</div> <div>- Acceptance Criteria: acceptance criteria that came with the user story</div> <br> <div><strong>User stories CMS.xlsx</strong></div> <div>34 CMS related user stories from a dataset that was originally created by</div> <div>*Lucassen, G., Dalpiaz, F., van der Werf, J.M.E., Brinkkemper, S.: Visualizing user</div> <div>story requirements at multiple granularity levels via semantic relatedness. In: Con-</div> <div>ceptual Modeling: 35th International Conference, ER 2016, Gifu, Japan, November</div> <div>14-17, 2016, Proceedings 35. pp. 463–478. Springer (2016)*</div> <br> <div>Columns</div> <div>- ID: a unique ID we assigned</div> <div>- Connextra: user story in connextra pattern</div> <br> <div><strong>Issues e-commerce.xlsx</strong></div> <div>54,396 issues, we harvested from seven different issue trackers between June 2011 and July 2024</div> <div>- magento2 (https://github.com/magento/magento2/issues)</div> <div>- nopCommerce (https://github.com/nopSolutions/nopCommerce/issues)</div> <div>- OpenCart (https://github.com/opencart/opencart/issues)</div> <div>- PrestaShop (https://github.com/PrestaShop/PrestaShop/issues)</div> <div>- Shopware5 (https://issues.shopware.com/?products=SW-5)</div> <div>- Shopware6 (https://issues.shopware.com/?products=SW-6)</div> <div>- WooCommerce (https://github.com/woocommerce/woocommerce/issues)</div> <br> <div>Columns</div> <div>- id: unique ID we have assigned</div> <div>- Issue Tracker: issue tracker this issue originates from</div> <div>- Title: title of the original issue</div> <div>- Body: body / description of the original issue</div> <div>- Preprocessed: result of preprocessing the issue as described in the paper</div> <div>- Sample: issue was part of our 3,500 sample issues we used to evaluate CrUISE-AC</div> <br> <div><strong>Issues CMS.xlsx</strong></div> <div>64,500 issues, we harvested from two different issue trackers between April 2002 and August 2024</div> <div>- Moodle (https://github.com/magento/magento2/issues)</div> <div>- Umbraco (https://github.com/nopSolutions/nopCommerce/issues)</div> <br> <div>Columns are the the same as for "Issues e-commerce.xlsx"</div> <br> <div><strong>trivia-trainingdata.csv</strong></div> Manually labelled dataset to train the trivia classifier. <div>The dataset contains 1916 phrases with an even distribution of 958 trivia and 958 non-trivia phrases.</div> <br> <div>- Label = 1: this sentence is trivia</div> <div>- Label = 0: this sentence is not considered trivia</div> <br> <div>Any source code was replaced by [CODE] to simplify the classification process. Source code in markdown could be identified easily as it is enclosed by a special character https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/creating-and-highlighting-code-blocks</div> <br><strong>Prompts</strong><br> <div><em>prompt_match.txt</em>: prompt we used across all LLMs to assess, if an issue potentially might affect a given user story</div> <em>prompt_generate.txt</em>: GPT4-turbo prompt to convert an issue text into gherkin-style acceptance criteria for a given user story<br> <div><em>prompt_evaluate.txt</em>: GPT4-turbo prompt to assess the usefulness of a newly generated acceptance criteria for a given user story</div> <br> <div><strong>Evaluation e-commerce.xlsx</strong></div> issue / user story pairs, generated acceptance criteria and result of manual evaluation.<br> <div> </div> <div>Columns</div> <div>- StoryID: unique ID of the user story (refer to User stories e-commerce.xlsx)</div> <div>- IssueID: unique ID of the issue (refer to Issues e-commerce.xlsx)</div> <div>- Issue: preprocessed issue text used as basis to generate the acceptance criterion</div> <div>- Connextra: user story in connextra pattern</div> <div>- Existing AC: acceptance criteria that originally came with the user story</div> <div>- AC: by CrUISE-AC generated acceptance criterion</div> <div>- AC_Explanation: explanation generated by CrUISE-AC why this AC adds new knowledge to the current user story</div> <div>- E1: evaluation result by expert 1 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E2: evaluation result by expert 2 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E3: evaluation result by expert 3 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E4: evaluation result by expert 4 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- 3/4 majority: did at least 3 experts assess this AC as relevant (1 = yes; 0 = no)</div> <br> <div><strong>Evaluation CMS.xlsx</strong></div> <div>- StoryID: unique ID of the user story (refer to User stories CMS.xlsx)</div> <div>- IssueID: unique ID of the issue (refer to Issues CMS.xlsx)</div> <div>- Issue: preprocessed issue text used as basis to generate the acceptance criterion</div> <div>- Connextra: user story in connextra pattern</div> <div>- AC: by CrUISE-AC generated acceptance criterion</div> <div>- AC_Explanation: explanation generated by CrUISE-AC why this AC adds new knowledge to the current user story</div> <div>- E1: evaluation result by expert 1 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E4: evaluation result by expert 4 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- E5: evaluation result by expert 5 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)</div> <div>- 2/3 majority: did at least 2 experts assess this AC as relevant (1 = yes; 0 = no)</div> </div>
Fig. 1 in Umbrageocoris Kondorosyi: A New Genus And Species Of Big-Eyed Bugs From New Guinea (Heteroptera: Lygaeoidea: Geocoridae)
Fig. 1. Habitus of Umbrageocoris kondorosyi sp. n.: A = dorsal (holotype, HNHM), B = lateral (paratype, BMNH)
Figs 1−4 in Catalogue of type specimens of true bugs (Hemiptera: Heteroptera) deposited in the National Museum, Prague, Czech Republic
Figs 1−4. Type labels of Ludvík Hoberlandt. 1−2 – Velia rhadamantha Hoberlandt, 1941, holotypus, characteristic style of labels in the 1940s; 3−4 – Sigara samani Hoberlandt, 1952, characteristic labels for the 1950s and later period. (Photos: L. Macháčková).
Fig. 14 in Pseudocetherinae (Hemiptera: Reduviidae) revisited: phylogeny and taxonomy of the lobe-headed bugs
Fig. 14. Aedeagus of species of Gerbelius Distant, 1903 and Voconia Stål, 1866. Scale bars = 0.5 mm.
Fig. 11 in Pseudocetherinae (Hemiptera: Reduviidae) revisited: phylogeny and taxonomy of the lobe-headed bugs
Fig. 11. Pygophore of species of Gerbelius Distant, 1903 and Voconia Stål, 1866. Median apical process of Gerbelius typicus Distant, 1903 and V. bakeri sp. nov. damaged/missing. Scale bars = 0.5 mm.
Fig. 8 in Pseudocetherinae (Hemiptera: Reduviidae) revisited: phylogeny and taxonomy of the lobe-headed bugs
Fig. 8. Dorsal habitus of holotypes of Voconia Stål, 1866 species with distribution: purple = Afrotropical region, green = Australasian region, blue = Neotropical region, pink = Oriental region. Scale bars = 3 mm.
Fig. 6 in Pseudocetherinae (Hemiptera: Reduviidae) revisited: phylogeny and taxonomy of the lobe-headed bugs
Fig. 6. Diagnostic characters of Voconia Stål, 1866 shown on Voconia dolichocephala sp. nov. (AMNH_PBI 00168745). A–B. Head, dorsal view; maxillary plates = blue, mandibular plates = green. C. Hemelytra, Cu-An1 cell = red. D. Head and thorax, lateral view; anteriad directed process = purple; E. Labium. F. Pygophore, dorsal view. G. Paramere.
Fig. 7 in Pseudocetherinae (Hemiptera: Reduviidae) revisited: phylogeny and taxonomy of the lobe-headed bugs
Fig. 7. Dorsal habitus of holotypes of Voconia Stål, 1866 species with distribution: purple = Afrotropical region, green = Australasian region, pink = Oriental region. Scale bars = 3 mm.
Fig. 20 in Pseudocetherinae (Hemiptera: Reduviidae) revisited: phylogeny and taxonomy of the lobe-headed bugs
Fig. 20. The distribution of species of Gerbelius Distant, 1903 and Voconia Stål, 1866 in Southeast Asia.
Fig. 2. Tree resulting from implied weighting analysis using K in Pseudocetherinae (Hemiptera: Reduviidae) revisited: phylogeny and taxonomy of the lobe-headed bugs
Fig. 2. Tree resulting from implied weighting analysis using K = 12 with characters unambiguously optimized. Symmetric resampling values over 51 are represented for trees obtained with K = 3, K = 6, K = 9, and K = 12 analyses. Characters and character states are described in pp. 11–22. Specimens not to scale.
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.