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345 results for “cracking”
Avizo reconstruction of Bone-cracking Borophagus coprolite lacm:vp:158715
<p>Borophagine canids have long been hypothesized to be North American ecological ‘avatars’ of living hyenas in Africa and Asia, but direct fossil evidence of hyena-like bone consumption is hitherto unknown. We report rare coprolites (fossilized feces) of Borophagus parvus from the late Miocene of California and, for the first time, describe unambiguous evidence that these predatory canids ingested large amounts of bone. Surface morphology, micro-CT analyses, and contextual information reveal (1) droppings in concentrations signifying scent-marking behavior, similar to latrines used by living social carnivorans; (2) routine consumption of skeletons; (3) undissolved bones inside coprolites indicating gastrointestinal similarity to modern striped and brown hyenas; (4) B. parvus body weight of ~24 kg, reaching sizes of obligatory large-prey hunters; and (5) prey size ranging ~35–100 kg. This combination of traits suggests that bone-crushing Borophagus potentially hunted in collaborative social groups and occupied a niche no longer present in North American ecosystems.</p>
Fused Image dataset for convolutional neural Network-based crack Detection (FIND)
<p>The “<strong>F</strong>used <strong>I</strong>mage dataset for convolutional neural <strong>N</strong>etwork-based crack <strong>D</strong>etection” (<strong>FIND</strong>) is a large-scale image dataset with pixel-level ground truth crack data for deep learning-based crack segmentation analysis. It features four types of image data including raw intensity image, raw range (i.e., elevation) image, filtered range image, and fused raw image. The FIND dataset consists of 2500 image patches (dimension: 256x256 pixels) and their ground truth crack maps for each of the four data types.</p> <p>The images contained in this dataset were collected from multiple bridge decks and roadways under real-world conditions. A laser scanning device was adopted for data acquisition such that the captured raw intensity and raw range images have pixel-to-pixel location correspondence (i.e., spatial co-registration feature). The filtered range data were generated by applying frequency domain filtering to eliminate image disturbances (e.g., surface variations, and grooved patterns) from the raw range data [1]. The fused image data were obtained by combining the raw range and raw intensity data to achieve cross-domain feature correlation [2,3]. Please refer to [4] for a comprehensive benchmark study performed using the FIND dataset to investigate the impact from different types of image data on deep convolutional neural network (DCNN) performance.</p> <p>If you share or use this dataset, please cite [4] and [5] in any relevant documentation. </p> <p>In addition, an image dataset for crack classification has also been published at [6].</p> <p>References:</p> <p>[1] Shanglian Zhou, & Wei Song. (2020). Robust Image-Based Surface Crack Detection Using Range Data. Journal of Computing in Civil Engineering, 34(2), 04019054. <a href="https://doi.org/10.1061/(asce)cp.1943-5487.0000873">https://doi.org/10.1061/(asce)cp.1943-5487.0000873</a></p> <p>[2] Shanglian Zhou, & Wei Song. (2021). Crack segmentation through deep convolutional neural networks and heterogeneous image fusion. Automation in Construction, 125. <a href="https://doi.org/10.1016/j.autcon.2021.103605">https://doi.org/10.1016/j.autcon.2021.103605</a></p> <p>[3] Shanglian Zhou, & Wei Song. (2020). Deep learning–based roadway crack classification with heterogeneous image data fusion. Structural Health Monitoring, 20(3), 1274-1293. <a href="https://doi.org/10.1177/1475921720948434">https://doi.org/10.1177/1475921720948434</a> </p> <p>[4] Shanglian Zhou, Carlos Canchila, & Wei Song. (2023). Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance. Automation in Construction, 146. <a href="https://doi.org/10.1016/j.autcon.2022.104678">https://doi.org/10.1016/j.autcon.2022.104678</a></p> <p>[5] (<strong>This dataset</strong>) Shanglian Zhou, Carlos Canchila, & Wei Song. (2022). Fused Image dataset for convolutional neural Network-based crack Detection (FIND) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6383044">https://doi.org/10.5281/zenodo.6383044</a></p> <p>[6] Wei Song, & Shanglian Zhou. (2020). Laser-scanned roadway range image dataset (LRRD). Laser-scanned Range Image Dataset from Asphalt and Concrete Roadways for DCNN-based Crack Classification, DesignSafe-CI. <a href="https://doi.org/10.17603/ds2-bzv3-nc78">https://doi.org/10.17603/ds2-bzv3-nc78</a></p>
Bridge Cracks Monitoring: Detection, Measurement, and Comparison using Augmented Reality
<p>Crack occurrence and propagation are among critical factors that affect the performance and lifespan of civil infrastructures such as bridges. Consequently, numerous crack detection and measurement methods have been proposed and developed in the recent decades in the areas of Structural Health Monitoring and non-destructive testing. Many novel technologies have emerged with the potential to overcome the limitations of the presented techniques of crack detection and characterization. Crack detection and characterization method used in this research lies in supplementing human visual inspection capabilities in a systematic manner through an appropriate level of automation. The Augmented Reality (AR) tool developed in this project allows a user to perform tasks in a real-world environment while visually receiving supplementary 3D computer-generated information to support the tasks. More specifically, we developed a crack detection/characterization tool in this research and deployed it in Microsoft HoloLens smart glasses. This AR tool provides the user with automatic data collection capability through AR headset camera and is a means of hands-free data sharing for inspectors while conducting their normal inspection. We conducted several laboratory and field experiments by which we evaluated the effectiveness of the developed crack detection and measurement system. The result confirm that the AR tool devised in this project has the potential to help the inspection process in terms of time, comfort and accuracy.</p>
Rapid Repair of Cracks on the Embankment Slopes Using Bio-Cement
<p>This research explored the feasibility of using Microbially Induced Carbonate Precipitation (MICP) to improve fine-grained soil mechanical properties, seal the soil cracks, and assess the improvement of MICP on slope stability. The conducted research tasks include (1) direct shear tests to investigate the mechanical behavior and biogeochemical reactions of low-plasticity silt treated by MICP, (2) cyclic wetting-drying tests to assess the feasibility of using MICP to seal and waterproof the soil cracks, and (3) SLOPE/W modeling of a slope treated by MICP. Direct shear tests were used to evaluate the shear responses of the low-plasticity silt under different overburden pressures (12, 25, and 35 kPa) and different bio-cement treatments. A series of cyclic wetting-drying tests were used to assess the effectiveness of MICP treatment on healing soil cracks. Crack lengths, area, width, and area percentage were measured and compared before and after the MICP treatment. SLOPE/W analysis was performed to assess the factor of safety of a slope under MICP treatment. The direct shear tests results show that the peak shear strengths increased by an average of 30% from the untreated to the MICP-treated soil samples. The wetting-drying cycle tests results show that MICP treatment can heal desiccation cracks, reducing crack length, crack width, and crack area. The results of the SLOPE/W modeling show that the MICP treatment had a positive effect on the improvement of slope stability, but more field tests are needed for optimizing the treatment solutions and procedures and assessing the long-term effect and ecological impacts.</p>
Full-field displacements and strains obtained by digital image correlation during fatigue crack growth experiments
<p>This data publication contains full-field displacements and strains obtained by 3D digital image correlation (DIC) using a GOM Aramis 12M system including three fatigue crack propagation (fcp) experiments of AA2024-T3 aluminium sheet material.</p> <p>The repository consists of three datasets of different experiments named </p> <ul> <li>S<sub>950,1.6 </sub>- MT950 specimen, 1.6 mm sheet thickness, load ratios R=0.3, 1.0</li> <li>S<sub>160,2.0 </sub>- MT160 specimen, 2.0 mm sheet thickness, load ratios R=0.1, 0.25, 0.5, 0.75, 1.0</li> <li>S<sub>160,4.7 </sub>- MT160 specimen, 4.7 mm sheet thickness, load ratios R=0.1, 0.25, 0.5, 0.75, 1.0</li> </ul> <p>where S<sub>w,t</sub> denotes a middle tension (MT) specimen with width w and thickness t. The nodal DIC measurements at different times during the experiments are provided as .txt files we call <em>"nodemaps" </em>and stored in subfolders "<strong>Nodemaps</strong>". Each <em>nodemap</em> consists of a header containing meta data information like a running number (current stage index) or the applied force, followed by the nodal displacements and strains in tabular form. Additionally, the dataset S<sub>160,4.7</sub> contains crack path and crack tip labels for each nodemap in the subfolder "<strong>GroundTruth</strong>". The ground truth is provided as arrays of size 256x256. Each pixel of the array contains the label "2" for the class "crack tip", "1" for the class "crack path", or "0" for the class "background". These labels were created in a semi-manual fashion and can be used for machine learned crack detection using supervised training.</p> <p>These datasets were recently used to evaluate neural attention of convolutional neural networks trained on fatigue crack tip detection in <a href="https://www.nature.com/articles/s41598-022-13275-1">Melching et al. (Sci Rep, 2022)</a>. Additional guidance on data loading and usage can also be found at <a href="https://github.com/dlr-wf/explainable-crack-tip-detection">https://github.com/dlr-wf/explainable-crack-tip-detection</a>.</p> <p>The experiments S<sub>160,2.0</sub> and S<sub>160,4.7 </sub>were conducted and analysed by <a href="https://doi.org/10.1111/ffe.13433">Strohmann et al. (FFEMS, 2021)</a>.</p> <p>The experiment S<sub>950,1.6</sub> was conducted and analysed by <a href="https://doi.org/10.1111/ffe.13335">Breitbarth et al. (FFEMS, 2020)</a>.</p>
Supplemental Material to Article "Validation of crack initiation model by means of cyclic full-scale blade test"
<p>This set supplements the figure data to the article "Validation of crack initiation model by means of cyclic full-scale blade test", DOI: <a href="https://doi.org/10.1088/1742-6596/2265/3/032045">https://doi.org/10.1088/1742-6596/2265/3/032045</a>.</p>
Dataset for determining crack kinematics from imaged crack patterns
<p>This is the dataset used to assess the performance of the crack kinematics algorithm proposed by Pantoja-Rosero et, al (2022) in the article "Determining crack kinematics from imaged crack patterns".</p>
Dataset for TOPO-Loss for continuity-preserving crack detection using deep learning
<p>This is the dataset used to assess the performance of the crack detection algorithm proposed by Pantoja-Rosero et, al (2022) in the article "TOPO-Loss for continuity-preserving crack detection using deep learning" (https://doi.org/10.1016/j.conbuildmat.2022.128264)</p>
High temperature properties incuding creep, creep crack growth rate and thermal fatigue linked with chemical composition of alloys derived from 1.4848 refractory stainless steels
<p>The following set of data results from cast alloys modifying chemical composition taking as reference 1.4848 alloy and getting sound samples that have been tested to calculate creep, creep crack growth rate and thermal fatigue data. </p>
TUBERS - Ultrasonic and Synthetic Crack Datasets
Open the record for dataset details and reuse information.
Dataset for: Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning." Automation in Construction 143 (2022): 104575. https://doi.org/10.1016/j.autcon.2022.104575.</p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (txt files): <ul> <li>00-Label_meaning.txt: the meaning of label number</li> <li>01-All_label.txt: Image, label (severity level)</li> <li>02-Train_label.txt: the training set: Image, Label (severity level)</li> <li>02-Test_label.txt: the test set: Image, Label (severity level)</li> </ul> </li> </ul>
Dataset for: Asphalt pavement crack detection based on convolutional neural network and infrared thermography
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Asphalt pavement crack detection based on convolutional neural network and infrared thermography." IEEE Transactions on Intelligent Transportation Systems 23, no. 11 (2022): 22145-22155. https://doi.org/10.1109/TITS.2022.3142393. </p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (binary images)</li> </ul>
Dataset for: Deep learning and infrared thermography for asphalt pavement crack severity classification
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Deep learning and infrared thermography for asphalt pavement crack severity classification." Automation in Construction 140 (2022): 104383. https://doi.org/10.1016/j.autcon.2022.104383. </p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (txt files): <ul> <li>00-Label_meaning.txt: the meaning of label number</li> <li>01-All_label.txt: Image, label (severity level)</li> <li>02-Train_label.txt: the training set: Image, Label (severity level)</li> <li>02-Test_label.txt: the test set: Image, Label (severity level)</li> </ul> </li> </ul>
FIGURE 85 in A Revision of the Malagasy Crack-Leg Spiders of the Genus Uduba Simon, 1880 (Araneae, Udubidae), with Description of 35 New Species from Madagascar
FIGURE 85. All locality records (stars) from this study for Uduba in Madagascar.
Crack propagation in heterogenous radiated material.
<p>Plot representing equations in string form used to implicitly propagate a crack in radiated material.</p>
Supplemental Material to Journal Article "Tunneling Crack Initiation in Trailing-Edge Bond Lines of Wind-Turbine Blades"
<p>This set supplements the figure data to the article "Tunneling Crack Initiation in Trailing-Edge Bond Lines of Wind-Turbine Blades", DOI: <a href="http://doi.org/10.2514/1.J058179">10.2514/1.J058179</a>.</p>
Dataset for Label-free and Sensitive Detection of Citrus Bark Cracking Viroid in Hop Using Ti3C2Tx MXene-modified Genosensor
<p>This is a dataset for a paper "Label-free and Sensitive Detection of Citrus Bark Cracking Viroid in Hop Using Ti3C2Tx MXene-modified Genosensor". All details about the data are included in the readme file.</p>
Dataset for article "Prediction of thermal shock induced cracking in multi-material ceramics using a stress-energy criterion" published in Engineering Fracture Mechanics
<p>Dataset contains graphical outputs of the numerical analysis performed using Finite element method in finite elements software Ansys Mechanical. It contains also photoes of the tested specimens. Further, material data used for the numerical analysis and measured by authors are included in the csv file and all necessary input codes for the FE system Ansys creating data for graphs in the publication are provided in the subfolder "Models-Ansys" within "Data" directory.</p> <p> </p>
Table 1 in On new genera and species of crack-leg spiders (Araneae, Udubidae) from Madagascar
<p><b>Table 1.</b> List of the <i>Zorascar</i> gen. nov. specimens sequenced for this study, with information on the sex/stage (M = male adult; F = female adult; juv. = juvenile), type specimen (Ht = holotype; Pt = paratype) collection number, DNA working code and GenBank accession number for COI and 16S rRNA markers.</p><table><tbody><tr><th><b>Species</b></th><th><b>Stage</b></th><th><b>Type</b></th><th><b>RMCA number</b></th><th><b>DNA</b></th><th><b>GenBank number</b></th></tr><tr><th><b>COI</b></th><th><b>16S</b></th></tr></tbody><tbody><tr><th><i>Z. pasunepipe</i></th><td>M</td><td>Ht</td><td>BE_RMCA_ARA.Ara.247343</td><td>RJ5</td><td>OK561858</td><td>OK564657</td></tr><tr><th><i>Z. pasunepipe</i></th><td>juv.</td><td>Pt</td><td>BE_RMCA_ARA.Ara.247344</td><td>RJ1</td><td>OK561857</td><td>OK564656</td></tr><tr><th><i>Z. pasunepomme</i></th><td>F</td><td>Ht</td><td>BE_RMCA_ARA.Ara.247346</td><td>RJ4</td><td>OK561861</td><td>OK564660</td></tr><tr><th><i>Z. pasunepomme</i></th><td>juv.</td><td>Pt</td><td>BE_RMCA_ARA.Ara.247349</td><td>RJ2</td><td>OK561859</td><td>OK564658</td></tr><tr><th><i>Z. pasunepomme</i></th><td>juv.</td><td>Pt</td><td>BE_RMCA_ARA.Ara.247348</td><td>RJ3</td><td>OK561860</td><td>OK564659</td></tr></tbody></table>
Appendix 2 in On new genera and species of crack-leg spiders (Araneae, Udubidae) from Madagascar
<p><b>Appendix 2</b> (continued on next page). GenBank sequences included in the phylogenetic analyses. In <b>blod</b> specimens belonging to species newly described in this work.</p><table><tbody><tr><th><b>Familly</b></th><th><i>Species</i></th><th><b>isolate</b></th><th><b>Reference</b></th><th><b>Collection reference</b></th><th><b>Locality</b></th><th><b>COI</b></th><th><b>16S</b></th></tr></tbody><tbody><tr><th>Ctenidae</th><td><i>Ancylometes bogotensis</i></td><td>ARACG256</td><td>Wheeler <i>et al.</i> 2017</td><td>CASENT 9021737</td><td>French Guiana</td><td>KY017632</td><td>KY015784</td></tr><tr><th>Senoculidae</th><td><i>Senoculus cf. iricolor</i></td><td>ARAMR700</td><td>Wheeler <i>et al.</i> 2017</td><td>MACN-Ar 11315</td><td>Argentina</td><td>KY017912</td><td>KY016110</td></tr><tr><th>Senoculidae</th><td><i>Senoculus</i> sp.</td><td>DP-2014</td><td>Polotow <i>et al.</i> 2015</td><td>UFMG 3250</td><td>Brazil</td><td>KM225122</td><td>/</td></tr><tr><th>Udubidae</th><td><i>Raecius asper</i></td><td>496-ARAMR496</td><td>Polotow <i>et al.</i> 2015, Wheeler <i>et al.</i> 2017</td><td>AMNH</td><td>Cameroon</td><td>KM225121- KY018035</td><td>KY016220</td></tr><tr><th>Udubidae</th><td><i>Uduba platnicki</i></td><td>DP-2014</td><td>Polotow <i>et al.</i> 2015</td><td>CASENT 9030253</td><td>Madagascar</td><td>KM225129</td><td>/</td></tr><tr><th>Udubidae</th><td><i>Uduba kavanaughi</i></td><td>ARACG301</td><td>Wheeler <i>et al.</i> 2017</td><td>CASENT 9024063</td><td>Madagascar</td><td>KY018036</td><td>KY016221</td></tr><tr><th>Udubidae</th><td><i>Tabiboka milleri</i> gen. et sp. nov.</td><td>ARACG300</td><td>Wheeler <i>et al.</i> 2017</td><td>CASENT 9024004</td><td>Madagascar</td><td>KY018037</td><td>/</td></tr><tr><th>Udubidae</th><td><i>Tabiboka milloti</i> gen. et sp. nov.</td><td>ARACG045</td><td>Wheeler <i>et al.</i> 2017</td><td>CASENT 9005657</td><td>Madagascar</td><td>KY018038</td><td>KY016222</td></tr><tr><th>Udubidae</th><td><i>Zorodictyna silvadavilae</i> sp. nov.</td><td>ARACG047</td><td>Wheeler <i>et al.</i> 2017</td><td>CASENT 9014998</td><td>Madagascar</td><td>KY018039</td><td>KY016223</td></tr><tr><th>Udubidae</th><td><i>Zorodictyna almae</i> sp. nov.</td><td>DP-2014d</td><td>Polotow <i>et al.</i> 2015</td><td>CASENT 9031271</td><td>Madagascar</td><td>KM225137</td><td>/</td></tr><tr><th>Udubidae</th><td><i>Tabiboka milleri</i> gen. et sp. nov.</td><td>DP-2014c</td><td>Polotow <i>et al.</i> 2015</td><td>CASENT 9035866</td><td>Madagascar</td><td>KM225136</td><td>/</td></tr><tr><th>Udubidae</th><td><i>Tabiboka polotowae</i> gen. et sp. nov.</td><td>DP-2014b</td><td>Polotow <i>et al.</i> 2015</td><td>CASENT 9029890</td><td>Madagascar</td><td>KM225135</td><td>/</td></tr><tr><th>Udubidae</th><td><i>Tabiboka polotowae</i> gen. et sp. nov.</td><td>DP-2014a</td><td>Polotow <i>et al.</i> 2015</td><td>CASENT 9029889</td><td>Madagascar</td><td>KM225134</td><td>/</td></tr><tr><th>Zoropsidae</th><td><i>Australotengella toddae</i></td><td>9023720</td><td>Polotow <i>et al.</i> 2015</td><td>CASENT 9023720</td><td>Australia</td><td>KM225097</td><td>/</td></tr><tr><th>Zoropsidae</th><td><i>Ciniflella</i> sp. ʻ <i>Iguazu</i> ʼ</td><td>ARAMR699</td><td>Wheeler <i>et al.</i> 2017</td><td>MACN-Ar 11314</td><td>Argentina</td><td>KY018070</td><td>KY016252</td></tr><tr><th>Zoropsidae</th><td><i>Griswoldia acaenata</i></td><td>90423202</td><td>Polotow <i>et al.</i> 2015</td><td>CASENT 9043202</td><td>South Africa</td><td>KM225109</td><td>/</td></tr><tr><th>Zoropsidae</th><td><i>Griswoldia disparilis</i></td><td>9024917</td><td>Polotow <i>et al.</i> 2015</td><td>CASENT 9024917</td><td>South Africa</td><td>KM225110</td><td>/</td></tr><tr><th>Zoropsidae</th><td><i>Griswoldia transversa</i></td><td>ARACG304</td><td>Wheeler <i>et al.</i> 2017</td><td>CASENT 9023631</td><td>South Africa</td><td>KY018071</td><td>/</td></tr><tr><th>Zoropsidae</th><td><i>Kilyana hendersoni</i></td><td>9023591 - ARACG307</td><td>Polotow <i>et al.</i> 2015, Wheeler <i>et al.</i> 2017</td><td>CASENT 9023591 - CASENT 9023718</td><td>Australia</td><td>KM225112- KY018072</td><td>KY016253</td></tr><tr><th>Zoropsidae</th><td><i>Kilyana</i> sp.</td><td>ARACG306</td><td>Wheeler <i>et al.</i> 2017</td><td>CASENT 9023680</td><td>Australia</td><td>KY018073</td><td>KY016254</td></tr><tr><th>Zoropsidae</th><td><i>Liocranoides archeri</i></td><td>ARAMH011</td><td>Wheeler <i>et al.</i> 2017</td><td>MACN_MCH 05_057</td><td>USA</td><td>KY018074</td><td>KY016255</td></tr><tr><th>Zoropsidae</th><td><i>Phanotea digitata</i></td><td>9043274</td><td>Polotow <i>et al.</i> 2015</td><td>CASENT 9043274</td><td>South Africa</td><td>KM225119</td><td>/</td></tr><tr><th>Zoropsidae</th><td><i>Phanotea sathegyna</i></td><td>ARACG309</td><td>Wheeler <i>et al.</i> 2017</td><td>CASENT 9023618</td><td>South Africa</td><td>KY018075</td><td>KY016256</td></tr><tr><th>Zoropsidae</th><td><i>Tengella radiata</i></td><td>ARASP070</td><td>Wheeler <i>et al.</i> 2017</td><td>AMNH-USNM</td><td>Costa-Rica</td><td>KY018076</td><td>KY016257</td></tr><tr><th>Zoropsidae</th><td><i>Tengella</i> sp.</td><td>DP-2014</td><td>Polotow <i>et al.</i> 2015</td><td>CAS 9047627</td><td>Panama</td><td>KM225123</td><td>/</td></tr><tr><th>Zoropsidae</th><td><i>Titiotus</i> sp.</td><td>DP-2014</td><td>Polotow <i>et al.</i> 2015</td><td>CASENT 9047630</td><td>USA</td><td>KM225126</td><td>/</td></tr><tr><th>Zoropsidae</th><td><i>Uliodon frenatus</i></td><td>9047620</td><td>Polotow <i>et al.</i> 2015</td><td>CASENT 9047620</td><td>New Zealand</td><td>KM225130</td><td>/</td></tr><tr><th>Zoropsidae</th><td><i>Uliodon</i> sp.</td><td>ARACG127</td><td>Wheeler <i>et al.</i> 2017</td><td>CASENT 9021454</td><td>New Zealand</td><td>KY018078</td><td>KY016259</td></tr><tr><th>Zoropsidae</th><td><i>Uliodon</i> sp.</td><td>ARACG124</td><td>Wheeler <i>et al.</i> 2017</td><td>CASENT 9021450</td><td>New Zealand</td><td>KY018077</td><td>KY016258</td></tr><tr><th>Zoropsidae</th><td><i>Uliodon</i> sp.</td><td>ARACG182</td><td>Wheeler <i>et al.</i> 2017</td><td>CASENT 9021453</td><td>New Zealand</td><td>KY018079</td><td>KY016260</td></tr><tr><th>Zoropsidae</th><td><i>Zorocrates fuscus</i></td><td>/</td><td>Blackledge <i>et al.</i> 2009</td><td>AMNH</td><td>Mexico</td><td>FJ607588</td><td>FJ607475</td></tr><tr><th>Zoropsidae</th><td><i>Zorocrates</i> sp.</td><td>ARAMR012</td><td>Wheeler <i>et al.</i> 2017</td><td>AMNH</td><td>Mexico</td><td>KY018080</td><td>KY016261</td></tr><tr><th>Zoropsidae</th><td><i>Zoropsis spinimana</i></td><td>ARACG048</td><td>Wheeler <i>et al.</i> 2017</td><td>CASENT 9019845</td><td>USA</td><td>KY018081</td><td>KY016262</td></tr></tbody></table>
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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.