Find research datasets worth reusing

FIGURE 5. a – d in Bathyal Mollusca from the cold-water coral biotope of Santa Maria di Leuca (Apulian margin, southern Italy)

FIGURE 5. a – d: Delectopecten vitreus (Gmelin, 1791); RV (a, c, d) and LV (b), sample BC 72, scale bars: 5 mm (a – b), 1 mm (c, juvenile), 0.1 mm (d, prodissoconch); e – g: Parvamussium fenestratum (Forbes, 1844); LV, samples BC 70 (e – f) and BC 72 (g), scale bars: 5 mm (e – f), 0.1 mm (g, prodissoconch); h – l: Cyclopecten hoskynsi (Forbes, 1844); RV (h, i, l) and LV (j, k), sample BC 72, scale bars: 5 mm (h – k), 0.1 mm (l, prodissoconch); m – o: Similipecten similis (Laskey, 1811); RV (n, o) and LV (m), sample BC 72, scale bars: 1 mm (m – n), 0.05 mm (o, prodissoconch).

FIGURE 5 in Diaphorodoris alba Portmann & Sandmeier, 1960 is a valid species: molecular and morphological comparison with D. luteocincta (M. Sars, 1870) (Gastropoda: Nudibranchia)

FIGURE 5. The tree portrays the phylogenetic relationships based on the H 3 + COI + 16 S combined dataset. Numbers at nodes are Bayesian posterior probability (pp., left) and ML bootstrap support (bs., right), respectively.

FIGURE 2 in Diaphorodoris alba Portmann & Sandmeier, 1960 is a valid species: molecular and morphological comparison with D. luteocincta (M. Sars, 1870) (Gastropoda: Nudibranchia)

FIGURE 2. SEM images of the buccal apparatus from Diaphorodoris alba (A, C, E) and D. luteocincta (B, D, F) specimens at different magnification levels.

FIGURE 27. Psolidium acorbulum Thandar, 2006. A – D in Sublittoral and bathyal sea cucumbers (Echinodermata: Holothuroidea) from the Northern Mozambique Channel with description of six new species

FIGURE 27. Psolidium acorbulum Thandar, 2006. A – D: SEM photos of ossicles from dorsal body wall (A – C) and ventral body wall (D). Scale bars: A, B = 100 µm; C = 10 µm; D = 20 µm.

FIGURES 15 – 18. D in Duocrassana longula, new genus and species of leafhopper (Hemiptera: Cicadellidae: Deltocephalinae) from southern Mexico and its relationship to other genera in Athysanini

FIGURES 15 – 18. D. longula gen. and sp. nov. 15. Male, dorsal view; 16. Female, dorsal habitus; 17 a. Female, face details (in INHS); 17 b. Female, face details (in CZUG); 18. Female, lateral habitus. Scale bar 1 mm.

opencc-by-4.0Dec 2016View details →

Fig. 3. A–D in Seven scuticociliates (Protozoa, Ciliophora) from Alabama, USA, with descriptions of two parasitic species isolated from a freshwater mussel Potamilus purpuratus

Fig. 3. A–D. Pseudocohnilembus hargisi Evans & Thompson, 1964. E–J. Parauronema cf. longum Song, 1995. A–C, E–I. In vivo. D, J. After silver impregnation. A, E. Ventral views of typical individuals, arrow in (A) shows caudal cilia. B–C, F–G. Different individuals, showing varying body shapes, arrowheads in (F) mark somatic kineties. D, J. Detailed structure of the buccal area. H. Ventral view, arrow refers to dumbbell-shaped crystals. I. Posterior end, arrow marks caudal cilium. Abbreviations: M1, 2, 3 = membranelle 1, 2 and 3; PM = paroral membrane; Sc = scutica. Scale bars: A–B = 15 μm; C, E = 40 μm; G = 60 μm.

opencc-by-4.0Dec 2016View details →

Fig. 2. A–D in Seven scuticociliates (Protozoa, Ciliophora) from Alabama, USA, with descriptions of two parasitic species isolated from a freshwater mussel Potamilus purpuratus

Fig. 2. A–D. Mesanophrys cf. carcini Small & Lynn in Aescht, 2001. E–G. Metanophrys similis Song et al., 2002. A–C, E–F. In vivo. D, G. After silver impregnation. A, E. Ventral views of typical individuals, arrow in (E) shows caudal cilia. B, F. Different individuals, showing different body shapes. C. Individual in morphogenesis. D, G. Detailed structure of the buccal area. Abbreviations: M1, 2, 3 = membranelle 1, 2 and 3; Ma = macronucleus; PM = paroral membrane. Scale bars: 30 μm.

opencc-by-3.0Dec 2016View details →

Fig. 4. SEM micrographs. — A–D. M in New species of Macunahyphes Dias, Salles & Molineri (Ephemeroptera: Leptohyphidae), with taxonomic notes

Fig. 4. SEM micrographs. — A–D. M. araca sp. nov. A. Male genitalia (ventral view). B. Male genitalia (lateral view). C. Egg (general aspect). D. Egg showing the micropylar area. — E. M. eduardoi Almeida & Mariano, 2015, egg (general aspect).

opencc-by-4.0Dec 1840View details →

AE.Genre AESCHNA.- A. GenreANAX. C. Genre CALEPTERIX 1. Genre LESTES. - L. Genre LIB E L LULA. (partie.) in pp 56 - 66, plates 1-4 in Monographie des Libellulidées d'Europe

AE.Genre AESCHNA.- A. GenreANAX. C. Genre CALEPTERIX 1. Genre LESTES. - L. Genre LIB E L LULA. (partie.)

opencc-by-4.0Mar 2017View details →

Data supplementing the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.

These data supplement the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175. Use is free for academic purposes, provided the aforementioned article is appropriately cited. The directory contains the following files stimuli.tar.gz - stimuli used in this study; note that this is based on the MONS database, but some deviations from the final version of the database do exist. ratings.mat contains the variables arousal - mean arousal rating valence - mean valence rating valence2 - squared mean valence rating (after subtracting midpoint) motivationalValue - mean motivation rating motivaionalValue2 - squared mean motivation rating (after subtracting midpoint) All variables are 104x3, where the first dimension is the stimulus number, and the second dimension the motivation ground truth (aversive, neutral, appetitive) Experiment 1 fixationsExperiment1.mat contains the variables fixationX, fixationY, fixationDuration, fixaitonOnset, fixationInitial, which contain for each fixation horizontal and vertical coordinate, the duration, the time of the onset relative to the trial onset and whether it is the initial fixation. All variables have dimensions 16x104x3x50, where the first dimension is the observer, the second the scene, the third the condition and the forth a counter of fixations. Whenever there are less than 50 fixations the remainder are filled with NaN. boundingBoxesExperiment1.mat contains for each critical object the bounding box coordinates x,y of upper left corner and width and height as variables boundingBoxX, boundingBoxY, boundingBoxW, boundingBoxH respectively. Note that this is relative to the eyetracker coordinates of experiment 1 (full display 1024x768, presentation in the center) and will therefore not match the coordinates of the images in the archive or the bounding box coordinates of experiment 2. Dimensions are 104x3, the dimensions representing scene number and condition, respectively. figure2.m uses these data to computes figure 2 of the article from these data dataForExperiment1.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of table 1. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object. table1.R computes and prints the models for table 1 Experiment 2 fixationsExperiment2.mat contains fixation data for experiment 2. Variable names as in experiment 1. Dimensions are 18x99x3x3x50, where the first dimension is the observer, the second the image number, the third the visual condition, the third the motivational condition and the fifth the fixation count. Since only one visual condition was shown to each observer per motivational condition, there is an additional variable 'hasData', which is 1 if the image was presented to the observer in this condition and 0 otherwise. Since fixations can be outside the image and will therefore be excluded, there is also an additional variable fixationNumber to keep a correct count of the fixation number in the trial. boundingBoxesExperiment2.mat contains bounding box data for experiment 2 in image (and fixation) coordinates. Notation as for experiment 1, but coordinates refer to image and eyetracking coordinates used for experiment 2 and therefore can differ occasionally. figure3and4.m generates figures 3 and 4 of the article from these data files. dataForExperiment2.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of tables 2 amd 3. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object. The fields imgMot and imgVis contain the motivational ground truth and the salience manipulation, respectively. table2.R uses the Rdata file to compute the models for table 2 of the article and print summary results table3.R uses the Rdata file to compute the models for table 3 of the article and print summary results. Note that the computation can take substantial time; results might deviate slightly depending on the exact version of R and its libraries used.

opencc-by-4.0Mar 2017View details →

Caractéristiques d'un entrepôt selon Re3data - version française

Version française du diagramme sur les caractéristiques d'une entrepôt par le Re3data (http://dx.doi.org/10.1371/journal.pone.0078080.g001).

opencc-by-4.0Jan 2015View details →

Dataset supplementing Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. Annals of the New York Academy of Sciences. 1339, 138-153.

Data supplementing the paper Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. Annals of the New York Academy of Sciences. 1339, 138-153. doi: 10.1111/nyas.12575 The files can be freely used for scientific purposes, provided this reference is appropriately cited. Files contain the behavioral data, the model can be found at https://doi.org/10.5281/zenodo.573026 The following files are contained in this folder: dataExp1.mat contains the data of experiment 1 The variables durationLeft and durationRight contain 5 x 6 x 6 cell arrays with the dominance durations for the left and right grating, respectively. Dimensions are subject x contrast level left x contrast level right. dataExp2.mat contains the data of experiment 2 Variables buttonStart, buttonEnd and whichButton contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain the start time and end time of each button press, and which button (1/2) was pressed, respectively. Variables presStart and presEnd contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain start and end of each blank period. All time stamps refer to the onset of the first blanking trial (end of continuous presentation) Variable prevPerz contains the percept (button) that was pressed at the end of the continuous presentation period. figure3_human.m, figure4_human.m and figure6_human.m exemplify the usage of the data by re-plotting the figures containing human data of the aforementioned paper

opencc-by-4.0May 2017View details →

Adult Wing images from Drosophila sechellia and D. simulans

These are images of adult wings from two Drosophila species that were dissected by Christian Marier (in the Dworkin lab) and imaged by Ian Dworkin in 2015. Strains were provided by the Wittkopp lab at the University of Michigan. Flies were reared in the Wittkopp lab by Richard Lusk. Adults were stored in 70% ethanol prior to dissection and mounting in 70% glycerol. All images were taken on an Olympus BX-51 microscope on a 4X objective (40X total magnification) and captured with a DP30BW digital camera using Olympus DP controller (V.3,1,1208) software. All images were collected for geometric morphometric analysis. Dsim = Drosophila simulans Dsech = Drosophila sechellia f VS M is for male and female.

opencc-by-4.0Jun 2017View details →

PONE-D-17-12804R1 (TRACMIT) dataset for Figures 3D, 4A, 4B, 4C

Dataset for Figures 3D, 4A, 4B and 4C of the manuscript TRACMIT: an effective pipeline for tracking and analyzing cells on micropatterns through mitosis PONE-D-17-12804R1

opencc-by-4.0Oct 2024View details →

Mobilise-D Technical Validation Study (TVS) dataset

<h1>Mobilise-D Technical Validation Study (TVS) Dataset</h1> This dataset was recorded as part of the Mobilise-D project, a comprehensive initiative aimed at developing and validating digital solutions for assessing mobility in real-world environments. The Mobilise-D project seeks to address the critical need for accurate, reliable, and scalable tools to monitor and evaluate gait and mobility patterns, particularly in populations with mobility impairments. The dataset comprises recordings from a diverse cohort of participants, including healthy individuals and patients with various mobility-related conditions. Data collection was conducted using state-of-the-art wearable sensors and devices, capturing a wide range of gait parameters and contextual information in both controlled and free-living settings. The primary objective was to ensure the robustness and precision of digital mobility assessment tools under real-world conditions. Key features of the dataset include: <ul> <li> Demographic & Clinical Data: Age, gender, height, weight, and clinical diagnoses. </li> <li> Sensor Data: Raw and processed data from accelerometers, gyroscopes, and other wearable sensors. </li> <li> Reference Gait Parameters: Stride length, stride frequency, gait speed, and variability measures. </li> </ul> The dataset has undergone rigorous validation processes to confirm its accuracy and reliability. It serves as a critical resource for researchers and developers aiming to enhance digital health technologies and improve clinical assessments of mobility. The TVS dataset paves the way for future innovations in digital biomarkers and personalized healthcare solutions. <h2>Brief Overview</h2> This dataset contains data from 108 participants from six cohort groups that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). Data was recorded across five measurement sites. Data availability varies between participants, and some tests might be missing for some participants. The recording was split into a comprehensive in-lab assessment and a 2.5 hour unsupervised free living conditions. For the in-lab measurements reference information from marker-based motion capture systems and the multi-device wearable <a href="https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2023.1143248/full">INDIP system</a> are provided. For the free-living recording, only the INDIP system is available as a reference. Participants wore a McRoberts MM+ IMU at the lower back. Some participants additionally wore a custom wrist-IMU at the non-dominant hand. The IMUs were synchronized with the reference system. The following tests were performed as part of the In-Lab data capture (Name in recording file in parentheses): <ol> <li> Timed-Up-and-Go (Test4) </li> <li> Straight Walk Comfortable (Test5) </li> <li> Straight Walk Slow (Test 6) </li> <li> Straight Walk Fast (Test 7) </li> <li> L-Test (Test8) </li> <li> Surface Test (Test9) </li> <li> Hallway Test (Test10) </li> <li> Simulated daily activities (Test11) </li> </ol> For some tests multiple trials are available. Additional trials were performed in the case of technical or performance issues. Hence, the last trial of each test should always be preferred. Tests not listed above (e.g. Test 1-3) are non-walking tests used for calibration. When performing gait related operations, these tests should be excluded. For the free-living recording Recording4 corresponds to the actual recording. Recording 1-3 only contains calibration recordings that are usually not required. Learn more about the data collection protocol: <blockquote> C. Mazzà, L. Alcock, K. Aminian, C. Becker, S. Bertuletti, T. Bonci, P. Brown, et al. "Technical Validation of Real-World Monitoring of Gait: A Multicentric Observational Study." BMJ Open 11, 12 (2021): e050785 (<a href="https://doi.org/10/gt55p7">https://doi.org/10/gt55p7</a>). S. Kirsty, T. Bonci, F. Salis, L. Alcock, E. Buckley, E. Gazit, C. Hansen, et al. "Design and Validation of a Multi-Task, Multi-Context Protocol for Real-World Gait Simulation." Journal of NeuroEngineering and Rehabilitation* 19, 1 (2022):141 (<a href="https://doi.org/10/gt55t6">https://doi.org/10/gt55t6</a>). </blockquote> <h2>Files</h2> /data/: Raw data files sorted by cohort/patientId/measurement_condition. /participant_information.xlsx: Basic demographic and clinical information of all participants and "data quality" overview for all recordings For each recording the following files are provided: <ul> <li> infoForAlgo.mat: Reduced set of relevant demographic information that is required to process the data with the Mobilise-D algorithmic pipeline </li> <li> data.mat: Core data file following the <a href="https://www.nature.com/articles/s41597-023-01930-9">Mobilise-D file format</a>. For each trial the data contains the raw sensor data of the lower-back IMU (SU), the raw data of all reference sensors, and calculated gold standard parameters for all relevant gait parameters based on the reference system. For some participants, data from a wrist worn sensor is included. </li> <li> test_list.json: A json file containing all the available tests and trials including the data.mat file. This information is also available via the data.mat file, but the json file is faster to parse and should help with identifying the correct data files to load. </li> </ul> <h2>Tips and Notes</h2> <h3>Data Quality</h3> Depending on the use case, specific trials should not be used. <code>participant_information.xlsx</code> file contains a sheet named data quality, that indicates for each system used, if the data was recorded properly. "0" indicates that the data is not usable at all, "1" indicates that some issues remain. This usually indicates partial or full data loss in a single test or unreliable reference information. These recordings might be usable for certain types of analysis, but should not be used for proper algorithm validation on the dataset. Only recordings with data quality >=2 for all required systems should be used. <h3>Walking Aid Use</h3> The <code>participant_information.xlsx</code> file contains 3 columns with information about walking aid use. The two columns <code>self_reported_indoors</code> and <code>self_reported_outdoors</code> describe the use of walking aids independent of the study context as reported by the patients of the day of the recording. This information might be different from the actual walking aids used during the assessment. This information can be found <code>use_during_lab_assessment</code> column. This information was recorded by the study conductor. For the free-living tasks patients were allowed to use any assistance they needed. Actual use was not recorded for this assessment. In general, only a small number of participants used walking aids within the study. Therefore, we do not recommend analyzing walking aid users as a different group or including walking aid use as a stratifier. <h3>General Notes</h3> <ul> <li> The participant IDs are "double pseudonymized" and do not correspond to data-ids used within the Mobilise-D project or previously published example data </li> <li> The first digit of the participant IDs identifies the recording center. This information might be helpful to identify systematic domain shifts in the data, as different centers used slightly different measurement setups. </li> <li> In case multiple trials are available for a single test, only use the last one when performing algorithm validation to keep the data between the participants balanced. </li> </ul> <h3>Reference Parameters</h3> Below some notes and general recommendation regarding the reference parameters: <ul> <li> Reference parameters are provided on a MicroWb and ContinousWalkingPeriod level. In most cases, you will likely want to work with the information in "ContinousWalkingPeriod" (if you are using mobgap to load the data, this information is simply called "Wb"). Learn more <a href="https://mobgap.readthedocs.io/en/latest/guides/q_and_a.html#walking-bouts-vs-gait-sequences">here</a> </li> <li> For in-lab measurements, the Stereophoto (aka. marker-based Mocap system) should be the preferred reference, as parameters are expected to be more accurate. However, due to limitations of the field-of-view of these systems, some walking trails are not completely covered by the references. </li> <li> Neither reference system includes turning information, as no established reference definition could be identified, that would allow for unbiased comparison of parameters. </li> </ul> Learn more about the methods for extracting reference parameters: <blockquote> T. Bonci, F. Salis, K. Scott, L. Alcock, C. Becker, S. Bertuletti, E. Buckley, et al. “An algorithm for accurate marker-based gait event detection in healthy and pathological populations during complex motor tasks” Frontiers in Bioengineering and Biotechnology, section Biomechanics, 10:868928, 2022 (DOI: 10.3389/fbioe.2022.868928). F. Salis , S. Bertuletti, T. Bonci, M. Caruso, K. Scott, L. Alcock, E. Buckley, et al. “A multi-sensor wearable system for the assessment of diseased gait in real-world conditions”. Frontiers in Bioengineering and Biotechnology, 11, 2023 (<a href="https://doi.org/10.3389/fbioe.2023.1143248">https://doi.org/10.3389/fbioe.2023.1143248</a>). </blockquote> <h2>Usage Recommendation</h2> This dataset is designed to validate algorithms and NOT to derive clinical insights from the patient cohorts. This dataset was used to validate the algorithms of the Mobilise-D computational pipeline for lower trunk IMU data. Details on the publications are reported below. Per-Block Validation: <blockquote> M.E. Micó-Amigo, T. Bonci, A. Paraschiv-Ionescu, M. Ullrich, C. Kirk, A. Soltani, A. Küderle, et al. "Assessing Real-World Gait with Digital Technology? Validation, Insights and Recommendations from the Mobilise-D Consortium." Journal of NeuroEngineering and Rehabilitation 20, no. 1 (June 14, 2023): 78 (<a href="https://doi.org/10/gt55qb">https://doi.org/10/gt55qb</a>). </blockquote> Full Pipeline Validation: <blockquote> K., Cameron, A. Kuederle, M.E. Mico-Amigo, T. Bonci, A. Paraschiv-Ionescu, M. Ullrich, A. Soltani, et al. "Estimating Real-World Walking Speed from a Single Wearable Device: Analytical Pipeline, Results and Lessons Learnt from the Mobilise-D Technical Validation Study." Scientific Reports, 14,1, 1754 2024 (<a href="https://doi.org/10.21203/rs.3.rs-2965670/v1">https://doi.org/10.21203/rs.3.rs-2965670/v1</a>). </blockquote> Implementation of these validation procedures are also available via the open-source library <a href="https://github.com/mobilise-d/mobgap/">mobgap</a>. We recommend the use of this library in all use cases, as it provides high level tools to load and process the dataset. Documentation for this can be found in the following examples: <ol> <li> <a href="https://mobgap.readthedocs.io/en/latest/auto_examples/data/_04_tvs_data_no_exc.html">The TVS dataset class</a> </li> <li> <a href="https://mobgap.readthedocs.io/en/latest/auto_examples/data/_01_loading_example_data.html">Working with data in mobgap</a> </li> <li> <a href="https://mobgap.readthedocs.io/en/latest/auto_examples/data/_02_working_with_ref_data.html">Working with reference data in mobgap</a> </li> </ol> <h2>Suggested Citation</h2> When you are working with the data, we suggest the following citation: <blockquote> Küderle, A. (2024). Mobilise-D Technical Validation Study (TVS) dataset [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.13899385">http://doi.org/10.5281/zenodo.13899385</a> </blockquote> Please cite our paper in your publications if our repository helps your research. <blockquote> C. Mazzà, L. Alcock, K. Aminian, C. Becker, S. Bertuletti, T. Bonci et al. "Technical Validation of Real-World Monitoring of Gait: A Multicentric Observational Study". BMJ Open 11, 12 2021): e050785. (<a href="https://doi.org/10/gt55p7">https://doi.org/10/gt55p7</a>). </blockquote> <h2>License and Legal Information</h2> Mobilise-D Technical Validation Study Dataset © 2024 by Mobilise-D Consortium is licensed under CC BY-NC-ND 4.0 <h2>Acknowledgments</h2> We extend our gratitude to all participants who contributed to the Mobilise-D project, enabling the comprehensive collection and analysis of mobility data. This work would not have been possible without the dedication and collaboration of the Mobilise-D Consortium members, including researchers, clinicians, and technical staff. We also acknowledge the funding and support provided by the European Union's Horizon 2020 research and innovation program under grant agreement No 820820. Special thanks to our partner institutions and organizations for their invaluable contributions and continued support. <h2>Disclaimer</h2> The Mobilise-D Technical Validation Study Dataset is provided for research purposes only. The Mobilise-D Consortium makes no warranties, express or implied, regarding the accuracy, completeness, or reliability of the dataset. Users of the dataset assume all responsibility for any conclusions drawn from the data. The dataset must be used in accordance with ethical guidelines and applicable laws and regulations. Any publications or presentations based on this dataset should appropriately cite the source. The Mobilise-D Consortium is not liable for any misuse of the dataset or for any direct, indirect, incidental, or consequential damages arising out of the use of the dataset.

opencc-by-4.0Jul 2024View details →

ThLebee/CoEg: Lettres d'Auguste Mariette (juillet 2025, v. 1,463)

Ajout des lettres à Lepsius (Musées royaux d'art et d'histoire de Bruxelles) et de celles envoyées à Brugsch jusqu'en 1858 (Università degli Studi di Milano, Biblioteca e Archivi di Egittologia).

FIG. 15 in Apport des collections du Muséum national d'Histoire naturelle et de Didier Morin à la connaissance des Batrachideinae et Lophotettiginae (Orthoptera, Tetrigidae) de Guyane

FIG. 15. — Distribution en Guyane des 14 espèces de Batrachideinae Bolívar, 1887 et Lophotettiginae Hancock, 1909 identifiées dans les deux collections étudiées. Sources: Wikimedia Maps © les contributeurs d'OpenStreetMap sous licence ODbL. Réalisé avec Qgis.

FIG. 14 in Apport des collections du Muséum national d'Histoire naturelle et de Didier Morin à la connaissance des Batrachideinae et Lophotettiginae (Orthoptera, Tetrigidae) de Guyane

FIG. 14. — Mâle de Phelene turgida (Bolívar, 1887) originaire de Régina (Nouragues, Saut Pararé), 11.XII.2010, coll. DM: A, habitus en vue latérale; B, habitus en vue dorsale; C, tête vue de face; D, tête et partie antérieure du pronotum en vue dorsale. Photos: G. Doucet.

FIG. 13 in Apport des collections du Muséum national d'Histoire naturelle et de Didier Morin à la connaissance des Batrachideinae et Lophotettiginae (Orthoptera, Tetrigidae) de Guyane

FIG. 13. — Femelle de Phelene maroon Cadena-Castañeda & Tumbrinck, 2021 originaire de Régina (Nouragues, Saut-Pararé), 24.VIII.2009, coll. DM: A, habitus en vue latérale; B, habitus en vue dorsale; C, tête vue de face; D, tête en vue dorsale; E, tête et partie antérieure du pronotum en vue latérale; F, extrémité de l'abdomen en vue dorsale; G, extrémité de l'abdomen en vue ventrale; H, ovipositeur en vue latérale. Photos: G. Doucet. Échelles: A, B, E, 1 mm; C, D, F-H, 0,5 mm.

FIG. 10. — Scaria madeleinae n in Apport des collections du Muséum national d'Histoire naturelle et de Didier Morin à la connaissance des Batrachideinae et Lophotettiginae (Orthoptera, Tetrigidae) de Guyane

FIG. 10. — Scaria madeleinae n. sp. holotype ♂, MNHN-EO-CAELIF11280 (A, B), Scaria producta Hancock, 1907 (C, D) et Scaria hamata (De Geer, 1773) (E, F): A, habitus en vue latérale; B, extrémité de l'abdomen en vue latérale; C, habitus en vue latérale; D, extrémité de l'abdomen en vue latérale; E, habitus en vue latérale; F, plaque sous-génitale en vue ventrale. Photos: G. Doucet. Échelles: A, C, E, 1 mm.