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FIGURE 2. A–E in Taxonomic updates on Pachyserica Brenske, 1898 and Serica MacLeay, 1819 reveal 38 new species and new challenges of Sericini systematics regarding DNA barcodes and genus-level diagnostic key characters (Coleoptera: Scarabaeidae: Sericinae)
FIGURE 2. A–E: Serica fansipan new species (holotype); F–K: S. lushui new species (holotype); A, H: aedeagus, left side lateral view; C, K: aedeagus, right side lateral view; B, I: parameres, dorsal view; J: aedeagus, dorsal view; D, F: habitus, dorsal view; E, G: habitus, lateral view. Scale bar: 0.5 mm. Habitus images not to scale.
FIGURE 4. A–F in Taxonomic updates on Pachyserica Brenske, 1898 and Serica MacLeay, 1819 reveal 38 new species and new challenges of Sericini systematics regarding DNA barcodes and genus-level diagnostic key characters (Coleoptera: Scarabaeidae: Sericinae)
FIGURE 4. A–F: Serica guangxiensis new species (holotype); H–M: S. christophreuteri new species (holotype); A, J: aedeagus, left side lateral view; E, M: aedeagus, right side lateral view; C, D, L: parameres, dorsal view; B, K: aedeagus, dorsal view; F, H: habitus, dorsal view; G, I: habitus, lateral view. Scale bar: 0.5 mm. Habitus images not to scale.
Fig. 12 in Intraspecific Variability of Melasis buprestoides (Linnaeus, 1761) Challenges the Validity of Melasis fermini Sánchez-Ruiz and Rosa, 2003 (Coleoptera: Eucnemidae)
Fig. 12. Color forms of males of Melasis buprestoides. a) Bicolored, France (57), Guermange (total length 6.1 mm),
Fig. 11 in Intraspecific Variability of Melasis buprestoides (Linnaeus, 1761) Challenges the Validity of Melasis fermini Sánchez-Ruiz and Rosa, 2003 (Coleoptera: Eucnemidae)
Fig. 11. Neighbor-joining tree using COI-5P DNA barcode of the 24 Melasis species of the dataset dx.doi.org/ 10.5883/DS-MELASIS. Distance model was performed with Kimura two-parameter and Isorhipis marmottani was used for genetic distance comparisons. Both coloration patterns (bicolored and unicolored) and BOLD sample-ID of each Melasis individual is referenced for clarity.
Fig. 10 in Intraspecific Variability of Melasis buprestoides (Linnaeus, 1761) Challenges the Validity of Melasis fermini Sánchez-Ruiz and Rosa, 2003 (Coleoptera: Eucnemidae)
Fig. 10. Relationship between elytral length/pronotal length and total body length. Black circles and red (gray in print version) squares represent unicolored and bicolored Melasis, respectively. Triangles refer to M. fermini types.
Fig. 9 in Intraspecific Variability of Melasis buprestoides (Linnaeus, 1761) Challenges the Validity of Melasis fermini Sánchez-Ruiz and Rosa, 2003 (Coleoptera: Eucnemidae)
Fig. 9. Relationship between the elytral length/width ratio and total body length. Black circles and red (gray in print version) squares represent unicolored and bicolored Melasis, respectively. Triangles refer to M. fermini types.
Fig. 3 in Intraspecific Variability of Melasis buprestoides (Linnaeus, 1761) Challenges the Validity of Melasis fermini Sánchez-Ruiz and Rosa, 2003 (Coleoptera: Eucnemidae)
Fig. 3. Elytral (a) and pronotal (b) width measurements of studied Melasis. Photo credit: P. Zagatti.
Fig. 6 in Intraspecific Variability of Melasis buprestoides (Linnaeus, 1761) Challenges the Validity of Melasis fermini Sánchez-Ruiz and Rosa, 2003 (Coleoptera: Eucnemidae)
Fig. 6. Color probability of a Melasis individual with respect to its size. Gray dots represent the number of individuals for each size. The darker the dot, the higher the number of individuals. a) Probability of being unicolored (curve), b) Probability of being bicolored (curve).
Fig. 5 in Intraspecific Variability of Melasis buprestoides (Linnaeus, 1761) Challenges the Validity of Melasis fermini Sánchez-Ruiz and Rosa, 2003 (Coleoptera: Eucnemidae)
Fig. 5. Total length of studied Melasis grouped by coloration and sex. Dots refer to Melasis specimens examined in this study and triangles refer to the photographed M. fermini types.
Fig. 2 in Intraspecific Variability of Melasis buprestoides (Linnaeus, 1761) Challenges the Validity of Melasis fermini Sánchez-Ruiz and Rosa, 2003 (Coleoptera: Eucnemidae)
Fig. 2. Elytral (a) and pronotal (b) length measurements of studied Melasis. Photo credit: P. Zagatti.
Fig. 1 in Intraspecific Variability of Melasis buprestoides (Linnaeus, 1761) Challenges the Validity of Melasis fermini Sánchez-Ruiz and Rosa, 2003 (Coleoptera: Eucnemidae)
Fig. 1. Photographs of Melasis fermini holotype and paratypes. a) Holotype (male), dorsal habitus (abdomen removed, total length 4.25 mm), antenna, and labels, b) Paratype no. 191659 (male), dorsal habitus (total length 4.19 mm), antenna, and labels, c) Paratype no. 191660 (female), dorsal habitus (total length 4.75 mm), antenna, and labels. Photo credits: M. Paris.
Figure 4 in The introduction of three cryptic tree frog species in the Dutch coastal dunes challenges conservation paradigms
Figure 4. Majority rule consensus phylogenetic tree resulting from Bayesian inference for Hyla orientalis haplotypes. Grey branches indicate the backbone phylogeny with representatives for each Western Palearctic Hyla species; the relatively distinct H. meridionalis and H. carthaginiensis were used as outgroup and are not shown. The pink haplotype is newly identified in the Netherlands; black haplotypes have not been reported in the Netherlands. Haplotype labels correspond to supplementary table S1.
Figure 2 in The introduction of three cryptic tree frog species in the Dutch coastal dunes challenges conservation paradigms
Figure 2. Majority rule consensus tree resulting from Bayesian inference to allocate new Hyla haplotypes to species. Grey branches indicate the backbone phylogeny with representatives for each Western Palearctic Hyla species; the relatively distinct H. meridionalis and H. carthaginiensis were used as outgroup and are not shown. New haplotypes are coloured blue (H. arborea) or pink (H. orientalis). Haplotype labels correspond to supplementary table S1.
Figure 1 in The introduction of three cryptic tree frog species in the Dutch coastal dunes challenges conservation paradigms
Figure 1. Map of the Netherlands showing sampled localities for Hyla tree frogs. A rough outline of the natural tree frog distribution range is shaded grey. Pies are sampled localities. Pie slices are coloured according to haplotype and pie sizes reflect sample sizes. Previously identified haplotypes are labelled 'old' and those newly identified in this study 'new'. The five populations introduced in the coastal dunes are labelled with the (approximate) date of appearance. Sampling details are in supplementary table S1.
Figure 3 in The introduction of three cryptic tree frog species in the Dutch coastal dunes challenges conservation paradigms
Figure 3. Majority rule consensus phylogenetic tree resulting from Bayesian inference for Hyla arborea haplotypes. Grey branches indicate the backbone phylogeny with representatives for each Western Palearctic Hyla species; the relatively distinct H. meridionalis and H. carthaginiensis were used as outgroup and are not shown. Blue haplotypes are newly identified and orange haplotypes were previously reported haplotypes in the Netherlands; black haplotypes have not been reported in the Netherlands. Haplotype labels correspond to supplementary table S1.
Perceptions, experiences and challenges of physical activity among firefighters with coronary heart disease risk factors in the City of Cape Town Fire and Rescue Services
Open the record for dataset details and reuse information.
A Developer-Centric Study Exploring Mobile Application Security Practices and Challenges
<p>This is the dataset for the paper: <strong>A Developer-Centric Study Exploring Mobile Application Security Practices and Challenges</strong></p> <p>This paper was accepted for publication at the International Conference on Software Maintenance and Evolution (ICSME 2024) - Industry Track</p> <p>Preprint: <a href="https://arxiv.org/abs/2408.09032">https://arxiv.org/abs/2408.09032</a></p> <p> </p> <h2><strong>Artifacts</strong></h2> <p>The table below describes the artifacts associated with this study.</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Purpose</strong></td> </tr> <tr> <td>SurveyResponse.csv</td> <td>The results of the online survey. This file contains only responses from participants who completed all required survey questions.</td> </tr> <tr> <td>SurveyQuestions.pdf & SurveyQuestions.tex</td> <td>These files contain the survey questions in the form they were presented to participants. They are in PDF and LaTeX format.</td> </tr> <tr> <td>ParticipantLocation.xlsx </td> <td>This file shows the location (based on Qualtircs metadata) of participants who responded to the survey.</td> </tr> <tr> <td>IRB_Approval.pdf</td> <td>This is the IRB approval document.</td> </tr> <tr> <td>freetext-responses-categorized.xlsx</td> <td>This file is related to survey questions #20, #22, and #23. It is a manual categorization of free-text responses by the authors. </td> </tr> <tr> <td>likert-figures\*.pdf</td> <td>The files in this directory are the Likert figures used in the paper.</td> </tr> <tr> <td>multichoice-questions\*.csv</td> <td>The files in this directory show the frequency of occurrence for the answer options associated with single-choice and multi-choice questions.</td> </tr> <tr> <td>code\</td> <td>Python code to generate the Likert charts and the CSV files that show the frequency of occurrence for the answer options.</td> </tr> <tr> <td>LICENSE.txt</td> <td>Open-source license compliant with OSI.</td> </tr> </tbody> </table> <h2>Code</h2> <p>The code is written in Python. Below is a description of the files:</p> <ol> <li>requirements.txt - Required packages</li> <li>MultiChoiceAnalysis.py - This file contains the logic to analyze the multichoice questions to generate the frequency of occurrence of the answer options. The CSV files are created in the parent directory</li> <li>main-mobileSecurity.py - This file reads SurveyResponse.csv (in the parent directory) and has the code to generate the likert charts and calls MultiChoiceAnalysis.py. The Likert charts are created in the parent directory.</li> </ol> <p>Perform the below steps to execute the code:</p> <ol> <li> Create a Python virtual environment: <blockquote> python -m venv venv</blockquote> </li> <li>Activate the virtual environment: <blockquote> venv\Scripts\activate</blockquote> </li> <li>Install packages <blockquote> pip install -r requirements.txt</blockquote> </li> <li>Run the Python code: <blockquote> python main-mobileSecurity.py</blockquote> </li> </ol> <p>Below are details about the code execution output:</p> <ul> <li>The CSV and PDF files will be created in the parent directory.</li> <li>The Likert charts (i.e., PDF files) are created in a directory called "likert-figures"</li> <li>The CSV files are created in a directory called "multichoice-questions"</li> <li>Elements from the CSV files are part of the write-up in the Results section of the manuscript. The numeric digits that are part of the file name correspond to the survey question.</li> <li>The data present in Tables 2 to 5 in the manuscript is obtained from the CSV files generated by the Python code. Specifically: <ul> <li>Table 2 - \multichoice-questions\survey-11-frequency_single_choice.csv</li> <li>Table 3 - \multichoice-questions\survey-12-frequency_single_choice.csv</li> <li>Table 4 - \multichoice-questions\survey-13-frequency_single_choice.csv</li> <li>Table 5 - \multichoice-questions\survey-14-frequency_single_choice.csv and \multichoice-questions\survey-15-frequency_single_choice.csv</li> </ul> </li> </ul> <p><em>Note: We include these output files in the artifacts package by default. The user can delete these directories and run the code to regenerate the files.</em></p>
Exploring Accessibility Trends and Challenges in Mobile App Development: A Study of Stack Overflow Questions
<p>This is the dataset for the paper: Exploring Accessibility Trends and Challenges in Mobile App Development: A Study of Stack Overflow Questions</p> <p>This paper was accepted for publication at the 58th Hawaii International Conference on System Sciences (HICSS) - Software Technology Track</p> <p>Preprint: <a href="https://arxiv.org/abs/2409.07945">https://arxiv.org/abs/2409.07945</a></p>
NeurOST-SSH-SST Maps for Ocean Data Challenge 2023a_SSH_mapping_OSE
<p>Global maps of sea surface height (SSH) and surface geostrophic currents generated using NeurOST, a deep learning for mapping SSH from nadir satellite altimetry and sea surface temperature, generated for the observing system experiment outlined in the Ocean Data Challenge '2023a_SSH_mapping_OSE'.</p> <p>Ocean Data Challenge link: https://github.com/ocean-data-challenges/2023a_SSH_mapping_OSE/tree/main </p> <p>These maps were made using L3 SSH + L4 SST.</p> <p>NeurOST citations:</p> <ul> <li>Martin, S. A., Manucharyan, G. E., and Klein, P. (2024). Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics. Geophysical Research Letters, 51, e2024GL110059. https://doi.org/10.1029/2024GL110059</li> <li>Martin, S. A., Manucharyan, G. E., and Klein, P. (2023). Synthesizing Sea Surface Temperature and Satellite Altimetry Observations Using Deep Learning Improves the Accuracy and Resolution of Gridded Sea Surface Height Anomalies. Journal of Advances in Modeling Earth Systems, 15, e2022MS003589. https://doi.org/10.1029/2022MS003589</li> </ul>
International Symposium on Achievements, Milestones, and Challenges in Biomedical and Health Informatics data
<p><strong>International Symposium on Achievements, Milestones, and Challenges in Biomedical and Health Informatics (BMHI), held in Athens, Greece, on 29 October 2022 - fatabse with paper titles, abstratcs, keywords and sections (with sub-sections)</strong></p>
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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.