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1,055 results for “bridges”
Medieval Bridge
a simple textured medieval bridge Source: Objaverse 1.0 / Sketchfab
Real railway traffic - Alcacer Bridge in Portugal
Open the record for dataset details and reuse information.
Duality of AI-based Interaction Systems in Organizations: Bridging Among Human and Machine
<p>This data was sampled along two online vignette studies, aming to clearify pschological determinants of acceptance of AI-based interaction systems in Organizations. It includes all raw data from the corresponding standardized online questionnaires, which were used to measure potential determinants as well as indicators of acceptance. It also includes identifiers for the randomly assigned vignettes. </p>
Data from: Effects of forest degradation on Amazonian ferns in a land-bridge island system as revealed by non-specialist inventories
<p>Background: Tropical deforestation and degradation worldwide have rapidly outpaced biodiversity field sampling. No study to date has assessed the effects of insular habitats induced by hydroelectric dams on Amazonian understorey plants. Fern community responses to anthropogenic effects on tropical forest islands can be efficiently revealed through simple and cheap, yet informative protocols that can be applied by non-specialists. </p> <p>Aims: This study seeks to both understand the drivers of fern and lycophyte assemblages on forest islands and investigate the relative costs and effectiveness of a simplified sampling protocol that can be implemented by non-specialists and has potential to be used to crowdsource ecological field data acquisition.</p> <p>Methods: Fern and lycophytes species were sampled by a non-specialist in 17 quarter-hectare plots on 10 forest islands at the lake of Balbina Hydroelectric Dam, central Amazonia. Sampling was carried out opportunistically during a field expedition planned to conduct tree inventory on permanent plots. We used a set of locally measured or GIS-derived predictors for each of the surveyed sites. We used Principal Coordinates Analysis and Generalized Linear Mixed Models (GLMMs) to further assess the influence of predictors on patterns of fern species richness and composition.</p> <p>Results: A total of 286 photographed individual ferns or lycophytes represented 23 taxa. The average number of taxa per plot was 6.1 on islands and 14.3 in the mainland. The insular species pool was a subset of the mainland pool of fern species. Richness was positively related to island size and negatively related to isolation and fire severity. Area, isolation and fire severity significantly explained variation in community composition. The relative cost of the non-specialist picture-based fern protocol was very modest (in our case, only 4% of the total expedition budget), even compared to the typically low cost of alternative orthodox field campaigns.</p> <p>Conclusion: Fern community structure in this forest archipelago was primarily driven by island size, isolation and fire disturbance. We show that a simple sampling protocol carried out by a non-specialist can lead to inexpensive and highly reliable ecological data. This opens an avenue for crowdsourcing ecological fern data collections using a citizen science approach.</p>
Bridge Load Posting Prediction
<p>There are approximately 13,000 bridges in Louisiana facilitating movement of people, goods, and services. At present, about 12% of the bridges are load posted, i.e., they are deemed to lack the strength to safely carry all legal loads. With time bridges will age and deteriorate; at the same time, legal loads might increase. Load posted bridges disrupt the movement of goods and commerce. Therefore, objective of this research was to estimate the number of load posted bridges in Louisiana over the next 50 years. For this purpose, herein, a data based approach was used. For a given bridge type, the approach first developed three random forest models, to predict the future deck, sub-structure, and super-structure condition ratings of all the bridges of that type over the next 50 years. The inputs to these random forest models included a large number of bridge parameters obtained from the National Bridge Inventory (NBI). Herein, the bridge types were obtained from the Louisiana Department of Transportation and Development (LADOTD). Next, for a given bridge type, another random forest model was developed which used bridge parameters from NBI along with condition ratings to predict the load posting decision for a specific year in the future. The number of load posted bridge in each type were aggregated to obtain an estimate of the number of load posted bridge over the next 50 years. The random forest models identified several bridge parameters that significantly influence bridge load posting, e.g., condition ratings, age, span length, and roadway width among others. The results showed that while bridge types like light weight concrete pre-cast slab units and timber bridge had a large number of load posted bridges at present, concrete slab type bridge may be of concern in the future.</p>
Efficient, Low-cost Bridge Cracking Detection and Quantification Using Deep-learning and UAV Images
<p>Many bridges in the State of Louisiana and the United States are working under serious degradation conditions where cracks on bridges threaten structural integrity and public security. To ensure structural integrity and public security, it is required that bridges in the US be inspected and rated every two years. Currently, this biannual assessment is largely implemented using manual visual inspection methods, which is slow and costly. In addition, it is challenging for workers to detect cracks in regions that are hard to reach, e.g., the top part of the bridge tower, cables, mid-span of the bridge girders, and decks. This research develops an efficient low-cost deep learning-based methodology to identify cracks on bridges using computer vision-based techniques and deep learning. The Convolutional Neural Networks (CNN) deep learning method is used to identify cracks from images. In this research, a programmable drone is developed that can fly along a pre-defined trajectory. A large volume of images was collected from local bridges and pavements using drones. The collected images were preprocessed and divided into around forty thousand 256 by 256-pixel sub-images and fed into the CNN model. Data augmentation techniques are applied to increase the number of images in some cases. Parameters of the selected CNN model were optimized to obtain the best configuration. To evaluate the performance of the method, images from a different local bridge were used for testing. Research results show that with the optimized CNN model, cracks in the images can be identified efficiently and accurately. The developed methodology can also category the cracked image as slight, moderate, or severe cracking based on a pre-defined quantification index. The research outcome of this project has the potential to automate crack damage identification of bridge key components in a cost-effective manner. Also, the developed methodology is expected to facilitate crack damage identification for other transportation infrastructures, e.g., pavement and traffic sign structures.</p>
Field Implementation and Monitoring of an Ultra-High Performance Concrete Bridge Deck Overlay
<p>This project focused on field implementation of an ultra-high performance concrete (UHPC) overlay during rehabilitation of an existing concrete bridge (No. 7032) deck in Socorro, New Mexico, USA. Bridge 7032 is a two-lane bridge that is approximately 300 ft. (91.4 m) long and 54 ft. (16.5 m) in width. Rehabilitation of bridge 7032 included removal of deteriorated concrete from the existing deck, installation of a high-performance deck (HPD) leveling course, and installation of a 1 in. (25 mm) UHPC overlay. An UHPC mixture with a 19.5 ksi (134 MPa) compressive strength developed in previous research was selected for this project. This mixture was revised at the beginning of the project, highlighting the need for robust mixtures that can accommodate constituent material substitutions. Prior to placement of the UHPC overlay, the HPD substrate was ceramic bead blasted to remove surface paste and partially expose fine aggregate, thoroughly cleaned, and maintained saturated for 24 hours prior to UHPC placement.</p> <p>The non-proprietary UHPC overlay was successfully placed in four sections between April 10, 2021 and April 27, 2021. During construction of the UHPC overlay, a total of 105 batches were placed. The average direct tensile bond strength of the UHPC overlaid bridge deck was 239 psi (1.65 MPa), which is conservative since several of the tests had fractures that occurred at the epoxy-UHPC interface. Strain gauges and thermocouples were used to monitor the UHPC overlay and existing deck through initial, early-age, and longer-term monitoring programs. Temperature and strain monitoring showed that daily and multi-day strain trends coincided well with daily temperature trends.<br> Major observations during this field implementation project were that four small areas of possible delamination were identified using non-destructive testing, the bond strength was good since the 239 psi (1.65 MPa) average was conservative, preliminary monitoring results were consistent with expectations based on temperature measurements and restraint provided by the concrete superstructure, and relative strains from the monitoring data are being used for detailed analysis of the bridge and overlay behaviors for an ongoing NMDOT project that runs for another two years.</p>
Cavenagh Bridge Sign
Source: Objaverse 1.0 / Sketchfab
Supplementary material 1 from: Helms IV JA, Bridge ES (2017) Range expansion drives the evolution of alternate reproductive strategies in invasive fire ants. NeoBiota 33: 67-82. https://doi.org/10.3897/neobiota.33.10300
Range expansion drives the evolution of alternate reproductive strategies in invasive fire ants :
Fig. 4 in Multispecies leatherback turtle assemblage from the Oligocene Chandler Bridge and Ashley formations of South Carolina, USA
Fig. 4. Ossicle of leatherback turtle cf. Egyptemys sp. (CCNHM 4289) from Oligocene of South Carolina, USA, in dorsal (A1), visceral (A2), and sutural A3) views.
Fig. 2 in Multispecies leatherback turtle assemblage from the Oligocene Chandler Bridge and Ashley formations of South Carolina, USA
Fig. 2. Ossicles of leatherback turtle Natemys sp. 1 from Oligocene of South Carolina, USA. CCNHM 5542 (A), CCNHM 5540 (B), CCNHM 4405.1– 4405.5 (C–G, respectively), CCNHM 4288 (H), and CCNHM 5541 (I), in dorsal (A1–I1), visceral (A2–I2), and sutural (A3–I3) views.
Fig. 1 in Multispecies leatherback turtle assemblage from the Oligocene Chandler Bridge and Ashley formations of South Carolina, USA
Fig. 1. The Ashley and Chandler Bridge formations and geologic context of CCNHM ossicles. A. Map of the southeastern United States. B. Map of Ashley and Chandler Bridge formation exposures on the coast of South Carolina. Stars denote Oligocene dermochelyid localities. C. Stratigraphic column of the Ashley and Chandler Bridge formations.
Dataset: Chain Bridge I (CBRG) 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.
Network Model with Internal Complexity Bridges Artificial Intelligence and Neuroscience
Open the record for dataset details and reuse information.
Fig 7 in Bridging the gap: A new species of arboreal Abronia (Squamata: Anguidae) from the Northern Highlands of Chiapas, Mexico
Fig 7. Color variation in preservative (ethanol after formalin) in dorsal and ventral view of type series of Abronia cunemica sp. nov. from Coapilla, Chiapas, Mexico. From left to right: adult male holotype, MZFC-HE 36544 (AGC 1428), 127 mm snout-to-vent length (SVL); adult female paratype, MZFC-HE 36545 (AGC 1429), 113 mm SVL; juvenile male paratype, MZFZ 4512 (AGC 1484), 91 mm SVL; adult female paratype, MZFZ 4513 (AGC 1491), 107 mm SVL; and adult female paratype, MZFZ 4514 (AGC 1492), 110 mm SVL. Photographs by Israel Solano-Zavaleta. https://doi.org/10.1371/journal.pone.0295230.g007
Figure 3 from: Evangelista-Vargas OD, Silveira LF (2018) Morphological evidence for the taxonomic status of the Bridge's Guan, Penelope bridgesi, with comments on the validity of P. obscura bronzina (Aves: Cracidae). Zoologia 35: 1-10. https://doi.org/10.3897/zoologia.35.e12993
Figure 3 CVA Plot of geographic groups according to morphometric measurements. Black circles: Parana Forest (group 1); Plus: Atlantic Forest 1 (group 2); White squares: Atlantic Forest 2 (group 3); Black squares: Atlantic Forest 3 (group 4); Exes: Araucaria Forest 1 (group 5); White circles: Araucaria Forest 2 (group 6); White diamonds: Pampa (group 7); Inverted black triangles: Chaco (group 8); White triangle: Yungas (group 9).
Figure 4 from: Evangelista-Vargas OD, Silveira LF (2018) Morphological evidence for the taxonomic status of the Bridge's Guan, Penelope bridgesi, with comments on the validity of P. obscura bronzina (Aves: Cracidae). Zoologia 35: 1-10. https://doi.org/10.3897/zoologia.35.e12993
Figure 4 From top to bottom, dorsal view of P. o. bronzina (MZUSP 49384, São Paulo, southeastern BR), P. o. obscura (MACN 48384, Corrientes, northeastern ARG), and P. o. bridgesi (MACN 8148a, Tucumán, northern ARG). Scale bars: 10.0 cm.
Figure 5 from: Evangelista-Vargas OD, Silveira LF (2018) Morphological evidence for the taxonomic status of the Bridge's Guan, Penelope bridgesi, with comments on the validity of P. obscura bronzina (Aves: Cracidae). Zoologia 35: 1-10. https://doi.org/10.3897/zoologia.35.e12993
Figure 5 From top to bottom: lateral view of P. o. bronzina (MZUSP 49384, São Paulo, southeastern BR), P. o. obscura (MACN 48384, Corrientes, northeastern ARG), and P. o. bridgesi (MACN 8148a, Tucumán, northern ARG). Scale bars: 10.0 cm.
Figure 1 from: Evangelista-Vargas OD, Silveira LF (2018) Morphological evidence for the taxonomic status of the Bridge's Guan, Penelope bridgesi, with comments on the validity of P. obscura bronzina (Aves: Cracidae). Zoologia 35: 1-10. https://doi.org/10.3897/zoologia.35.e12993
Figure 1 Distribution of Penelope obscura. Black squares: specimens analyzed personally; Black triangles: specimens analyzed by photographs; White circles: living individuals (photos).
Figure 2 from: Evangelista-Vargas OD, Silveira LF (2018) Morphological evidence for the taxonomic status of the Bridge's Guan, Penelope bridgesi, with comments on the validity of P. obscura bronzina (Aves: Cracidae). Zoologia 35: 1-10. https://doi.org/10.3897/zoologia.35.e12993
Figure 2 Geographic groups of Penelope obscura separated by biogeographic provinces: Parana forest (1), Atlantic Forest (2,3,4), Araucaria Forest (5,6), Pampa (7), Chaco (8) and Yungas (9).
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.