Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,567
datasets available to search
ShareScore release 0.9.0
Dataset results
2,567 results for “bugs”
FIGURES 5 – 10 in First record of the thread-legged assassin bug genus Proguithera from Japan, with description of a new species (Hemiptera: Heteroptera: Reduviidae)
FIGURES 5 – 10. Proguithera kiinugama sp. nov. 5 – 6, Head and prothorax, male, dorsal (5) and lateral (6) views; 7 – 10, head, male (7 – 8) and female (9 – 10), dorsal (7, 9) and lateral (8, 10) views. Scale bar: 0.5 mm.
FIGURE 5 A – L in Scratching the surface? Taxonomic revision of the subgenus Schizoptera (Odontorhagus) reveals vast undocumented biodiversity in the largest litter bug genus Schizoptera Fieber (Hemiptera: Dipsocoromorpha)
FIGURE 5 A – L. Line drawings of dorsal (above) and ventral (below) views of male genitalia, different colors correspond to different structures labeled on S. (Odonotorhagus) acuta, n. sp.
FIGURE 6 M – W in Scratching the surface? Taxonomic revision of the subgenus Schizoptera (Odontorhagus) reveals vast undocumented biodiversity in the largest litter bug genus Schizoptera Fieber (Hemiptera: Dipsocoromorpha)
FIGURE 6 M – W. Line drawings of dorsal (above) and ventral (below) views of male genitalia, different colors correspond to different structures labeled on S. (Odonotorhagus) acuta, n. sp. (Fig. 5 A).
Tomcat bug-report
<p>About the Data</p> <p>This dataset is one of the Datasets donated by An Ngoc Lam.</p> <p>Overview of Data</p> <p>The data is present in 2 files:</p> <p>“Tomcat.xlsx” : A spreadsheet with the bug-ids, commits, its summary, files etc.</p> <p>“Tomcat.xml” : An xml file with more detailed information than the above spreadsheet(detailed files changed).</p> <p>Attribute Information</p> <p>The spreadsheet contains a table with the “bug_id”, “summary”, “description”, “time_reported”, “commit associated”, “status of commit” and “files committed”.</p> <p>The xml contains the above information and additionally the lines associated with the commit.</p> <p>Paper Abstract</p> <p>Bug localization refers to the automated process of locating the potential buggy files for a given bug report. To help developers focus their attention to those files is crucial. Several existing automated approaches for bug localization from a bug report face a key challenge, called lexical mismatch, in which the terms used in bug reports to describe a bug are different from the terms and code tokens used in source files. This paper presents a novel approach that uses deep neural network (DNN) in combination with rVSM, an information retrieval (IR) technique. rVSM collects the feature on the textual similarity between bug reports and source files. DNN is used to learn to relate the terms in bug reports to potentially different code tokens and terms in source files and documentation if they appear frequently enough in the pairs of reports and buggy files. Our empirical evaluation on real-world projects shows that DNN and IR complement well to each other to achieve higher bug localization accuracy than individual models. Importantly, our new model, HyLoc, with a combination of the features built from DNN, rVSM, and project’s bug-fixing history, achieves higher accuracy than the state-of-the-art IR and machine learning techniques. In half of the cases, it is correct with just a single suggested file. Two out of three cases, a correct buggy file is in the list of three suggested files.</p>
How Developers Locate Performance Bugs — Supplementary Material
<p><strong>Abstract:</strong></p> <p><em>Background:</em> Performance bugs can lead to severe issues regarding computation efficiency, power consumption, and user experience. Locating these bugs is a difficult task because developers have to judge for every costly operation whether runtime is consumed necessarily or unnecessarily. Objective: We wanted to investigate how developers, when locating performance bugs, navigate through the code, understand the program, and communicate the detected issues.</p> <p><em>Method:</em> We performed a qualitative user study observing twelve developers trying to fix documented performance bugs in two open source projects. The developers worked with a profiling and analysis tool that visually depicts runtime information in a list representation and embedded into the source code view.</p> <p><em>Results:</em> We identified typical navigation strategies developers used for pinpointing the bug, for instance, following method calls based on runtime consumption. The integration of visualization and code helped developers to understand the bug. Sketches visualizing data structures and algorithms turned out to be valuable for externalizing and communicating the comprehension process for complex bugs.</p> <p><em>Conclusion:</em> Fixing a performance bug is a code comprehension and navigation problem. Flexible navigation features based on executed methods and a close integration of source code and performance information support the process.</p> <p><strong>Dataset:</strong></p> <ol> <li> <p><strong>Tutorial:</strong> We provide the slides (PDF) and the video (MP4) we used in the tutorial phase of our study.</p> </li> <li> <p><strong>Locating Bugs:</strong> We also provide supplementary material for each research question. We provide the advices we prepared for each bug in case a team got stuck (PDF); the questions we asked after each bug fixing session can be found on the introduction slides (PDF).</p> <ul> <li> <p><strong>RQ1:</strong> Navigating and Understanding</p> <ul> <li> <p><strong>RQ1.1:</strong> <em>How was information from the profiling tool or other parts of the IDE used to locate the performance bug?</em> Cross-case analysis (in German) (XLSX+ODS)</p> </li> <li> <p><strong>RQ1.2:</strong> <em>Is the in-situ visualization of the profiling data beneficial compared to a traditional list representation?</em> Cross-case analysis (in German) (XLSX+ODS)</p> </li> <li> <p><strong>RQ1.3:</strong> <em>What navigation strategies do developers pursue to locate a specific performance bug?</em> Interaction logs (TXT), Navigation visualizations (SVG), Screen recordings for Bug 3 (MP4, without audio because of confidentiality)</p> </li> </ul> </li> <li> <p><strong>RQ2:</strong> Understanding and Communicating</p> <ul> <li> <p><strong>RQ2.1:</strong> <em>How do developers communicate with each other when locating a performance bug?</em> Coding (XLSX+ODS), Sketches (PDF), Screen recordings for Bug 3 (MP4, without audio because of confidentiality)</p> </li> <li> <p><strong>RQ2.2:</strong> <em>Could sketches help to understand and communicate a performance bug?</em> Coding (XLSX+ODS), Sketches (PDF), Cross-case analysis (in German) (XLSX+ODS), Sketching videos for Bug 3 (MP4, without audio because of confidentiality)</p> </li> </ul> </li> </ul> </li> <li> <p><strong>Questionnaire:</strong> The questionnaire that the participants filled out at the end of the study can be found here (PDF).</p> </li> </ol>
Data from: Exploring hymenopteran parasitoid communities and their hosts: A comparative study of farmland and semi-natural ecotones with focus on pentatomoid bugs and their antagonists
<p>Here, we provide abundance data, the respective R-script and R-readable data files from a small-scale study on hymenopteran parasitoid communities and their hosts, with special focus on pentatomoid bugs and their egg parasitoids. This comparative study was conducted in farmland and semi-natural ecotones from June to September 2020 in South Tyrol, Italy. The fauna was sampled during four sampling events with sweep netting, beat netting, yellow pan traps, Malaise traps and visual inspections. Arthropods were identified to order level, hymenopteran parasitoids to family level, pentatomoid bugs to species level and egg parasitoids of pentatomoids as far as possible to species level. If identification was not possible, pentatomoid parasitoids were left at genus level. The abundance data collected with each survey method were pooled per survey events and survey site.</p>
Fig. 15 in Taxonomic revision of the African assassin bug genus Fusius (Heteroptera: Reduviidae: Peiratinae)
Fig. 15. Male genitalia of Fusius rubricosus (Stål, 1855), non-type specimen from South Africa (NHMUK). A–C. Pygophore. D. Left paramere. E. Right paramere. F–I. Phallus. A, G. Ventral view. B. Caudal view. C, H–I. Lateral view. D–E. Outer ventrolateral view. F. Dorsal view. Scale bar: A–E = 1.00 mm; F–I = 0.85 mm.
Fig. 13. Fusius ugandensis Miller, 1957 in Taxonomic revision of the African assassin bug genus Fusius (Heteroptera: Reduviidae: Peiratinae)
Fig. 13. Fusius ugandensis Miller, 1957, holotype, ♂ (NHMUK 013586528), habitus. A. Dorsal view. B. Ventral view. C. Lateral view. Scale bar = 2.00 mm.
Fig. 8. Fusius sylvestris Miller, 1957 in Taxonomic revision of the African assassin bug genus Fusius (Heteroptera: Reduviidae: Peiratinae)
Fig. 8. Fusius sylvestris Miller, 1957, holotype, ♂ (NHMUK 013586556), habitus. A. Dorsal view. B. Ventral view. C. Lateral view. Scale bar = 2.00 mm.
Fig. 6. Fusius distinctus Miller, 1957 in Taxonomic revision of the African assassin bug genus Fusius (Heteroptera: Reduviidae: Peiratinae)
Fig. 6. Fusius distinctus Miller, 1957, holotype, ♂ (NHMUK 013586553), habitus. A. Dorsal view. B. Ventral view. C. Lateral view. Scale bar = 2.00 mm.
Fig. 9 in Taxonomic revision of the African assassin bug genus Fusius (Heteroptera: Reduviidae: Peiratinae)
Fig. 9. Male genitalia of Fusius distinctus Miller, 1957, non-type specimen from DR Congo (NHMUK). A–C. Pygophore. D. Left paramere. E. Right paramere. F–I. Phallus. A, G. Ventral view. B. Caudal view. C, H–I. Lateral view. D–E. Outer ventrolateral view. F. Dorsal view. Scale bar: A–E = 1.00 mm; F–I = 0.85 mm.
Fig. 5 in Taxonomic revision of the African assassin bug genus Fusius (Heteroptera: Reduviidae: Peiratinae)
Fig. 5. Male genitalia of Fusius dilutus Miller, 1957, non-type specimen from Nigeria (NHMUK). A–C. Pygophore. D. Left paramere. E. Right paramere. F–I. Phallus. A, G. Ventral view. B. Caudal view. C, H, I. Lateral view. D, E. Outer ventrolateral view. F. Dorsal view. Scale bar: A–E = 1.00 mm; F–I = 0.90 mm.
Fig. 4. Fusius liberiensis Miller, 1957 in Taxonomic revision of the African assassin bug genus Fusius (Heteroptera: Reduviidae: Peiratinae)
Fig. 4. Fusius liberiensis Miller, 1957, holotype, ♂ (NHMUK 013586567), habitus. A. Dorsal view. B. Ventral view. C. Lateral view. Scale bar = 2.00 mm.
Fig. 11. Fusius hargreavesi Miller, 1957 in Taxonomic revision of the African assassin bug genus Fusius (Heteroptera: Reduviidae: Peiratinae)
Fig. 11. Fusius hargreavesi Miller, 1957, holotype, ♂ (NHMUK 013586528), habitus. A. Dorsal view. B. Ventral view. C. Lateral view. Scale bar: = 2.00 mm.
Fig. 2. Fusius dilutus Miller, 1957 in Taxonomic revision of the African assassin bug genus Fusius (Heteroptera: Reduviidae: Peiratinae)
Fig. 2. Fusius dilutus Miller, 1957, paratype, ♀ (NHMUK 013586562), habitus. A. Dorsal view. B. Ventral view. C. Lateral view. Scale bar = 2.00 mm.
Fig. 12 in Taxonomic revision of the African assassin bug genus Fusius (Heteroptera: Reduviidae: Peiratinae)
Fig. 12. Fusius rubricosus (Stål, 1855), lectotype, ♂ (NHRS-GULI 000000133), habitus. A. Dorsal view. B. Ventral view. C. Lateral view. Scale bar = 2.00 mm.
Fig. 1. Fusius dilutus Miller, 1957 in Taxonomic revision of the African assassin bug genus Fusius (Heteroptera: Reduviidae: Peiratinae)
Fig. 1. Fusius dilutus Miller, 1957, holotype, ♂ (NHMUK 013586560) habitus. A. Dorsal view. B. Ventral view. C. Lateral view. Scale bar = 2.00 mm.
Fig. 10 in Taxonomic revision of the African assassin bug genus Fusius (Heteroptera: Reduviidae: Peiratinae)
Fig. 10. Fusius hflavus (Reuter, 1881) stat. rev. & comb. nov., lectotype, ♂ (NHRS-GULI 00000125), habitus. A. Dorsal view. B. Ventral view. C. Lateral view. Scale bar = 2.00 mm.
Fig. 3. Fusius gowdeyi Miller, 1957 in Taxonomic revision of the African assassin bug genus Fusius (Heteroptera: Reduviidae: Peiratinae)
Fig. 3. Fusius gowdeyi Miller, 1957, holotype, ♂ (NHMUK 013586565), habitus. A. Dorsal view. B. Ventral view. C. Lateral view. Scale bar = 2.00 mm.
Fig. 7. Fusius distinctus Miller, 1957 in Taxonomic revision of the African assassin bug genus Fusius (Heteroptera: Reduviidae: Peiratinae)
Fig. 7. Fusius distinctus Miller, 1957, paratype, ♀ (NHMUK 013586555), habitus. A. Dorsal view. B. Ventral view. C. Lateral view. Scale bar = 2.00 mm.
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.