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3,295 results for “fractures”
Data from: Oral bisphosphonates are associated with increased risk of atypical femoral fractures in elderly women
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Data from: Assessing leg length discrepancy post-total hip arthroplasty for neck of femur fractures: a retrospective analysis
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Data for: The biomechanics of tooth strength: testing the utility of simple models for predicting fracture in geometrically complex teeth
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Bedrock geologic map of Hubbard Brook Experimental Forest and maps of fractures and geology in roadcuts along Interstate-93, Grafton County, New Hampshire.
Bedrock Geologic Map of Hubbard Brook Experimental Forest and maps of fractures and geology in roadcuts along Interstate-93, Grafton County, New Hampshire. Two maps are included in this data set. Interstate I-93 map includes plots summarizing data for fractures shown on map roadcuts. Citation: Barton, C. C., R. H. Camerlo and S. W. Bailey. 1997. Bedrock Geologic Map of Hubbard Brook Experimental Forest and maps of fractures and geology in roadcuts along Interstate-93, Grafton County, New Hampshire. Sheet 1, Scale 1:12,000; Sheet 2, Scale 1:200. U.S. Geological Survey, Miscellaneous Investigations Series, Map I-2562 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
FIGURE 9. Portaratrum birdi n in Two new species of paratanaoid tanaidaceans of the family incertae sedis (Crustacea: Peracarida) from polymetallic nodule fields in the eastern Clarion-Clipperton Fracture Zone
FIGURE 9. Portaratrum birdi n. sp. Holotype female. (A) pleopod-1; (B) uropod. Scale bar: 100 µm.
Dataset of: "Seismic signatures of fractured porous rocks: The partially-saturated case"
<p>This package contains the data of the paper Solazzi et al. (2020). Further information is given in the README file.</p> <p>The software package for the oscillatory relaxation tests, called Parrot, is available upon request to the authors.</p>
RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Tuning/Validation Set)
<p>RibFrac dataset is a benchmark for developping algorithms on rib fracture detection, segmentation and classification. We hope this large-scale dataset could facilitate both clinical research for automatic rib fracture detection and diagnoses, and engineering research for 3D detection, segmentation and classification.</p> <p>This is the Tuning Set (a.k.a. Validation Set in machine learning terminology) of RibFrac dataset, including 80 CTs and the corresponding annotations. Files include:</p> <ol> <li>ribfrac-val-images.zip: 80 chest-abdomen CTs in NII format (nii.gz).</li> <li>ribfrac-val-labels.zip: 80 annotations in NII format (nii.gz).</li> <li>ribfrac-val-info.csv: labels in the annotation NIIs. <ul> <li>public_id: anonymous patient ID to match images and annotations.</li> <li>label_id: discrete label value in the NII annotations.</li> <li>label_code: 0, 1, 2, 3, 4, -1 <ul> <li>0: it is background</li> <li>1: it is a displaced rib fracture</li> <li>2: it is a non-displaced rib fracture</li> <li>3: it is a buckle rib fracture</li> <li>4: it is a segmental rib fracture</li> <li>-1: it is a rib fracture, but we could not define its type due to ambiguity, diagnosis difficulty, etc. Ignore it in the classification task. </li> </ul> </li> </ul> </li> </ol> <p> </p> <p>If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as:</p> <p><em>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-</em><em>Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020). (<a href="https://doi.org/10.1016/j.ebiom.2020.103106">DOI</a>)</em></p> <p>or using bibtex</p> <p><em>@article{ribfrac2020,<br> title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet},<br> author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming},<br> journal={EBioMedicine},<br> year={2020},<br> publisher={Elsevier}<br> }</em></p> <p> </p> <p>The RibFrac dataset is a research effort of thousands of hours by experienced radiologists, computer scientists and engineers. We kindly ask you to respect our effort by appropriate citation and keeping data license.</p> <p> </p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Training Set Part 2)
<p>RibFrac dataset is a benchmark for developping algorithms on rib fracture detection, segmentation and classification. We hope this large-scale dataset could facilitate both clinical research for automatic rib fracture detection and diagnoses, and engineering research for 3D detection, segmentation and classification.</p> <p>Due to size limit of zenodo.org, we split the whole RibFrac Training Set into 2 parts; This is the Training Set Part 2 of RibFrac dataset, including 120 CTs and the corresponding annotations. Files include:</p> <ol> <li>ribfrac-train-images-2.zip: 120 chest-abdomen CTs in NII format (nii.gz).</li> <li>ribfrac-train-labels-2.zip: 120 annotations in NII format (nii.gz).</li> <li>ribfrac-train-info-2.csv: labels in the annotation NIIs. <ul> <li>public_id: anonymous patient ID to match images and annotations.</li> <li>label_id: discrete label value in the NII annotations.</li> <li>label_code: 0, 1, 2, 3, 4, -1 <ul> <li>0: it is background</li> <li>1: it is a displaced rib fracture</li> <li>2: it is a non-displaced rib fracture</li> <li>3: it is a buckle rib fracture</li> <li>4: it is a segmental rib fracture</li> <li>-1: it is a rib fracture, but we could not define its type due to ambiguity, diagnosis difficulty, etc. Ignore it in the classification task. </li> </ul> </li> </ul> </li> </ol> <p> </p> <p>If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as:</p> <p><em>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-</em><em>Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020). (<a href="https://doi.org/10.1016/j.ebiom.2020.103106">DOI</a>)</em></p> <p>or using bibtex</p> <p><em>@article{ribfrac2020,<br> title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet},<br> author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming},<br> journal={EBioMedicine},<br> year={2020},<br> publisher={Elsevier}<br> }</em></p> <p> </p> <p>The RibFrac dataset is a research effort of thousands of hours by experienced radiologists, computer scientists and engineers. We kindly ask you to respect our effort by appropriate citation and keeping data license.</p> <p> </p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Training Set Part 1)
<p>RibFrac dataset is a benchmark for developping algorithms on rib fracture detection, segmentation and classification. We hope this large-scale dataset could facilitate both clinical research for automatic rib fracture detection and diagnoses, and engineering research for 3D detection, segmentation and classification.</p> <p>Due to size limit of zenodo.org, we split the whole RibFrac Training Set into 2 parts; This is the Training Set Part 1 of RibFrac dataset, including 300 CTs and the corresponding annotations. Files include:</p> <ol> <li>ribfrac-train-images-1.zip: 300 chest-abdomen CTs in NII format (nii.gz).</li> <li>ribfrac-train-labels-1.zip: 300 annotations in NII format (nii.gz).</li> <li>ribfrac-train-info-1.csv: labels in the annotation NIIs. <ul> <li>public_id: anonymous patient ID to match images and annotations.</li> <li>label_id: discrete label value in the NII annotations.</li> <li>label_code: 0, 1, 2, 3, 4, -1 <ul> <li>0: it is background</li> <li>1: it is a displaced rib fracture</li> <li>2: it is a non-displaced rib fracture</li> <li>3: it is a buckle rib fracture</li> <li>4: it is a segmental rib fracture</li> <li>-1: it is a rib fracture, but we could not define its type due to ambiguity, diagnosis difficulty, etc. Ignore it in the classification task. </li> </ul> </li> </ul> </li> </ol> <p> </p> <p>If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as:</p> <p><em>Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-</em><em>Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020). (<a href="https://doi.org/10.1016/j.ebiom.2020.103106">DOI</a>)</em></p> <p>or using bibtex</p> <p><em>@article{ribfrac2020,<br> title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet},<br> author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming},<br> journal={EBioMedicine},<br> year={2020},<br> publisher={Elsevier}<br> }</em></p> <p> </p> <p>The RibFrac dataset is a research effort of thousands of hours by experienced radiologists, computer scientists and engineers. We kindly ask you to respect our effort by appropriate citation and keeping data license.</p> <p> </p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
Influences of reinforcement and displacement rate on microstructure, mechanical properties and fracture behaviors of cylinder-head aluminum alloy
<p>In the present work, the material microstructure, mechanical properties and fracture behaviors of cylinder-head aluminum alloys were experimentally investigated under different displacement rates and with and without the reinforcement. By the addition of nano-clay-particles and the heat treatment, it was tried to improve mechanical properties of the base material. For this objective, after the fabrication of as-cast and nano-composite samples, respectively by gravity and stir-casting; tensile testing was done on standard samples at the displacement rate of 0.1, 1 and 10 mm/min. Then, the sensitivity analysis was performed on experimental data to find quantitatively effects of two parameters of the reinforcement and the displacement rate. In addition, the optical microscopy and the field-emission scanning electron microscopy were utilized for the microstructure and the fracture surface, plus the energy dispersive X-ray analysis. Obtained results indicated that the microstructure of the aluminum alloy was finer due to the reinforcement. Besides, both the ultimate strength and the elongation enhanced. The regression analysis implied that the strength was sensitive only to the reinforcement. Although the elongation was sensitive to both the reinforcement and the displacement rate. Investigations of fracture surfaces showed brittle behaviors with cleavage and quasi-cleavage marks.</p>
Fracture dolomite as an archive of continental palaeo-environmental conditions
<p>Supplementary Information File (raw data; Table 1-8) to the article "Fracture dolomite as an archive of continental palaeo-environmental conditions", published in Communications Earth & Environment</p>
Data from: The fracture failure of granite after varied durations of thermal treatment: an experimental study
Energy-extraction from nuclear materials produces high-level radioactive waste (HLW). In geological nuclear waste storage repositories, the decay of radioactive elements generates heat, exposing the reservoir rocks to high-temperature conditions for long periods. To explore the effects of these conditions, this study examines the ability of granite to resist fracturing after thermal treatment for 10 hours, 10 days, 30 days, and 60 days. The results show that: The fracture toughness of the granite remained basically unchanged with up to 10 days of thermal treatment. After thermal treatment for 60 days, the mode-I, mode-II and mixed-mode (I+II) fracture toughness decreased by 15.39%, 18.07% and 15.18%, respectively, compared with samples heated for 10 hours. The change trends of the ability of granite to resist tensile, shear and mixed (tensile + shear) failure with increased thermal treatment duration were basically consistent. Moreover, there was little change in its brittle fracturing characteristics with increase heating duration. Changes caused to the internal microstructure of the granite by high temperature were ongoing even up to 60 days.
Data from: Study of mixed mode fracture toughness and fracture trajectories in gypsum interlayers in corrosive environment
Based on the engineering background of water dissolving mining for hydrocarbon storage in multi-laminated salt stratum, the mixed mode fracture toughness and fracture trajectory of gypsum interlayers soaked in half-saturated brine at various temperatures (20°C, 50°C and 80°C) were studied by using CSNBD (centrally straight-notched Brazilian disc) specimens with required inclination angles (0°, 7°, 15°, 22°, 30°, 45°, 60°, 75°, 90°) and SEM (scanning electron microscopy). The results showed: (i) The fracture load of gypsum specimens first decreased then increased with increasing inclination angle, due to the effect of friction coefficient. When soaked in brine, the fracture toughness of gypsum specimens gradually decreased with increasing brine temperature. (ii) When soaked in brine, the crystal boundaries of gypsum separated and became clearer, and the boundaries became more open between the crystals with increasing brine temperature. Besides, tensile micro-cracks appeared on the gypsum crystals when soaked in 50°C brine, and the intensity of tensile cracks became more severe when soaking in 80°C brine. (iii) The experimental fracture envelopes derived from the conventional fracture criteria and lay outside these conventional criteria. The experimental fracture envelopes were dependent on the brine temperature and gradually expanded outward as brine temperature increases. (iv) The size of FPZ (fracture process zone) was greatly dependent on the damage degree of materials and gradually increased with increase of brine temperature. The study has important implication for the control of shape and size of salt cavern.
Data from: Diabetes mellitus and the risk of fractures at specific sites: a meta-analysis
Objective: Diabetes mellitus (DM) is associated with an increased fracture risk; however, the impact of DM and subsequent fracture at different sites and the associations according to patient characteristics remain unknown. Design: Meta-analysis. Data Sources: The PubMed, EMBASE, and Cochrane Library databases were searched from inception to March 2018. Eligibility Criteria: We included prospective and retrospective cohort studies on the associations of DM and subsequent fracture risk at different sites. Data extraction and synthesis: Two authors independently extracted data and assessed the study quality. Relative risks (RRs) with 95% confidence intervals (CIs) were calculated using a random-effects model, and the heterogeneity across the included studies was evaluated using I2 and Q statistics. Results: Overall, DM was associated with an increased risk of total (RR: 1.32; 95% CI: 1.17–1.48; P<0.001), hip (RR: 1.77; 95% CI: 1.56–2.02; P<0.001), upper arm (RR: 1.47; 95% CI: 1.02–2.10; P=0.037), and ankle fractures (RR: 1.24; 95% CI: 1.10–1.40; P<0.001), whereas DM had no significant impact on the incidence of distal forearm (RR: 1.02; 95% CI: 0.88–1.19; P=0.809) and vertebral fractures (RR: 1.56; 95% CI: 0.78–3.12; P=0.209). RR ratios suggested that compared with type 2 DM (T2DM) patients, type 1 DM (T1DM) patients had greater risk of total (RR ratio: 1.24; 95% CI: 1.08–1.41; P=0.002), hip (RR ratio: 3.43; 95% CI: 2.27–5.17; P<0.001), and ankle fractures (RR ratio: 1.71; 95% CI: 1.06 –2.78; P=0.029). Although no other significant differences were observed between subgroups, the association of DM with upper arm or ankle, vertebrae, and total fracture differed according to sex, study design, and country, respectively. Conclusions: DM patients had greater risks of total, hip, upper arm, and ankle fractures, with T1DM having a more harmful effect than T2DM.
FIGURE 12 in Description of two new species of munnopsid isopods (Crustacea: Isopoda Asellota) from manganese nodules area of the Clarion-Clipperton Fracture Zone Pacific Ocean
FIGURE 12. Storthyngura yuzhmorgeo sp. nov. holotype male, MIMB 24420. Pereopods 1–7.
FIGURE 6 in Description of two new species of munnopsid isopods (Crustacea: Isopoda Asellota) from manganese nodules area of the Clarion-Clipperton Fracture Zone Pacific Ocean
FIGURE 6. Rectisura slavai sp. nov., holotype female, MIMB 24418. Pereopods 5–7 and pleopods 3–5.
FIGURE 5 in Description of two new species of munnopsid isopods (Crustacea: Isopoda Asellota) from manganese nodules area of the Clarion-Clipperton Fracture Zone Pacific Ocean
FIGURE 5. Rectisura slavai sp. nov., holotype female, MIMB 24418. Pereopods 1–4.
FIGURE 13 in Deep-sea nematodes of the family Microlaimidae from the Clarion-Clipperton Fracture Zone (North-Eastern Tropic Pacific), with the descriptions of three new species*
FIGURE 13. Microlaimus discolensis, specimen No. 1, male, total view. Scale bar = 100 µm.
FIGURE 6 in Deep-sea nematodes of the family Microlaimidae from the Clarion-Clipperton Fracture Zone (North-Eastern Tropic Pacific), with the descriptions of three new species*
FIGURE 6. Caligocanna mirabilis, female, specimen No. 7, a total view. Scale bar = 50 µm.
FIGURE 1 in Deep-sea nematodes of the family Microlaimidae from the Clarion-Clipperton Fracture Zone (North-Eastern Tropic Pacific), with the descriptions of three new species*
FIGURE 1. Sampling area and location of stations (marked with a square).
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.