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FIGURE 3 in Redescription of soft tissue preservation in the holotype of Scaphognathus crassirostris (Goldfuss, 1831) using reflectance transformation imaging
FIGURE 3. Close-ups of two different regions on the main slab demonstrating the optical difference between unprepared regions associated with soft parts (green arrow) and the surfaces prepared by Goldfuss (red arrow), without being processed with the specular enhancement mode. 3A. The sharp border between the unprepared ochre- and beige-coloured limestone surface and the homogenous, striated surface directly ventral to the above-mentioned bones, which underwent preparation. 3B. The striations created by Goldfuss are more clearly discernible dorsal to the cervical vertebral column (red arrow). Scale bar in both illustrations equals 10 mm.
FIGURE 6 in Redescription of soft tissue preservation in the holotype of Scaphognathus crassirostris (Goldfuss, 1831) using reflectance transformation imaging
FIGURE 6. Close-ups of RTIViewer snapshots of the region ventral to the zeugopodial bones of the right wing on the main slab, taken under different lighting conditions, but all processed using the specular enhancement mode (except for 6A and 6H). Scale bar for all illustrations equals 10 mm, except for 6G and 6H (one millimetre). Markings for various pycnofibre types used throughout this Figure: Type 2 (bifurcated; yellow circle), Type 3 (trident-like; orange circle), Type 5 (tuft, red circle), and Type 6 (symmetrical "feather", green circle). The path of individual pycnofibre impressions, and the path of individual side branches of single impressions are illustrated by red markings (either by straight lines or by curved arcs as in 6D, 6E, and 6H). 6A, 6C. Overview of the area with the pycnofibres ventral to the zeugopodial bones of the right wing (upper left corner of both images) and the phalanges of the right wing finger (near the right image margin) under normal light (6A) as well as under the specular enhancement mode (6C). 6B. Schematic sketch of Figure 6C, showing the appearance of the pycnofibre impressions under normal light. 6D-6E. Closeups of 6C. Note the parallel to subparallel alignment of several pycnofibre impressions (red vertical lines in 6D and 6E) and the easily detected caudally curved side branches of the Type 4 pycnofibre (red rectangle in 6F). The complex structure with several putative side branches at the right image margin between Type 2 and 3 pycnofibres (red arrow) is more likely to represent an arrangement of overlapping impressions of individual pycnofibres. 6F. Sketch of the RTI images 6D and 6E. 6G. Detailed close-up of the Type 2 pycnofibre, outlined by red markings. 6H. Detailed close-up of the Type 5 pycnofibre. The longest branch in the middle has a distinctive bifurcation (red circle).
FIGURE 10 in Redescription of soft tissue preservation in the holotype of Scaphognathus crassirostris (Goldfuss, 1831) using reflectance transformation imaging
FIGURE 10. Close-up RTIViewer snapshots of the region enclosed by the articulation of the humerus with the zeugopodial bones of the right wing and the intersection point of the zeugopodial bones of both wings on the main slab, taken under different lighting conditions, but all processed using the specular enhancement mode (with the exception of 10A and 10B). Scale bar for all illustrations equals 10 mm. Markings for various soft part impressions used throughout this Figure: the longest soft part impression connecting the zeugopodial bones of the right wing with the humerus of the same wing is marked by a thick transversal red line, the other shorter ones running parallel to subparallel to each other by thin red lines, channel-like grooves on the bone surface are pointed out by a red rectangle. Dissolved limestone layer surfaces are pointed out by red rectangles. 10A. Overview over the area with the organic remains between the zeugopodial bones of both wings. The location of the most pronounced grooves is highlighted (yellow rectangle) as well as the blood vessels (orange rectangle). Image modified from the.rti file of Jäger et al. (2018). 10B. The impressions in this area do not share a common starting point. Note the parallel to subparallel arrangement of the shorter soft part impressions. Also, pay attention to the channel-like grooves on the bone surface and the whitish irregularly-shaped stains of the sediment layer between the zeugopodial bones, probably being the result of aqueous solutions, which might have occurred during fossilisation, and which might have dissolved the former uppermost sedimentary layer. Although speculative, such solutions might have been derived from escaping body fluids in the context of the taphonomy of the integumentary appendages (see Foth, 2012 for a detailed discussion). 10C-10D. Specular enhancement images of 10B, taken under different lighting conditions to highlight the parallel arrangement of the soft part-related impressions and the channel-like grooves on the bones. Note the oblong channel connecting individual shorter ones (thick green arrow) and especially the zigzag pattern of some shorter channels (brown ellipse in both figures). 10E. Interpretative drawing of Figure 10C and 10D. 10F. The blood vessels near the intersection point of the zeugopodial bones of both wings. The subparallel alignment of the vessels (red slightly curved lines in Figure 10F) might indicate a similarity with the blood vessel system in the Rhamphorhynchus specimen JME SOS 4784 (Tischlinger and Frey, 2002; Frey et al., 2003). Note the distinct bifurcation of the rightmost vessel (red circle). Image modified from the.rti file of Jäger et al. (2018). 10G. Detailed close-up of Figure 10F. Small pits (red circles) might be the result of degradation processes in the context of the decay of the pterosaur carcass, although the exact generic process is uncertain.
FIGURE 9 in Redescription of soft tissue preservation in the holotype of Scaphognathus crassirostris (Goldfuss, 1831) using reflectance transformation imaging
FIGURE 9. Close-ups of RTIViewer snapshots of the region ventral to the cervical vertebral column and at the articulation of the first with the second phalanx of the right wing finger on the main slab, processed without (9A and 9D) and with the specular enhancement mode (9B and 9E). Scale bar for all illustrations equals 10 mm. Markings for various pycnofibre types used throughout this Figure: The Type 2 (bifurcated) pycnofibre type is marked by a red rectangle, orange-brown sediment surfaces on which the aktinofibrils impressions are to be found are illustrated by a red rectangle and a red ellipse. The suggested border of the partly preserved wing membrane after Jäger et al. (2018) is also marked (red transversal line). 9A. The whitish amorphous rock surface ventral to the cervical vertebral column. 9B. Specular enhancement image of 9A. Frequent occurrence of pycnofibres marked by a red triangle. The pycnofibre accumulation within the red circle might represent closely spaced neighbouring bifurcated Type 2 pycnofibres (pointed out by a red arrow). 9C. Interpretative drawing of 9B. 9D. Aktinofibril impressions close to the articulation of the first with the second phalanx of the right wing finger, especially well preserved within two orange-brown sediment surfaces. Also note the presence of aktinofibrils on the surface of the phalanges, visible in 9D as well as in 9E (recognisable by a grooved bony surface). 9E. Specular enhancement image of 9D. Aktinofibrils beyond the patagium border (and therefore laying on the bone surface of the phalanges) indicate their taphonomical displacement. 9F. Interpretative drawing of 9E demonstrating the spatial arrangement of the aktinofibril impressions. Not shown are the aktinofibrils on the bone surface.
FIGURE 2 in Redescription of soft tissue preservation in the holotype of Scaphognathus crassirostris (Goldfuss, 1831) using reflectance transformation imaging
FIGURE 2. Main slab (2A) and counter slab (2B) of the Scaphognathus crassirostris holotype, IGPB Goldfuss 1304a and b. Black rectangles and triangles illustrate the four different body regions in which soft part preservation is present: dorsal to the dorsal vertebral column until the base of the cervical vertebral column (1), ventral to the zeugopodial bones and next to the first and second phalanx of the fourth wing finger of the right wing (2), ventral to the cervical vertebral column (3), and the region enclosed by the zeugopodial and stylopodial bones of both wings (4). Images adapted from Jäger et al. (2018).
Transformers Model Zoos and Soups: A Population of Language and Vision Models
<p>Model Zoos submitted to the NeurIPS 2024 Dataset & Benchmark track: "<em>Transformer Model Zoos and Soups: A Population of Language and Vision Models</em>"</p> <p>We generate two model zoos, one for computer vision built on the ViT-S architecture, and one for language modeling based on the BERT architecture. For each, we train several backbone models with varying hyperparameters, and further fine-tune them using multiple hyperparameter combinations. We further annotate every model with performance metrics. These include test accuracy and F1-score, as well as the generalization gap. For the vision models, we also include the robust accuracy after a FGSM attack.</p>
Efficacy of Transformational Breath® for anxiety management in professional voice users
<p>Raw data for publication of original research entitled "<span>Efficacy of Transformational Breath<sup>®</sup> for anxiety management in professional voice users".</span></p> <p><span>Randomised controlled trial</span></p> <p><span>Quantitative and qualitative data sets relating to responses of treatment and control groups.</span></p>
Vision-Transformer, ViT, model validation dataset
<p><span>The U.S. cotton industry is highly concerned with removing plastic contamination from cotton lint. A major source of this </span><span>contamination is the plastic used to wrap cotton modules produced by John Deere round module harvesters. A machine-vision </span><span>detection and removal system has been developed to address this problem, using low-cost color cameras to detect plastic in the </span><span>cotton stream and remove it. However, the system requires a lot of calibration and is difficult for cotton gin workers to operate due to </span><span>its reliance on custom machine-vision classifier running on low-cost ARM computers running Linux. This research aims to make the system more user-friendly by adding an </span><span>auto-calibration feature that can track cotton colors and avoid plastic images, reducing the need for skilled personnel to operate the </span><span>system and making it easier for the cotton ginning industry to adopt. This image dataset was created to validate several Vision-</span><span>Transformer, ViT, AI models that in combination provides the key enabling technology for the auto-calibration code.</span></p>
Dataset: VanEck Digital Transformation ETF (DAPP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 2 in Multi-Year Dynamics Of Faunistic Complexes Of Amphibian And Reptile In Natural And Transformed Ecosystems In Belarus
Fig. 2. Changes in the species composition and number of faunistic complexes of amphibians and reptiles in the long-term monitoring points on the site of a low-land bog transformed into farmland (drainage reclamation work was implemented in 2000) Notes: R.a. – Rana arvalis, R.t. – Rana temporaria, B.b. – Bufo bufo, P.e.c. – Pelophelaх esculentus complex, B.v. – Bufotes viridis, P.f. – Pelobates fuscus, L.v. – Lissotriton vulgaris, Z.v. – Zootoca vivipara, N.n. – Natrix natrix, V.b. – Vipera berus, L.a. – Lacerta agilis
Fig. 1 in Multi-Year Dynamics Of Faunistic Complexes Of Amphibian And Reptile In Natural And Transformed Ecosystems In Belarus
Fig. 1. Options of long-term dynamics of herpetocomplex species diversity in regular monitoring sites in Belarus Notes: a-upland bog, natural reserve (stable state), b-floodplain meadow, poorly transformed territory (fluctuating dynamics), c-mixed coniferous-small-leaved forest, transformed landscape (unstable state)
Fig. 1 in Present Factors And Crucial Trends Of Anthropogenic Transformation Of Herpetofauna In Belarus
Fig. 1. Generalized scheme of impact anthropogenic transformation of ecosystems on the assemblies of amphibians and reptiles in Belarus.
Fig. 2 in Present Factors And Crucial Trends Of Anthropogenic Transformation Of Herpetofauna In Belarus
Fig. 2. Changes in the species structure of amphibian and reptile's assemblies as result of impact different anthropogenic factors.
Figure 2. – Otolith images from a in Automatic method to transform routine otolith images for a standardized otolith database using R
Figure 2. – Otolith images from a binocular dissecting microscope under different types of illumination: A. Reflected light and B. Transmitted light.
Figure 1 in Automatic method to transform routine otolith images for a standardized otolith database using R
Figure 1. – Different types of otolith images showing common issues. Scale is different between images depending on the sample, some broken otoliths, various exposures and colors, some particles may be present (hair, bubbles).
Figure 1 in Ostracods in the plankton of the Sivash Bay (the Sea of Azov) during its transformation from brackish to hypersaline state
Figure 1. Bay Sivash. Occurrence of the five ostracod species (only "alive" specimens) at the sampling stations in 2004, 2014 and 2015. Red icons – Cyprideis torosa, green – Loxoconcha bulgarica, violet – Loxoconcha aestuarii, blue – Cytherois cepa, yellow – Leptocythere devexa.
Input data and results of the RECC v2.5 model for the transformation scenarios of the global building stock
<p>This dataset contains the input data and core results of the RECC v2.5 model for the transformation scenarios of the global building stock. For details abou the RECC model, see DOI <a href="https://doi.org/10.1111/jiec.13023" target="_blank" rel="noopener">https://doi.org/10.1111/jiec.13023</a> and the RECC model landing page: <a href="https://www.industrialecology.uni-freiburg.de/odym-recc" target="_blank" rel="noopener">https://www.industrialecology.uni-freiburg.de/odym-recc</a></p> <p>The following data are included in this dataset:</p> <ul> <li>The entire model input database (120 model parameters)</li> <li>The parameters for the sensitivity analysis (8 parameters)</li> <li>The 70 folders with the core results</li> <li>The master classification file RECC_Classifications_Master_V2.0.xlsx</li> <li>The model config file RECC_Config.xlsx</li> <li>The list of scenario configurations RECC_ModelConfig_List.xlsx</li> <li>The result compilation and exporting configuration file RECCv2.5_EXPORT_Combine_Select.xlsx</li> <li>The main result summary file (extracted from the 70 result folders) Results_Extracted_RECCv2.5_10Regs_sep.xlsx</li> <li>The result summary file for comparison with the CRAFT model timber supply RECCv2.5_10Regs_CRAFT_Coupling_SHARE.xlsx</li> <li>The results of the sensitivity analysis: Results_Extracted_RECCv2.5_10Regs_Sensitivity_sep.xlsx</li> </ul> <p>Note that the result folders of the sensitivity analysis are not archived here (too little information in relation to the data volume). They can be requested from the author. The results can also be recreated by running the RECC model with the sensitivity analysis parameters.</p> <p>The model itself is available as Python code from <a href="https://github.com/IndEcol/RECC-ODYM" target="_blank" rel="noopener">https://github.com/IndEcol/RECC-ODYM</a></p>
Datasets for sandboxing use case SUC3 corresponding to cyber attacks affecting the differential protection scheme of a HV transformer
<p><span>These datasets reflect two main scenarios (S1-S2) associated to the operation of a sandboxing use case SUC3 corresponding to cyber attacks affecting the differential protection scheme of a HV transformer. Details about are illustrated in Section 1.3 of the supporting document. These scenarios analyse the operation of the digital twin of the IEEE 9-bus system and the differential protection scheme under healthy conditions, cyber-attack on communication channels of IEC 61850 Sample Values (SVs) protocol, and a fault in HV side of a transformer in the power system. The scenarios are presented with selected time-series plots in Section 1.3, accompanied a detailed analysis of the processes included and an impact assessment. Thus, d</span><span>uring execution of each scenario, data such as electrical measurements were captured and are collected</span> in the form of the datasets presented here.</p> <p>Specifically, </p> <ul> <li>SUC3/S1 <strong>Differential protection operation during transformer fault</strong> corresponds to the dataset of first scenario (S1) of the third sandboxing use case (SUC3) of the KIOS CoE Sandboxing for cyber-physical analysis of EPES, which examines the operation of differential protection scheme (implemented in Typhoon controller) for a HV/MV transformer. The protection scheme receives data sent through IEC 61850 SVs from the two sides of the transformer. Specifically, this dataset corresponds to the first scenario (S1) of SUC3, where a short-circuit occurred on the HV side of a HV/MV transformer of the system. More details about the scenario related to this dataset can be found in Section 1.3.1 of the supporting document. This dataset includes electrical measurements of the current flow, in RMS and sinusoidal format, from the HV and MV sides of HV/MV transformer of the digital twin of the IEEE 9-bus system. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of RMS values were recorded by the Typhoon controller, while the sinusoidal measurements were recorder by OPAL-RT.</li> <li>SUC3/S2 <strong>MITM with FDI cyber-attack in the SVs of HV transformer side</strong> corresponds to the dataset of the second scanario (S2) of the third sandboxing use case (SUC3) of the KIOS CoE Sandboxing for cyber-physical analysis of EPES, which examines a MITM with FDI cyber-attack is conducted on the measurements of the HV side of the transformer, virtually implemented within the sandboxing, and introduces a multiplicative change to the current measurements before they are received by the differential protection scheme via IEC 61850 protocol. Section 1.3.1 of the supporting document provides more details about the scenario related to this<br>dataset. This dataset includes electrical measurements of the current flow, in RMS and sinusoidal format, from the HV and MV sides of HV/MV transformer of the digital twin of the IEEE 9-bus system. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV files which were recorded with a 30-second and 40-second time resolution, respectively. The measurements of RMS values were recorded by the Typhoon controller, while the measurements from the sine waves were recorder by OPAL-RT.</li> </ul>
Figure 6. (a1), (a2), (a3), (a4), (a5), (a6), (a7) and (a8) watermarked image is degraded respectively through JPEG2000 compression, JPEG compression, median filtering, adding Salt&Pepper noise, rotating, center cropping, surrounding cropping and scaling. (b1), (b2), (b3), (b4), (b5), (b6), (b7) and (b8) The corresponding extracted watermarks.-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>This paper has described a scheme for digital watermarking of still images based on discrete<br> wavelet transform. In the proposed method, the embedded logo watermark can be extracted without<br> access to the original image. It has been confirmed that the proposed watermarking method is able<br> to extract the embedded logo watermark from the watermarked images that have degraded through<br> compression, filtering, cropping and scaling. Although this algorithm is not robust against rotation,<br> it can completely extract the watermark from watermarked images that lose about 35% of their<br> areas by cropping attack.</p>
Figure 1. (a) Original watermark (b) extracted watermarks after compression(c) merged watermark-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>Therefore, each bit of the logo watermark is stored in one coefficient of a sub-block to keep<br> the capacity of watermarking fixed.<br> When a region of the watermarked image is destroyed; the whole watermark can be<br> extracted using other regions of the watermarked image by merging extracted watermarks. Figure 1<br> shows result of merging logo watermarks that were extracted from a compressed (with JPEG2000<br> algorithm) watermarked image.</p>
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.