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Figure 12. - APlacotrochidescylindrica, holotype, Museum of Tropical Queensland G55627, off Queensland BPlacotrochidesfrustum, holotype, USNM 36451, Lesser Antilles; paratype, NMC, Hudson 4B, Lesser Antilles C Placotrochuslaevis, USNM 81994, Great Barrier Reef, Australia D Falcatoflabellumraoulensis, upper image, holotype, Museum of New Zealand, CO 258, Kermadec Ridge; lower images, paratype, USNM 94313, Kermadec Ridge. Scale bars: 1mm (A); 2 mm (B); 10 mm (C), except for basal scar, which is 5 mm; 1 mm (D), except latera view, which is 5 mm.
Figure 12. - APlacotrochidescylindrica, holotype, Museum of Tropical Queensland G55627, off Queensland BPlacotrochidesfrustum, holotype, USNM 36451, Lesser Antilles; paratype, NMC, Hudson 4B, Lesser Antilles C Placotrochuslaevis, USNM 81994, Great Barrier Reef, Australia D Falcatoflabellumraoulensis, upper image, holotype, Museum of New Zealand, CO 258, Kermadec Ridge; lower images, paratype, USNM 94313, Kermadec Ridge. Scale bars: 1mm (A); 2 mm (B); 10 mm (C), except for basal scar, which is 5 mm; 1 mm (D), except latera view, which is 5 mm.
Figure 11. - ATruncatoflabellumarcuatum, lateral and calicular views, holotype, NZOI H633, Norfolk Ridge; edge view, paratype, USNM 94280, Norfolk Ridge B Blastotrochusnutrix, USNM 97553, Siboga, Indonesia C Placotrochidesscaphula, USNM 94273, NZOI G941, New Zealand D Placotrochidesminuta, holotype, Australian Museum G16747, Flores Sea. Scale bars: 10 mm (A–C), except for calice of A and basal scar of B, which are 5 mm; 1 mm (D).
Figure 11. - ATruncatoflabellumarcuatum, lateral and calicular views, holotype, NZOI H633, Norfolk Ridge; edge view, paratype, USNM 94280, Norfolk Ridge B Blastotrochusnutrix, USNM 97553, Siboga, Indonesia C Placotrochidesscaphula, USNM 94273, NZOI G941, New Zealand D Placotrochidesminuta, holotype, Australian Museum G16747, Flores Sea. Scale bars: 10 mm (A–C), except for calice of A and basal scar of B, which are 5 mm; 1 mm (D).
Figure 9. - ATruncatoflabellumparipavoninum, USNM 96650, Soela 1/84/77, Western Australia B Truncatoflabellumstabile, USNM 98886, off Madeira C Truncatoflabellumcorbicula, USNM 67939, NZGS GS1341, Waitaki Valley, New Zealand (Duntroonian = Lower Oligocene) D Truncatoflabelluminconstans, syntypes, Valdivia 100, Zoologisches Museum Berlin. Scale bars: all 10 mm, except for basal scar views of B and C.
Figure 9. - ATruncatoflabellumparipavoninum, USNM 96650, Soela 1/84/77, Western Australia B Truncatoflabellumstabile, USNM 98886, off Madeira C Truncatoflabellumcorbicula, USNM 67939, NZGS GS1341, Waitaki Valley, New Zealand (Duntroonian = Lower Oligocene) D Truncatoflabelluminconstans, syntypes, Valdivia 100, Zoologisches Museum Berlin. Scale bars: all 10 mm, except for basal scar views of B and C.
Figure 8. - ATruncatoflabellumcumingi, neotype, USNM 81976, Te Vega 1-54, Indonesia B Truncatoflabellumvanuatu, holotype, USNM 71860, Pleistocene of Vanuatu C Truncatoflabellumduncani, paratype, USNM M353592, Balcombe's Bay, Victoria (Balcombian = Middle Miocene) D Truncatoflabellummultispinosum, paratype, USNM 91741, South Africa. Scale bars: all 10 mm, except for basal scar views of B and C.
Figure 8. - ATruncatoflabellumcumingi, neotype, USNM 81976, Te Vega 1-54, Indonesia B Truncatoflabellumvanuatu, holotype, USNM 71860, Pleistocene of Vanuatu C Truncatoflabellumduncani, paratype, USNM M353592, Balcombe's Bay, Victoria (Balcombian = Middle Miocene) D Truncatoflabellummultispinosum, paratype, USNM 91741, South Africa. Scale bars: all 10 mm, except for basal scar views of B and C.
Figure 7. - Section of the female proboscis of the Japanese Epicephala species. All photographs were taken from non-type specimens. A Epicephalaanthophilia (slide No. AK303) B Epicephalabipollenella (slide No. AK298) C Epicephalalanceolatella (slide No. AK300) D Epicephalaperplexa (slide No. AK301) E Epicephalaobovatella (slide No. AK307) F Epicephalacorruptrix (slide No. AK304) G Epicephalavitisidaea (slide No. AK297) H Epicephalaparasitica (slide No. AK308), arrows indicate rudimentary sensilla I Epicephalanudilingua (slide No. AK309). lp, labial palp. Scale bar: 0.1 mm.
Figure 7. - Section of the female proboscis of the Japanese Epicephala species. All photographs were taken from non-type specimens. A Epicephalaanthophilia (slide No. AK303) B Epicephalabipollenella (slide No. AK298) C Epicephalalanceolatella (slide No. AK300) D Epicephalaperplexa (slide No. AK301) E Epicephalaobovatella (slide No. AK307) F Epicephalacorruptrix (slide No. AK304) G Epicephalavitisidaea (slide No. AK297) H Epicephalaparasitica (slide No. AK308), arrows indicate rudimentary sensilla I Epicephalanudilingua (slide No. AK309). lp, labial palp. Scale bar: 0.1 mm.
Figure 1. - Representative specimens of the nine Epicephala species in Japan. Wing pattern of Epicephalaparasitica is sexually dimorphic, so specimens of both sexes are shown for this species. A Epicephalaanthophilia (Amami Island, Kagoshima, ♀, holotype) B Epicephalabipollenella (Henoko, Okinawa, ♀) C Epicephalalanceolatella (Cape Hedo, Okinawa, ♀, holotype) D Epicephalaperplexa (Cape Hedo, Okinawa, ♀, holotype) E Epicephalaobovatella (Tomogashima, Wakayama, ♂, paratype) F Epicephalacorruptrix (Takae, Okinawa, ♀, holotype) G Epicephalavitisidaea (Yona, Okinawa, ♀) H Epicephalaparasitica (Yonaguni Island, Okinawa, ♀, holotype) I Epicephalaparasitica (Hateruma Island, Okinawa, ♂) J Epicephalanudilingua (Watarase-yusuichi, Tochigi, ♀, holotype). Scale bar: 5 mm.
Figure 1. - Representative specimens of the nine Epicephala species in Japan. Wing pattern of Epicephalaparasitica is sexually dimorphic, so specimens of both sexes are shown for this species. A Epicephalaanthophilia (Amami Island, Kagoshima, ♀, holotype) B Epicephalabipollenella (Henoko, Okinawa, ♀) C Epicephalalanceolatella (Cape Hedo, Okinawa, ♀, holotype) D Epicephalaperplexa (Cape Hedo, Okinawa, ♀, holotype) E Epicephalaobovatella (Tomogashima, Wakayama, ♂, paratype) F Epicephalacorruptrix (Takae, Okinawa, ♀, holotype) G Epicephalavitisidaea (Yona, Okinawa, ♀) H Epicephalaparasitica (Yonaguni Island, Okinawa, ♀, holotype) I Epicephalaparasitica (Hateruma Island, Okinawa, ♂) J Epicephalanudilingua (Watarase-yusuichi, Tochigi, ♀, holotype). Scale bar: 5 mm.
Figure 4. - Seventh abdominal segment and corpus and ductus bursae of the Japanese Epicephala species. A, Epicephalaanthophilia (paratype, slide No. AK250) B Epicephalabipollenella (slide No. AK281) C Epicephalalanceolatella (non-type, slide No. AK251) D Epicephalaperplexa (paratype, slide No. AK253) E Epicephalaobovatella (non-type, slide No. AK246) F Epicephalacorruptrix (paratype, slide No. AK262) G Epicephalavitisidaea (slide No. AK239) H Epicephalaparasitica (non-type, slide No. AK293) I Epicephalanudilingua (paratype, slide No. AK296). Scale bar: 1 mm.
Figure 4. - Seventh abdominal segment and corpus and ductus bursae of the Japanese Epicephala species. A, Epicephalaanthophilia (paratype, slide No. AK250) B Epicephalabipollenella (slide No. AK281) C Epicephalalanceolatella (non-type, slide No. AK251) D Epicephalaperplexa (paratype, slide No. AK253) E Epicephalaobovatella (non-type, slide No. AK246) F Epicephalacorruptrix (paratype, slide No. AK262) G Epicephalavitisidaea (slide No. AK239) H Epicephalaparasitica (non-type, slide No. AK293) I Epicephalanudilingua (paratype, slide No. AK296). Scale bar: 1 mm.
Supplementary materials for "Testing the goodness of the EVS gender role attitudes scale"
<p>The files include supplementary results for background analyses performed for the manuscript "Testing the goodness of the EVS gender role attitudes scale" submitted to the journal <em>Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique</em>. One file reports the result from exploratory factor analyses run on the EVS 2008 scale with 7 and 8 items; the other one shows intermediate results from the stepwise regression models performed on the same scale from EVS 2008 and EVS 1999.</p> <p>Lomazzi, V. (2017). Testing the Goodness of the EVS Gender Role Attitudes Scale. <em>Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique</em>, <em>Forthcoming</em>. https://doi.org/10.1177/0759106317710859</p>
Data supplementing the article "Assessing ecological status with diatoms DNA metabarcoding : scaling-up on a WFD monitoring network (Mayotte island, France)" V. Vasselon, F. Rimet, K. Tapolczai, A. Bouchez submitted to Ecological Indicators journal
<p>These data supplement the article"Assessing ecological status with DNA metabarcoding or microscopy? Comparison using benthic diatoms in tropical rivers" V. Vasselon, F. Rimet, K. Tapolczai, A. Bouchez submitted to Ecological Indicators journal.</p> <p>The directory contains the following files:</p> <p><strong>80 PGM sequencing libraries (raw data, fastq files).rar </strong>- contains the 80 fastq files provided by the sequencing platform with demultiplexed DNA reads (raw data prior any bioinformatics treatments).</p> <p><strong>80 fastq files information.xlsx</strong> - contains the information relative to the 80 samples including: the ID used in Mothur analyses (corresponding to the name of the fastq files), the sample name, the sampling site code, the name of the river, the monitoring network to which rivers belong, the year of sampling and the GPS coordinates of sampling sites.</p> <p><strong>OTU (95 percent of similarity) list of 80 Mayotte samples.xlsx</strong> - contains the final OTU list obtained after applying all the bioinformatics treatments (trimming, clustering,...): OTUs created at 95% of similarity, the number of DNA reads per sample was normalized at 5710 reads (the smallest values obtained in one sample). A DNA representative sequence and the taxonomic assignment determined using Mothur (using classify.otu command) are also provided for each OTU.</p>
AMDADOS datasets and scaling benchmarks
<p>This dataset provides data from benchmark scaling experiments on the AMDADOS advection diffusion code integrated with data assimilation. Results are included for performance in serial environment, MPI parallel and parallelized using the AllScale environment</p>
Photos of ostraca of Iruña-Veleia (Spain) with scale and transcription.
<p>Photos of ostraca of Iruña-Veleia (Spain) with scale and transcription in the article by Joseba Lizeaga.</p>
A brief self-rating scale for the assessment of individual differences in gesture perception and production
<p>Data related to the following publication:</p> <p>Nagels, A., <strong>Kircher, T.</strong>, Steines, M., Grosvald, M., & <strong>Straube, B.</strong> (2015). A brief self-rating scale for the assessment of individual differences in gesture perception and production. <em>Learning and Individual Differences, 39</em>, 73–80.</p> <p> </p> <p>The Statistical Package for Social Sciences (SPSS) software was applied to perform a Principal Component Analysis (PCA) with varimax rotation.</p> <p>SPSS file: BAG_all_data_220_online.sav includes all relevant data of the E-scale (EP1-EP25) and the BAG scale (GE1-GE12).</p> <p>SPSS script: Syntax_BAG_analyses.sps includes the relevant scripts.</p> <p> </p>
Velocity measurements of a bench scale buoyant plume applying particle image velocimetry
<p>2D PIV velocity field data from the open plume experiment run 2015 at the <br> Forschungszentrum Juelich. An electrically heated copper block placed in an enclosure <br> creates a buoyancy driven plume.</p> <p><strong>IMPORTANT: </strong>The data set is provided on the following webpage<br> https://www.fz-juelich.de/ias/ias-7/EN/Research/Fire_Dynamics/Data/2017_openplume/_node.html</p> <p> </p> <p> </p>
Measures of the heights of the letters of the ostraca with scale of Iruña-Veleia, and, distances between lines and between letters of the ostraca with scale of Iruña-Veleia.
<p>Measures of the heights of the letters of the ostraca with scale of Iruña-Veleia, and, distances between lines and between letters of the ostraca with scale of Iruña-Veleia.</p>
Lab-scale membrane bioreactor (MBR) high-frequency online data
<p>This dataset is composed of 4 periods of more or less continuous operation, but with varying length. The measured online data, with a frequency of per second, are stored chronologically in 4 compressed csv-files 'Data_MBR_Period_x'. The installation was running with some fixed and variable settings. The variable settings are stored in 'Settings_MBR'. They were only logged when changed, making this a small file. All additional information on the used sensors etc. is listed in the 'Metadata'-file, including the values of the fixed settings. Finally, 'Schematic.png' gives an overview of the MBR setup and the location of the sensors.</p>
Large-scale Forest Stand Height mapping for the northeast of U.S. and China using L-band spaceborne repeat-pass InSAR and GEDI
<p><span>The forest height mosaic for the northeastern parts of China and U.S are generated based on a global-to-local inversion approach proposed in </span><span><span>(Yu et al., 2023)</span></span><span> by making use of Spaceborne repeat-pass InSAR and spaceborne GEDI data. The sparsely but extensively distributed LiDAR samples provided by NASA’s GEDI mission are used to parametrize the semi-empirical repeat-pass InSAR scattering model(Lei et al., 2017) and to obtain forest height estimates. Compared to our previous efforts </span><span><span>(Lei et al., 2018, Lei and Siqueira, 2022)</span></span><span>, this paper further removes the assumptions that were made given the limited availability of calibration samples at that time, and developed a new inversion approach based on a global-to-local two-stage inversion scheme. This approach allows a better use of local GEDI samples to achieve finer characterization of temporal decorrelation pattern and thus higher accuracy of forest height inversion.<span> </span>This approach is further fully automated to enable a large-scale forest mapping capability. Two forest height mosaic maps were generated for the entire northeastern regions of U.S. and China with total area of 18 million hectares and 112 million hectares, respectively. The validation of the forest height estimates demonstrates much improved accuracy achieved by the proposed approach compared to the previous efforts i.e., reducing from RMSE of 3-4 m on the order of 3-6-hectare aggregated pixel size to RMSE 3-4 m on the order of 0.81-hectare pixel size. The proposed fusion approach not only addresses the sparse spatial sampling problem inherent to the GEDI mission, but also improve the accuracy of forest height estimates compared to the GEDI-interpolated maps by a factor of 20% at 30-m resolution. The extensive evaluation of forest height inversion against LVIS LiDAR data indicates an accuracy 3-4 m on the order of 0.81 hectare over smooth areas and 4-5 m over hilly areas in U.S., whereas the forest height estimates over northeastern China are best compared with small footprint LiDAR validation data even at an accuracy of even below 3.5 m with R2 mostly above 0.6. Such a forest height inversion accuracy at sub-hectare pixel size provides promising values towards the existing and future spaceborne LiDAR (</span><span>JAXA’s MOLI, NASA’s GEDI, China’s TECIS</span><span>) and InSAR missions (</span><span>NASA-ISRO’s NISAR, JAXA’s ALOS-4 and China’s LuTan-1</span><span>). This fusion prototype can work as a cost-effective solution for public users to obtain a wall-to-wall forest height mapping at large scale when only spaceborne repeat-pass InSAR data are available and freely accessible.</span></p>
Assessing the long-term Stability of the Spielberger State-Trait Inventory trait scale over 3.5 Years
<p>The Spielberger State Trait Anxiety Inventory (STAI) is the most cited measure of state and trait anxiety, and is routinely employed in a variety of research and clinical contexts. Here, we investigate the temporal stability as well as the convergent and discriminant validity of the German version of the STAI-T scale across multiple time points in two independent samples (105 and 120 Caucasians). We observed temporal stabilities of .42 - .67 for intervals between 20 and 41 months and from .81 - .87 for intervals of five to 12 months, with decreasing stability as the time interval increased. Temporal stability estimates of the STAI-T were similar to those of related constructs. Additionally, examining the relationships within a nomological network support the recent conclusion that the STAI-T also shares substantial variance with questionnaires measuring negative emotionality such as depression, and hence does not measure anxiety specifically – despite its name. These results provide further psychometric information on what the STAI-T actually measures and to what extent STAI-T scores are expected to be stable across longer time intervals. This is of relevance for researchers aiming, for example, to use the STAI-T scale for predicting symptom trajectories and evaluating the effectiveness of therapeutic interventions.</p>
Kappa matrices presented in "An EFT approach to baryon number violation: lower limits on the new physics scale and correlations between nucleon decay modes" arXiv:2312.13361
<p>In this archive we provide the kappa matrices introduced in Sec 2.4 of our paper <em>An EFT approach to baryon number violation: lower limits on the new physics scale and correlations between nucleon decay modes</em> <a href="https://arxiv.org/abs/2312.13361">2312.13361</a>, also <a href="https://link.springer.com/article/10.1007/JHEP07(2024)004">published in JHEP</a>. These are the numerical values that are presented as matrix plots in Appendix C. Here they are packaged into CSV files, whose first row is the row/column label.</p> <p>Please consult the README for further information on how to use these data.</p> <p><em>Version 3: Fixed error common to d=7 matrices.</em></p>
SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations
<h2>SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations</h2> <p>SynProtX is a deep learning model that integrates large-scale proteomics data, molecular graphs, and chemical fingerprints to predict synergistic effects of anticancer drug combinations. It provides robust performance across tissue-specific and study-specific datasets, enhancing reproducibility and biological relevance in drug synergy prediction.</p> <p>This Zenodo repository includes a <code>.tar.gz</code> archive containing all essential components to reproduce the experiments described in the study. This archive is designed to work seamlessly with the coding pipeline available at: <a href="https://github.com/manbaritone/SynProtX" target="_blank" rel="noopener">https://github.com/manbaritone/SynProtX</a>.</p> <h3>License:</h3> <p>Creative Commons Zero v1.0 Universal (CC0)<br>This work is released under CC0, dedicating it to the public domain. You are free to use, modify, and distribute it without restriction.</p> <h3>Archive Contents:</h3> <p>This compressed file includes:</p> <ul> <li>Datasets<br>- Tissue Datasets: <code>ALMANAC-Breast</code>, <code>ALMANAC-Lung</code>, <code>ALMANAC-Ovary</code>, <code>ALMANAC-Skin</code><br>- Study Datasets: <code>FRIEDMAN</code>, <code>ONEIL</code></li> <li>Supporting Files<br>- Raw and preprocessed data<br>- Feature dictionaries<br>- Hyperparameter configurations<br>- Trained model weights</li> </ul> <h3>Folder Structure:</h3> <blockquote> <p><code>SynProtX/</code><br><code>├── data/ # Raw and preprocessed data</code><br><code>│ ├── export/ # Processed protein/gene expression & drug combinations</code><br><code>│ ├── nps/ # Numpy arrays for all datasets</code><br><code>│ ├── nps_intersected/ # Dataset-specific numpy arrays</code><br><code>│ └── raw/ # Original data from DrugComb, CCLE, COSMIC, ChEMBL V31, ProCan-DepMapSanger</code><br><code>├── feature_dicts/ # Feature dictionaries for drug combinations</code><br><code>├── hyperparams/ # Hyperparameter configs for SynProtX-GATFP</code><br><code>│ ├── classification/ # For classification tasks</code><br><code>│ └── regression/ # For regression tasks</code><br><code>├── state_dict/ # Trained model weights</code><br><code>│ ├── classification/ # PyTorch checkpoints for classification</code><br><code>│ └── regression/ # PyTorch checkpoints for regression</code><br><code>└── README_Zenodo.md # This file</code></p> </blockquote> <h3>For more information, please visit:</h3> <p><strong>GitHub:</strong> <a href="https://github.com/manbaritone/SynProtX" target="_blank" rel="noopener">https://github.com/manbaritone/SynProtX</a></p>
Australian Frog Atlas: Fine-scale species distribution maps informed by the FrogID dataset
<p>This is version three of the Australian Frog Atlas: shapefiles and KML files of the most up to date, accurate and detailed set of Australian frog species distribution maps available, and both a shapefile and detailed PDF map of Australian frog species richness. All of these are available as a free resource under the Creative Commons Attribution (CC-BY) 4.0 License. When using any material from the Australian Frog Atlas, please refer to and cite Cutajar et al. (2022), outlined in full below, which introduces the dataset and describes it in detail.</p> <p>Cutajar. T. P., Portway, C. D., Gillard, G. L., and Rowley, J. J. L. (2022) Australian Frog Atlas: Fine-scale species distribution maps informed by the FrogID dataset. <em>Technical Reports of the Australian Museum Online</em>. 36: 1-48. <a href="https://doi.org/10.3853/j.1835-4211.36.2022.1789">https://doi.org/10.3853/j.1835-4211.36.2022.1789</a></p> <p><strong>Caveat:</strong> The occurrence data used in the development of these maps are from a range of sources. Every effort has been made to ensure accuracy and completeness, including vetting of data using published literature. However, no guarantee is given, nor responsibility taken by the authors or their institutions for errors or omissions, nor in respect of any information or advice given in relation to, or as a consequence of, the maps and data provided herein. These maps are indicative only and aim to capture the known and presumed distributions of individual frog species and frog species richness within Australia. A taxonomic revision of Australo-Papuan tree frogs published in June 2025 proposes the reclassification of more than 120 Australian tree frog species (in the genera <em>Litoria</em> and <em>Cyclorana</em>) into 22 genera. This proposed change has not been adopted in this version of the Australian Frog Atlas. </p> <p> </p> <p> </p>
ScienceDex guides
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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.