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3,036 results for “ct”
A roadmap to reconstructing muscle architecture from CT data
<div> <span>Skeletal muscle is responsible for voluntary force generation across animals, and muscle architecture largely determines the parameters of mechanical output. The ability to analyze muscle performance through muscle architecture is thus a key step towards better understanding the ecology and evolution of movements and morphologies. In pennate skeletal muscle, volume, fiber lengths and attachment angles to force transmitting structures comprise the most relevant parameters of muscle architecture. Measuring these features through tomographic techniques offers an alternative to tedious and destructive dissections, particularly as the availability of tomographic data is rapidly increasing. However, there is a need for streamlined computational methods to access this information efficiently. Here, we establish and compare workflows using partially automated image analysis for fast and accurate estimation of animal muscle architecture. After isolating a target muscle through segmentation, we evaluate freely available and proprietary fiber tracing algorithms to reconstruct muscle fibers. We then present a script using the Blender Python API to estimate attachment angles, fiber lengths, muscle volume and Physiological Cross-Sectional Area. We apply these methods to insect and vertebrate muscle and provide guided workflows. Results from fiber tracing are consistent compared to manual measurements but much less time-consuming. Lastly, we emphasize the capabilities of the open-source 3D software Blender as both a tool for visualization and a scriptable analytic tool to process digitized anatomical data. Across organisms, it is feasible to extract, analyze, and visualize muscle architecture from tomography data by exploiting the spatial features of scans and the geometric properties of muscle fibers. As digital libraries of anatomies continue to grow, the workflows and approach presented here can be part of the open-source future of digital comparative analysis.</span> </div>
CT-FAN: A Multilingual dataset for Fake News Detection
<p><strong>By downloading the data, you agree with the terms & conditions mentioned below:</strong></p> <p><strong>Data Access: </strong>The data in the research collection may only be used for research purposes. Portions of the data are copyrighted and have commercial value as data, so you must be careful to use them only for research purposes. </p> <p>Summaries, analyses and interpretations of the linguistic properties of the information may be derived and published, provided it is impossible to reconstruct the information from these summaries. You may not try identifying the individuals whose texts are included in this dataset. You may not try to identify the original entry on the fact-checking site. You are not permitted to publish any portion of the dataset besides summary statistics or share it with anyone else.</p> <p>We grant you the right to access the collection's content as described in this agreement. You may not otherwise make unauthorised commercial use of, reproduce, prepare derivative works, distribute copies, perform, or publicly display the collection or parts of it. You are responsible for keeping and storing the data in a way that others cannot access. The data is provided free of charge.</p> <p><strong>Citation</strong></p> <p>Please cite our work as</p> <pre>@InProceedings{clef-checkthat:2022:task3, author = {K{\"o}hler, Juliane and Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Wiegand, Michael and Siegel, Melanie and Mandl, Thomas}, title = "Overview of the {CLEF}-2022 {CheckThat}! Lab Task 3 on Fake News Detection", year = {2022}, booktitle = "Working Notes of CLEF 2022---Conference and Labs of the Evaluation Forum", series = {CLEF~'2022}, address = {Bologna, Italy},} </pre> <pre>@article{shahi2021overview, title={Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection}, author={Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Mandl, Thomas}, journal={Working Notes of CLEF}, year={2021} }</pre> <p><strong>Problem Definition:</strong> Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other (e.g., claims in dispute) and detect the topical domain of the article. This task will run in <strong>English and German.</strong></p> <p><strong>Task 3:</strong> <strong>Multi-class fake news detection of news articles (English)</strong> Sub-task A would detect fake news designed as a four-class classification problem. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. The training data will be released in batches and roughly about 1264 articles with the respective label in English language. Our definitions for the categories are as follows:</p> <ul> <li> <p>False - The main claim made in an article is untrue.</p> </li> <li> <p>Partially False - The main claim of an article is a mixture of true and false information. The article contains partially true and partially false information but cannot be considered 100% true. It includes all articles in categories like partially false, partially true, mostly true, miscaptioned, misleading etc., as defined by different fact-checking services.</p> </li> <li> <p>True - This rating indicates that the primary elements of the main claim are demonstrably true.</p> </li> <li> <p>Other- An article that cannot be categorised as true, false, or partially false due to a lack of evidence about its claims. This category includes articles in dispute and unproven articles.</p> </li> </ul> <p><strong>Cross-Lingual Task (German)</strong></p> <p>Along with the multi-class task for the English language, we have introduced a task for low-resourced language. We will provide the data for the test in the German language. The idea of the task is to use the English data and the concept of transfer to build a classification model for the German language.</p> <p><strong>Input Data</strong></p> <p>The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows:</p> <ul> <li>ID- Unique identifier of the news article</li> <li>Title- Title of the news article</li> <li>text- Text mentioned inside the news article</li> <li>our rating - class of the news article as false, partially false, true, other</li> </ul> <p><strong>Output data format</strong></p> <ul> <li>public_id- Unique identifier of the news article</li> <li>predicted_rating- predicted class</li> </ul> <p>Sample File</p> <pre><code>public_id, predicted_rating 1, false 2, true</code></pre> <p><strong>IMPORTANT! </strong></p> <ol> <li>We have used the data from 2010 to 2022, and the content of fake news is mixed up with several topics like elections, COVID-19 etc.</li> </ol> <p><strong>Baseline:</strong> For this task, we have created a baseline system. The baseline system can be found at <a href="https://zenodo.org/record/6362498">https://zenodo.org/record/6362498</a></p> <p><strong>Related Work</strong></p> <ul> <li>Shahi GK. AMUSED: An Annotation Framework of Multi-modal Social Media Data. arXiv preprint arXiv:2010.00502. 2020 Oct 1.<a href="https://arxiv.org/pdf/2010.00502.pdf">https://arxiv.org/pdf/2010.00502.pdf</a></li> <li>G. K. Shahi and D. Nandini, “FakeCovid – a multilingual cross-domain fact check news dataset for covid-19,” in workshop Proceedings of the 14th International AAAI Conference on Web and Social Media, 2020. <a href="http://workshop-proceedings.icwsm.org/abstract?id=2020_14">http://workshop-proceedings.icwsm.org/abstract?id=2020_14</a></li> <li>Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. <em>Online Social Networks and Media</em>, <em>22</em>, 100104. doi: <a href="https://dx.doi.org/10.1016%2Fj.osnem.2020.100104">10.1016/j.osnem.2020.100104</a></li> <li>Shahi, G. K., Struß, J. M., & Mandl, T. (2021). Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection. <em>Working Notes of CLEF</em>.</li> <li>Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeno, A., Míguez, R., Shaar, S., ... & Mandl, T. (2021, March). The CLEF-2021 CheckThat! lab on detecting check-worthy claims, previously fact-checked claims, and fake news. In <em>European Conference on Information Retrieval</em> (pp. 639-649). Springer, Cham.</li> <li>Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeño, A., Míguez, R., Shaar, S., ... & Kartal, Y. S. (2021, September). Overview of the CLEF–2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News. In <em>International Conference of the Cross-Language Evaluation Forum for European Languages</em> (pp. 264-291). Springer, Cham.</li> </ul>
Micro-CT abdominal reconstruction of a worker honeybee abdomen
<p><strong>Micro-CT abdominal reconstruction of worker honeybees.</strong><em> </em></p> <p>The samples were obtained from a honeybee colony located in Cortijo Tinajas (Lecrín, Granada, Spain; coordinates: 36°55'25.1"N 3°31'42.9"W; 36.923645,-3.528575) and kindly provided by Apinevada S. L. Micro-CT scans were carried out at the Centro de Instrumentación Científica (University of Granada, Granada, UGR), using a Xradia 510 VERSA (ZEISS) (Xradia 510 VERSA (ZEISS). The following settings were established to get the same resolution of all samples: 4X magnification, 6.1515 μm pixel size, 200.021 mm mm source-sample distance, 20 mm detector-sample distance, and 2034 images.</p>
Confocal and micro-CT data of Rhomeocalpsua torosa, holotype, NIGP180054
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Rhomeocalpsua torosa</em> (NIGP180054) from mid-Cretaceous Burmese amber. This dataset is associated with the publication "<em>Rhomeocalpsua torosa</em> gen. et sp. nov., a unique lineage of Endomychidae from mid‑Cretaceous Burmese amber (Coleoptera: Coccinelloidea)" (DOI:10.11646/palaeoentomology.5.2.7).</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm (Argon) or 566 nm (DPSS 561-10) laser excitation lines. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</p> <p>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 1.9324 μm; power, 4 W; acceleration voltage, 50 kV; exposure time, 1.5 s; projections, 3001. The TIFF stack is provided.</p>
Confocal and micro-CT data of Cretakarenni shaoi, holotype, NIGP177331
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Cretakarenni shaoi</em> (NIGP177331) from mid-Cretaceous Burmese amber. Additional confocal data for <em>Cretakarenni birmanicus</em> (QUST-INSECT-0003) are also provided. This dataset is associated with the publication "Minute clubbed beetles with cephalic horns in mid-Cretaceous amber from northern Myanmar (Coleoptera: Monotomidae)" (DOI:10.11865/zs.2022206).</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm Argon laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</p> <p>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 2.0815 μm; power, 4 W; acceleration voltage, 50 kV; exposure time, 3 s; projections, 2801. The TIFF stack is provided.</p>
Video 2. Whole honeybee abdomen Micro-CT
<p>Videos of the whole bee abdomen using a collection of<strong> </strong> 2D images for 3D stacking. Micro-CT scans were carried out at the Centro de Instrumentación Científica (University of Granada, Granada, UGR), using a Xradia 510 VERSA (ZEISS) (Xradia 510 VERSA (ZEISS). The following settings were established to get the same resolution of all samples: 4X magnification, 6.1515 μm voxel size, 200.021 mm mm source-sample distance, 20 mm detector-sample distance, BIN 1 (2048x2048 pixels on CCD), and 2034 projections for whole bee abdomen. Voltage, current, filter and exposure time were adjusted according to features of the samples as follows: bee whole abdomen: 60 kV accelerating voltage (a.v.), 83 μA beam current (b.c.), 40 s exposure time (e.t.) and LE2 source filter (s.f.).</p>
Seeds in box micro-CT dataset
<p>Nikon XTH225 micro-CT dataset of sunflower seeds in an acrylic box.</p> <p>1572 projections size 1000x1000 pixels in TIFF-format, over 0-360 degrees both included, cone-beam geometry.</p> <p>Scan geometry meta data in .xtekct file. Rotation axis has a small tilt, see information from dual-slice centering in parameters CentreOfRotationTop and CentreOfRotationBottom in the file "47209 testscan korn01_recon.xtekct".</p> <p>Only raw projections provided, no reconstruction based on vendor software.</p>
Confocal and micro-CT data of Xenostanus jiangkuni, holotype, NIGP177782
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Xenostanus</em><strong> </strong><em>jiangkuni</em> (NIGP177782) from mid-Cretaceous Burmese amber. This dataset is associated with the publication "Earliest fossil record of Corylophidae from Burmese amber and the phylogeny of Corylophidae (Coleoptera: Coccinelloidea)" (DOI:10.3897/asp.80.e81736).</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm (Argon) laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</p> <p>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 1.6106 μm; power, 3 W; acceleration voltage, 40 kV; exposure time, 1.5 s; projections, 3001. The TIFF stack is provided.</p>
FOD CT Data: air pockets in avocado and stone in modelling clay
<p><strong>Summary</strong></p> <p>This submission contains X-ray CT data of avocado fruits and pieces of modelling clay containing pebble stones.<br> Data for every object include binned pre-processed projections and volume segmentations.<br> These datasets can be used for training and testing deep learning methods for foreign object detection.</p> <p>The data is made available as a part of the paper "CT-based data generation for foreign object detection on a single X-ray projection".</p> <p><strong>Data acquisition</strong></p> <p>A majority of raw data for modeling clay (excluding 10 samples without pebble stones in the Test subset) is taken from the dataset<br> "A collection of 131 CT datasets of pieces of modeling clay containing stones"<br> [](https://doi.org/10.5281/zenodo.5866228)</p> <p>The remaining pieces of modeling clay and all avocado fruits were scanned at the FleX-ray laboratory<br> of the Centrum Wiskunde & Informatica (CWI) in Amsterdam, the Netherlands (details can be found in [Coban 2020]).<br> For every fruit, we made scans with significantly different amounts of air pockets by waiting for a few days between experimental acquisitions.<br> The measurements were performed with the voltage of 90 kV, power of 45 W, exposure time of 300 ms per projection, and magnification factor of 1.3.<br> The original X-ray image size was 1912 px x 1520 px with a pixel size of 75 μm, 1440 images were acquired for every sample.<br> For faster deep learning model training, images and reconstructions were downsampled with a factor of 4, leading to the effective pixel size of 300 μm and voxel size of 230 μm.<br> Additional scans of the pieces of modeling clay were acquired with settings similar to the main collection.</p> <p><strong>Data Description</strong></p> <p>The submission is split into "Avocado" and "Playdoh" (pieces of modeling clay) datasets. Each dataset is further split into Training and Test subsets.</p> <p>The folder for every scanned object contains<br> - ./log/ - subfolder with logarithmed X-ray projections after darkfield and flatfield correction.<br> - ./segm/ - subfolder with slices of the segmented volume.<br> - ./scan settings.txt - a file with scanner metadata containing scan geometry<br> - ./volume_info.csv - a file with a voxel count for every class in the segmentation.</p> <p>For playdoh objects, the segmentation classes are modeling clay (Class 1) and pebble stone (Class 2). In this case, pebble stones are foreign objects.</p> <p>For avocado objects, the segmentation classes are peel (Class 1), avocado meat (Class 2), seed (Class 3) and air pockets (Class 4). Air pockets are considered a foreign object.</p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's GitHub page.</p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with<br> - vladyslav.andriiashen [at] cwi.nl</p> <p><strong>References</strong></p> <p>[Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p>
ClinSpEn-CT Data: Parallel English-Spanish Biomedical Terminology
<p><strong>UPDATE August 22nd 2022: </strong>The data in this repository has been merged with the rest of the ClinSpEn data, you may access it here: https://doi.org/10.5281/zenodo.6497350</p> <p>This repository contains the sample, test and background data for the ClinSpEn-Clinical Terms sub-track. The direction of this sub-track is ES>EN.</p> <p>ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content.</p> <p>The terms were directly extracted from medical literature and clinical records, with particular focus on diseases, symptoms, findings, procedures and professions and translated and revised by professional medical translators.</p> <p>The sample set contains 7 000 terms as a tab-separated file (TSV), with the first column corresponding to English terms and the second column to Spanish terms.</p> <p>The test and background data is made up of a TSV file with two columns: term number and Spanish term.</p> <p>Related Links:</p> <p><strong>- Sub-track website with more information: </strong><a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> <p><strong>- WMT website: </strong><a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> <p><strong>- CodaLab: </strong><a href="https://codalab.lisn.upsaclay.fr/competitions/6696">https://codalab.lisn.upsaclay.fr/competitions/6696</a></p> <p> </p> <p><strong>- ClinSpEn-CC (Clinical Cases):</strong> <a href="https://doi.org/10.5281/zenodo.6497350">https://doi.org/10.5281/zenodo.6497350</a></p> <p> </p> <p><strong>- ClinSpEn-CT (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6497372">https://doi.org/10.5281/zenodo.6497372</a></p> <p><strong>- ClinSpEn-OC (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6497388">https://doi.org/10.5281/zenodo.6497388</a></p>
Confocal and micro-CT data of Varcalium lawrencei, holotype, NIGP177336
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Varcalium lawrencei</em> (NIGP177336) from mid-Cretaceous Burmese amber. This dataset is associated with the publication "A new marsh beetle from mid-Cretaceous amber of northern Myanmar (Coleoptera: Scirtidae)" (DOI:10.1038/s41598-022-16822-y).</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm (Argon) laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</p> <p>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 16.916 μm; power, 4 W; acceleration voltage, 50 kV; exposure time, 2 s; projections, 2001. The TIFF stack is provided.</p>
Hyperspectral X-ray CT Voxelized TV reconstruction of a single, iodine-stained lizard head sample
<p><strong>Dataset description</strong></p> <p>These datasets are voxel based reconstructions of hyperspectral CT data using the Core Imaging Library (CIL). They are stored as NeXus files (derived from hdf5) which can be read in, visualised and manipulated using CIL.</p> <p> - PDHG_TV_1000_Sp_alpha_0.004.nxs</p> <p>Is the solution after 1000 iterations of PDHG with TV applied in the spatial domain. </p> <p> - PDHG_TV_1000_SpCh_alpha_0.003_beta_0.5.nxs</p> <p>Is the solution after 1000 iterations of PDHG with TV applied both in the spatial domain, and in the energy (channel) domain. </p> <p> </p> <p><strong>Dataset intended use</strong></p> <p>These datasets are used in the CIL training notebook:</p> <p>https://github.com/TomographicImaging/CIL-Demos/blob/main/examples/3_Multichannel/03_Hyperspectral_reconstruction.ipynb</p> <p>They can be imported using CIL, with the following code snippet:</p> <pre><code class="language-python">from cil.io import NEXUSDataReader reader = NEXUSDataReader(file_name='path/to/data/PDHG_TV_1000_Sp_alpha_0.004.nxs') data = reader.read()</code></pre> <p> </p>
Confocal and micro-CT data of Nothattagenus burmiticus, holotype, NIGP164890
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Nothattagenus burmiticus</em> (NIGP164890) from mid-Cretaceous Burmese amber. This dataset is associated with the publication "'<em>Attagenus</em>' <em>burmiticus</em> from mid-Cretaceous amber reinterpreted as a member of Orphilinae (Coleoptera: Dermestidae)" (DOI:10.11646/palaeoentomology.5.4.12).</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm Argon laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</p> <p>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 2.7017 μm; power, 3 W; acceleration voltage, 40 kV; exposure time, 2.5 s; projections, 2501. The TIFF stack is provided.</p>
Confocal and micro-CT data of Angucharcotes thayerae, holotype, NIGP180055
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Angucharcotes thayerae</em> (NIGP180055) from mid-Cretaceous Burmese amber. This dataset is associated with the publication "Earliest fossil record of Phloeocharinae in mid-Cretaceous amber from northern Myanmar (Coleoptera: Staphylinidae)" (DOI:10.1016/j.cretres.2022.105371).</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm (Argon) laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</p> <p>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 1.4777 μm; power, 3 W; acceleration voltage, 40 kV; exposure time, 5 s; projections, 2801. The TIFF stack is provided.</p>
A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging: geometry and simulation data
<p>This dataset contains the original µCT scan data, the scripts and intermediate results for the generation of the geometrical fiber model, as well as the structural simulation files and their experimental validation data described in the paper <a href="https://journals.sagepub.com/doi/10.1177/00405175221137009">"A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging"</a>, published in Textile Research Journal.</p>
Mechanical alphabet, TM1360 (CT)
Christopher Polhem (1661-1751) created the so-called mechanical alphabet in the years just before 1700. The wooden models illustrated the fundamentals of physics and mechanics and were intended to serve as an educational tool in teachings at the Laboratorium mechanicum established in 1697. After his death, the collection continued to serve as a didactic instrument and became part of the Swedish Royal Model Chamber. In 1925, the collection of 31 models was deposited at the newly established National Museum of Science and Technology. [TM1360](https://digitaltmuseum.se/021026544809/mekaniskt-alfabet-modell?aq=owner%3A%22S-TEK%22&i=30"tm1360") (TEKS0014705) CT-Scan. Created by Kristofer Jansson, Interspectral. Read more about [Polhem]( https://en.wikipedia.org/wiki/Christopher_Polhem"polhem") on Wikipedia. Source: Objaverse 1.0 / Sketchfab
FH2_FRACTESUS CT-0.16T
<div>Fractesus project. Fracture test mini-CT. Raw data 15Kh2MFAA. MTA-EK. </div> <div> <div> <p> </p> </div> </div>
Confocal and micro-CT data of Yassibum yoshitomii, holotype, NIGP200730
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Yassibum yoshitomii</em> (NIGP200730) from mid-Cretaceous Burmese amber. This dataset is associated with the publication "An unusual Cretaceous beetle with affinity to Anamorphidae (Coleoptera: Coccinelloidea)" (DOI: 10.7717/peerj.18008).</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 561 nm (DPSS 561-10) laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</p> <p>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 1.6339 μm; power, 3 W; acceleration voltage, 40 kV; exposure time, 4 s; projections, 3001. The TIFF stack is provided.</p>
Confocal and micro-CT data of Vicelva rasilis, holotype, NIGP166145
<div>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Vicelva rasilis</em> (NIGP166145) from mid-Cretaceous Burmese amber. This dataset is associated with the publication "A new fossil species of the extant genus <em>Vicelva</em> from mid-Cretaceous Kachin amber (Coleoptera: Staphylinidae)" (DOI:10.1002/ece3.11487).</div> <div> </div> <div>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm (Argon) or 561 nm (DPSS 561-10) laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</div> <div> </div> <div>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 6.8202 μm; power, 4 W; acceleration voltage, 50 kV; exposure time, 1.5 s; projections, 2401. The TIFF stack is provided.</div>
Confocal and micro-CT data of Foveapeltis rutai
<div>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the type specimens of <em>Foveapeltis rutai</em> (holotype, NIGP203568, paratypes, IZGAS-BA-COL001 and IZGAS-BA-COL003) from mid-Cretaceous Kachin amber. This dataset is associated with the publication "<em>Foveapeltis</em> gen. nov., an unusual cleroid genus with large hypomeral cavities from mid-Cretaceous amber (Coleoptera: Cleroidea)" (DOI:10.1002/ece3.11589).</div> <div> </div> <div>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm Argon laser excitation line. Both original CZI files and exported TIFF stacks are provided.</div> <div> </div> <div>Micro-CT data for NIGP203568 were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 1.7815 μm; power, 3 W; acceleration voltage, 40 kV; exposure time, 4 s; projections, 3001. The TIFF stacks are provided.</div>
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.