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datasets available to search
ShareScore release 0.9.0
Dataset results
60 results for “Cost efficiency”
Data from: Cost of transport is a repeatable trait but is not determined by mitochondrial efficiency in zebrafish (Danio rerio)
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Data from: Cost-efficient high throughput capture of museum arthropod specimen DNA using PCR-generated baits
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Data from: Depth dependent dive kinematics suggest cost-efficient foraging strategies by tiger sharks
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Data from: Aging infrastructure creates opportunities for cost-efficient restoration of aquatic ecosystem connectivity
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Data from RACE: Resource Aware Cost-Efficient Scheduler for Cloud Fog Environment
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Data from: Increase in light interception cost and metabolic mass component of leaves are coupled for efficient resource use in the high altitude vegetation
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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>
The Hidden Cost of Code Completion: Understanding the Impact of the Recommendation-list Length on its Efficiency
<p>This contains the data set and the code we take advantage of to process data and make visualization. The data set needs to be unzipped before the program could run on it. Besides, we choose another visualization tool to make the results more explicit.</p>
Supplementary material 4 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Table S3
Supplementary material 6 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Figure S1. Pictures were taken with a digital microscope (Keyence VHX-6000, Keyence, Osaka, Japan)
Supplementary material 1 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Protocol 1 – DIY-DS
Supplementary material 3 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Table S2. Raw read table
Supplementary material 2 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Table S1. PCR primers used in this study
Supplementary material 8 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Figure S3
Supplementary material 5 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Script 1
A Novel Low-Cost Tool for a More Efficient and Reliable Weight-for-Height/Length Assessment
ClinicalTrials.gov study NCT03780348. IPD Sharing: NO. Countries: 0. Publications: 4.
3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling (Glitazone)
GEO Series GSE125570. Homo sapiens. 80 samples. Type: Expression profiling by high throughput sequencing.
3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling (Run0050)
GEO Series GSE125568. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.
A simple, fast and cost-efficient protocol for ultra-sensitive ribosome profiling
GEO Series GSE294467. Escherichia coli; Homo sapiens; Mus musculus; Saccharomyces cerevisiae. 21 samples. Type: Other; Expression profiling by high throughput sequencing.
3’Pool-seq: an optimized cost-efficient and scalable method of whole-transcriptome gene expression profiling (Run0064)
GEO Series GSE125569. Mus musculus. 32 samples. Type: Expression profiling by high throughput sequencing.
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.