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29
datasets available to search
ShareScore release 0.9.0
Dataset results
29 results for “technology identification”
Data for: Using radio frequency identification (RFID) technology to characterize nest site selection in wild Japanese tits (Parus minor)
<p>Selecting a suitable nest site is critical to the survival and reproduction of birds. Prospecting allows individuals to gather information on the local quality of potential future breeding sites, which may help them make the best nest site selection decision. However, few studies have focused on the direct links between the prospecting activity of breeders and subsequent nest site selection. In this study, we investigated the prospecting pattern of Japanese tits (<em>Parus minor</em>) during the pre-breeding period of the first breeding attempt and whether nest site characteristics influence their nest box visiting behaviour and occupied nest site. We used radio frequency identification (RFID) to track the movements of Japanese tits visiting nest boxes and compared nest site characteristics between visited and unvisited (control) nest boxes, as well as between visited and occupied nest boxes. We found that Japanese tits started visiting nest boxes approximately 20 days before breeding, visited an average of 6 nest boxes and eventually chose the most visited nest box for breeding activities. Japanese tits were more likely to visit nest boxes that had less canopy cover and lower shrub density but a greater total number of surrounding trees and ultimately chose breeding nest boxes with a smaller entrance inclination, in nesting trees with a larger diameter at breast height (DBH) which were surrounded by trees with a larger DBH. Our results suggest that Japanese tits visit several potential breeding sites before choosing breeding nest boxes and that nest site characteristics can influence their prospecting activity and nest site selection.</p>
Data for: Using radio frequency identification (RFID) technology to characterize nest site selection in wild Japanese tits (Parus minor)
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Radiofrequency Identification Technology in Locating Non-palpable Breast Lesions in Patients Undergoing Surgery
ClinicalTrials.gov study NCT03202472. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Research on potential disruptive technology identification based on technology network
<p><span>Three evident and meaningful characteristics of disruptive technology are the zeroing effect that causes sustaining technology useless for its remarkable and unprecedented progress, reshaping the landscape of technology and economy, and leading the future mainstream of technology system, all of which have profound impacts and positive influences. The identification of disruptive technology is a universally difficult task. Therefore, the paper aims to enhance the technical relevance of potential disruptive technology identification results and improve the granularity and effectiveness of potential disruptive technology identification topics. According to the life cycle theory, dividing the time stage, then constructing and analyzing the dynamic of technology networks to identify potential disruptive technology. Thereby, using the LDA topic model further to clarify the topic content of potential disruptive technologies. This paper takes the large civil UAVs as an example to prove the feasibility and effectiveness of the model. The results show that the potential disruptive technology in this field is the main equipment, data acquisition, and information transmission.</span></p>
Fig. 2 in The application of B-omics^ technologies for the classification and identification of animals
Fig. 2 Lateral view of the harpacticoid copepod Evansula pygmaea (Scott, 1903) based on confocal laser scanning microscopy (CLSM)
Fig. 1 in The application of B-omics^ technologies for the classification and identification of animals
Fig. 1 Modern analytical -omics technologies (left) that allow a characterization of the genome, transcriptome, proteome, and metabolome of an organism
Fig. 4 in The application of B-omics^ technologies for the classification and identification of animals
Fig. 4 Aspects of an idealized species description using morphological, genomic, proteomic, and metabolomic data sets as part of an integrative cybertaxonomic approach
PREventing Adverse Events Post-Discharge Through Proactive Identification, Multidisciplinary Communication, and Technology
ClinicalTrials.gov study NCT05232656. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Going mobile: Using portable genomic technologies for PCR-free in situ species identification and real-time molecular systematics
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Data from: Research on potential disruptive technology identification based on technology network
Open the record for dataset details and reuse information.
Fig. 3 Dorsal 3D in The application of B-omics^ technologies for the classification and identification of animals
Fig. 3 Dorsal 3D print of a serolid isopod (Crustacea, Peracarida)
Identification of Wright fishhook cactus using drone and remote sensing technology
<p>Obtaining accurate population estimates of plants has been an integral part of the listing, recovery, and delisting of species under the U.S. Endangered Species Act (ESA) of 1973 and for monitoring vegetation in response to livestock grazing management. However, obtaining such estimates for many plant species remains a daunting and labor-intensive task. The use of small unmanned aircraft systems (sUAS or drones) may provide an effective alternative to ground surveys for rare and endangered plants.</p> <p>The objective of our study was to evaluate the effectiveness of using sUAS (DJI Phantom 4 Pro with a 20 MP camera) to survey for Wright fishhook cactus (<i>Sclerocactus wrightiae</i> L.D.Benson), a small (1-8 cm diameter) endangered plant species endemic to Utah, located in southwest USA desert grazing lands. This species functions in enhancing soil stability, providing nectar for pollinating insect species, and increasing biodiversity in hot arid environments.</p> <p>We used georectified images overlaid with grid plots in ArcGIS Pro to 1) assess the effectiveness of very high resolution remotely sensed imagery for detecting and counting individual cacti and then compared these with ground surveys and 2) determine the optimal altitude (10 m, 15 m, or 20 m) and associated resolution for identifying individual cactus plants.</p> <p>Our results demonstrated that the lowest altitude flights (10 m) provided the best detection rates (from 26.6% at 20m to 67% at 10m; <i>p</i><0.001) and counts (<i>p</i><0.001). We generated population estimates based on the inclusion of error terms in the analysis. We suggest that sUAS can be effectively used to locate cactus within grazing land areas, but should be coupled with ground surveys for higher accuracy and reliability. We suggest that sUAS surveys can be effectively conducted for locating cactus populations within the flowering period and for documenting known populations outside of the flowering period. While sUAS remote sensing did not provide a complete census of Wright fishhook cactus plants, likely due to its small, obscure, thorny, low-growing structure, nonetheless this tool can be effective in early plant population detection, monitoring populations in response to grazing activities, and preventing potential soil/plant disturbance resulting from ground-based surveys.</p>
High-throughput Omic Technology for Identification of Biomarkers of Relapsing Acute Disseminated Encephalomyelitis in Immune Cell Network
ClinicalTrials.gov study NCT06863974. IPD Sharing: YES. Countries: 1. Publications: 0.
Identification of Wright fishhook cactus using drone and remote sensing technology
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Identification of the 5' end of the pig miRNA by the RAKE technology
GEO Series GSE28139. Sus scrofa. 9 samples. Type: Non-coding RNA profiling by array.
Identification of imprinted gene Grb10 to associate with the pluripotency state in nuclear transfer embryonic stem cells using high throughput sequencing technology
GEO Series GSE92308. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
Identification of alternative splicing events regulated by the splicing factor SRSF1 using data from exon-junction microarray technologies
GEO Series GSE53410. Homo sapiens. 9 samples. Type: Expression profiling by array.
Identification of the 3' end of the pig miRNA by the RAKE technology [platform 1: 9003467]
GEO Series GSE28137. Sus scrofa. 12 samples. Type: Non-coding RNA profiling by array.
Identification of sRNA binding to ITSmetZW and ITSmetWV using MS2-affinity purification coupled with RNA sequencing (MAPS) technology
GEO Series GSE66517. Escherichia coli K-12. 2 samples. Type: Other.
Identification of the 3' end of the pig miRNA by the RAKE technology [platform 2: 9003179]
GEO Series GSE28138. Sus scrofa. 12 samples. Type: Non-coding RNA profiling by array.
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.