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Dataset results
424 results for “In-situ”
Lidocaine In-situ Gel Prior to Intrauterine Device Insertion in Women With no Previous Vaginal Delivery
ClinicalTrials.gov study NCT03166111. IPD Sharing: NO. Countries: 1. Publications: 1.
Analysis of Ocular Surface Microbiota in Dry Eye Patients After Femtosecond Laser-assisted In-situ Keratomileusis (FS-LASIK)
ClinicalTrials.gov study NCT06448468. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Reservoir in-situ stress state determined by retrieved granite cores from the Gonghe enhanced geothermal system and its implications, northeastern Tibetan Plateau, China
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Data from: Using genetic techniques to quantify reinvasion, survival and in-situ breeding rates during control/eradication operations
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Data from: Influence of in-situ oil sands development on caribou (Rangifer tarandus) movement
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Research data supporting "Void-free 3D bioprinting for in-situ endothelialization and microfluidic perfusion"
<p>Raw research data supporting the publication:</p> <p>Ouyang, Li. et al., 2019, Advanced Functional Materials. DOI: 10.1002/adfm.201908349</p>
FLUID: Bio-optical water quality parameters (in-situ 2017-2019) in 4 Baltic lakes
<p>The database consists bio-optical measurements done under FLUID<sup>1</sup> project in 4 different lakes (Burtnieks, Lubans, Razna and Võrtsjärv) during 2017-2019.</p> <p>The listed parameters are: </p> <ul> <li>LAKE (name)</li> <li>DATE </li> <li>TIME (local time)</li> <li>LAT (latitude)</li> <li>LON (longitude)</li> <li>Air T (air temperature in Celcius)</li> <li>Water T (water temperature in 0.5 m depth in Celcius)</li> <li>DO (dissolved oxygen in ppm)</li> <li>O2 (O2 saturation in %)</li> <li>Secchi (in meters)</li> <li>Chl a (chlorophyll-a in mg/m3)</li> <li>Pheo (pheopigments in mg/m3) </li> <li>TSS (total suspended sediments in mg/l)</li> <li>OSS (organinc suspended sediments in mg/l)</li> <li>MSS (mineral suspended sediments in mg/l)</li> <li>CDOM (absorption of coloured dissolved organic matter at 400 nm)</li> <li>TN (total nitrogen in mg/l)</li> <li>TP (total phosphorus in mg/l)</li> <li>CO2 (carbon dioxide in mg/l)</li> </ul> <p><sup>1</sup>FLUID is funded by ERDF, Latvian state budget and IES proposal No.1.1.1.2/VIAA/1/16/137, Contract No. 1.1.1.2/16/I/001 “Innovative tool for lake monitoring using remote sensing data"</p>
Data from: Redesigning the 'choice architecture' of hospital prescription charts: a mixed methods study incorporating in-situ simulation
Objectives: To incorporate behavioural insights into the user-centred design of an inpatient prescription chart (Imperial Drug Chart Evaluation and Adoption Study, IDEAS chart) and to determine whether changes in the content and design of prescription charts could influence prescribing behaviour and reduce prescribing errors. Design: A mixed-methods approach was taken in the development phase of the project; in situ simulation was used to evaluate the effectiveness of the newly developed IDEAS prescription chart. Setting: A London teaching hospital. Interventions/methods: A multimodal approach comprising (1) an exploratory phase consisting of chart reviews, focus groups and user insight gathering (2) the iterative design of the IDEAS prescription chart and finally (3) testing of final chart with prescribers using in situ simulation. Results: Substantial variation was seen between existing inpatient prescription charts used across 15 different UK hospitals. Review of 40 completed prescription charts from one hospital demonstrated a number of frequent prescribing errors including illegibility, and difficulty in identifying prescribers. Insights from focus groups and direct observations were translated into the design of IDEAS chart. In situ simulation testing revealed significant improvements in prescribing on the IDEAS chart compared with the prescription chart currently in use in the study hospital. Medication orders on the IDEAS chart were significantly more likely to include correct dose entries (164/164 vs 166/174; p=0.0046) as well as prescriber's printed name (163/164 vs 0/174; p<0.0001) and contact number (137/164 vs 55/174; p<0.0001). Antiinfective indication (28/28 vs 17/29; p<0.0001) and duration (26/28 vs 15/29; p<0.0001) were more likely to be completed using the IDEAS chart. Conclusions: In a simulated context, the IDEAS prescription chart significantly reduced a number of common prescribing errors including dosing errors and illegibility. Positive behavioural change was seen without prior education or support, suggesting that some common prescription writing errors are potentially rectifiable simply through changes in the content and design of prescription charts.
Data from: In-situ recording of ionic currents in projection neurons and Kenyon cells in the olfactory pathway of the honeybee
The honeybee olfactory pathway comprises an intriguing pattern of convergence and divergence: ~60.000 olfactory sensory neurons (OSN) convey olfactory information on ~900 projection neurons (PN) in the antennal lobe (AL). To transmit this information reliably, PNs employ relatively high spiking frequencies with complex patterns. PNs project via a dual olfactory pathway to the mushroom bodies (MB). This pathway comprises the medial (m-ALT) and the lateral antennal lobe tract (l-ALT). PNs from both tracts transmit information from a wide range of similar odors, but with distinct differences in coding properties. In the MBs, PNs form synapses with many Kenyon cells (KC) that encode odors in a spatially and temporally sparse way. The transformation from complex information coding to sparse coding is a well-known phenomenon in insect olfactory coding. Intrinsic neuronal properties as well as GABAergic inhibition are thought to contribute to this change in odor representation. In the present study, we identified intrinsic neuronal properties promoting coding differences between PNs and KCs using in-situ patch-clamp recordings in the intact brain. We found very prominent K+ currents in KCs clearly differing from the PN currents. This suggests that odor coding differences between PNs and KCs may be caused by differences in their specific ion channel properties. Comparison of ionic currents of m- and l-ALT PNs did not reveal any differences at a qualitative level.
Supporting material for the data on the in-situ formation of Zr conversion coating on Al2024
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Chapter 3 - A highly-dynamic East Antarctic Ice Sheet during the Miocene: A multi-proxy sedimentary provenance approach using in-situ 87Rb/87Sr dating of detrital K-feldspar in ODP Site 1165, Prydz Bay
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In-situ Characterization Data for Carbon Fiber Composite Manufacturing
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Supporting data to "Open-source, low-cost, in-situ turbidity sensor for river network monitoring"
<p>This folder contains the Supporting Dataset that is part of the Manuscript "Open-source, low-cost, in-situ turbidity sensor for river network monitoring."</p>
Chapter 2 - Dynamic collapse and regrowth of the Antarctic Ice Sheet in the Weddell Sea Sector during the Middle Miocene: A novel multi-proxy sedimentary provenance approach using in-situ 87Rb/87Sr dating of detrital K-feldspar - Supplementary Materials
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Fig. 2 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 2: A: Confidence level along depth for Argo-model average temperature profile differences in South Adriatic (blue) & Otranto Strait (green). B: Confidence level along depth for Argo-model average temperature profile differences in Northern (yellow) and Southern (red) Ionian. C: Confidence level along depth for Argo-model average salinity profile differences in South Adriatic (blue) & Otranto Strait (green). D: Confidence level along depth for Argo-model average salinity profile differences in Northern (yellow) and Southern (red) Ionian. E: Confidence level along depth for Argo-model average temperature (green) and salinity (brown) profile differences in the whole study area. The shaded rectangular denotes the area of statistical significant differences with a confidence of 95% between the Argo and model distributions (null-hypothesis rejected, p <0.05).
MATLAB code for processing images of the in-situ etching process and analysis of etching kinetics for MAX phase to MXene transformation
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Audio Visual in-situ Monitoring Dataset for Laser Directed Energy Deposition (LDED) of Maraging Steel C300
<p>This dataset presents a set of acoustic signals and coaxial CCD images captured during a single-bead wall experiment in robotic Laser Directed Energy Deposition (LDED) using Maraging Steel C300. The acoustic data was recorded using a high-fidelity Prepolarized microphone sensor (Xiris WeldMIC), capturing the intricate sound profiles associated with the LDED process at a sampling rate of 44,100 Hz. The coaxial CCD melt pool images are captured at 30 Hz.</p> <p><strong>Laser Directed Energy Deposition:</strong></p> <p>This dataset was generated with a robotic LDED process that consists of a six-axis industrial robot (KUKA KR90) coupled with a two-axis positioner, a laser head, and a coaxial powder-feeding nozzle.</p> <p> </p> <p><strong>File Naming Convention:</strong></p> <ul> <li>Audio files within the <strong>audio_files</strong> folder are named following the pattern <strong>sample_ExperimentID_SampleID.wav</strong>. Given that there's only one experiment and one sample provided in this demo dataset, the naming will be consistent, for example, <strong>sample_1_1.wav</strong> for the first file.</li> <li>Coaxial melt pool image files within the <strong>images </strong>folder are named following the pattern <strong>sample_ExperimentID_SampleID.jpg</strong>. </li> </ul> <p><strong>Annotation Details:</strong></p> <ul> <li>The <strong>annotations_1.csv</strong> file contains detailed labels for each audio file and image file, correlating to the conditions observed during the experiment, aiding in quick identification and analysis.</li> </ul> <p><strong>Handcrafted features for ML modelling:</strong></p> <ul> <li>The <strong>audio_features.h5</strong> file contains various physics-informed acousitc feature extracted through Python, which can be used for baseline ML modelling purpose.</li> </ul> <p><strong>Experimental Parameters:</strong> The dataset reflects a controlled experiment setup with the following specifications:</p> <ul> <li>Geometry: Single bead wall structure</li> <li>Dimensions: 90 mm * 42.5 mm</li> <li>Number of layers: 50</li> <li>Laser beam diameter: 2 mm</li> <li>Layer thickness: 0.85 mm</li> <li>Stand-off distance: 12 mm</li> <li>Laser profile: Gaussian</li> <li>Laser wavelength: 1064 nm</li> </ul> <p><strong>Process Parameters:</strong></p> <ul> <li>Laser power: 2.3 kW</li> <li>Speed: 25 mm/s</li> <li>Dwell time: 0 s</li> <li>Powder flow rate: 12 g/min</li> </ul> <p>This dataset aims to facilitate the development and testing of acoustic-based, or multi-sensor fusion-based defect detection models for real-time quality monitoring in LDED processes. It can also serve as a reference point for further research on sensor fusion, machine learning, and real-time monitoring of manufacturing processes.</p>
Supplemental material for article "An analytical approach for bacteria modeling in an estuarine system and in-situ die-off rate estimation"
<p>It contains several supplemental figures and text. </p>
Improved 3D Characterization of in-situ Soil Desiccation Cracking by multi-source Data Integration
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Grain-Scale Stress Heterogeneity in Concrete from In-Situ X-Ray Measurements
<p>All data in this repository is described in Data-Description_Zenodo.docx.</p> <p> </p>
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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.