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Dataset results
37 results for “Bioimaging”
Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 3 - Raw Data survey entries
<p>This document provides extended, supplementary data and information to the manuscript "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey" by Schmidt C., Hanne J, Moore J, Meesters C, Ferrando-May E, Weidtkamp-Peters S, and members of the NFDI4BIOIMAGE initiative. [version 1; peer review: awaiting peer review] F1000Research 2022, 11:638, https://doi.org/10.12688/f1000research.121714.1</p> <p>This extended data includes:</p> <p>- The raw dataset of survey entries, anonymized (IP addresses and personal comments deleted)</p>
Dynamic FRET example videos related to "Mars, a molecule archive suite for reproducible analysis and reporting of single-molecule properties from bioimages"
<p>Videos of dynamic switching between iso-I and iso-II conformations of a holiday junction at 50 mM Magnesium resulting in high and low FRET from Cy3 and Alexa647 labels positioned on the arms. Holiday junctions are surface immobilized through a biotin attachment and imaged using TIRF microscopy. The camera sensor is split using a dual view so that the acceptor emission is on the top and the donor emission is on the bottom. Videos from each position are provided as compressed zip files containing a sequence of tif files and associated metadata text file. Image sequences were collected using Micro-Manager 2.0 using ALEX or alternating laser excitation with alternating 637 and 532 pulses separated as two different channels. Beam profile images are provided for 637 and 532 excitation allowing for correction of the non-uniform beam profiles. The following 2D affine transformation matrix can be used to transform from the top acceptor emission region to the bottom donor emission region during processing.</p> <p>Affine 2D transformation from top to bottom: (m00, m01, m02, m10, m11, m12), (1.00276, 0.000208, 1.01236, 0.000267, 1.00312, 507.21025)</p> <p>A detailed image processing workflow for this dataset using Mars can be found under the example section at <a href="https://duderstadt-lab.github.io/mars-docs/">https://duderstadt-lab.github.io/mars-docs/</a> or directly at <a href="https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/">https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/</a></p>
Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 4 - Analysis Data Sheet
<p>This dataset is extended data to the manuscript "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey" by Schmidt C., Hanne J, Moore J, Meesters C, Ferrando-May E, Weidtkamp-Peters S, and members of the NFDI4BIOIMAGE initiative. [version 1; peer review: awaiting peer review] F1000Research 2022, 11:638, https://doi.org/10.12688/f1000research.121714.1</p> <p>This extended data includes:</p> <p>- Data Analysis Sheet and results table</p> <p>Note: The data is anonymized (i.e., all IP addresses as well as personal comments were deleted)</p> <p>The revised version was published after the peer-review process of the original article on zenodo.org</p>
baskaufs/Bioimages: Bioimages Release 2024-04-26
<p>Update trees on Vanderbilt campus to reflect recent deaths.</p>
[ELMI2023] BioImage Town (BIT) FAIR Data Metro Map
<p>Figures created collaboratively by the presenters of the Data Management and Analysis session of ELMI2023 (https://elmi2023.eu/) for their presentations. They represent an idealized metro through which data ("the passengers") travel between various solutions ("the stops") within bioimaging ("BioImage Town"), but also connecting to IT solutions, metadata, and other areas, though of course the real situation is much more complicated. Working together, we should be able to the improve the number of easy-to-use, performant, and complete solutions through BioImage Town for the benefit of the community.</p>
[HCB] BioImage Town (BIT) FAIR Data Metro Map
<p>Figures created collaboratively by the presenters of the Data Management and Analysis session of ELMI2023 (https://elmi2023.eu/) for their presentations. They represent an idealized metro through which data ("the passengers") travel between various solutions ("the stops") within bioimaging ("BioImage Town"), but also connecting to IT solutions, metadata, and other areas, though of course the real situation is much more complicated. Working together, we should be able to the improve the number of easy-to-use, performant, and complete solutions through BioImage Town for the benefit of the community.</p> <p>See previous version at https://zenodo.org/record/8019760</p> <p> </p>
EfficientBioAI: Making Bioimaging AI Models Efficient in Energy and Latency
<p>This dataset contains trained deep learning models, dataset and experiment files for the manuscript "EfficientBioAI: Making Bioimaging AI Models Efficient in Energy and Latency". Please find the software and more information including tutorials here: <a href="https://github.com/MMV-Lab/EfficientBioAI">MMV-Lab/EfficientBioAI (github.com)</a>.</p>
Boosting the Near-Infrared Emission of Ag2S Nanoparticles by a Controllable Surface Treatment for Bioimaging Applications
<p>Dataset of https://pubs.acs.org/doi/10.1021/acsami.1c19344</p>
Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data
<p>guided walkthrough of poster at <a href="https://doi.org/10.5281/zenodo.6821815">https://doi.org/10.5281/zenodo.6821815</a></p> <p>which provides an overview of contexts, frameworks, and models from the world of bioimage data as well as metadata and the techniques for structuring this data as Linked Data.</p> <p>You can also watch the video in the browser on the <a href="https://gerbi-gmb.de/i3dbio/i3dbio-resources/metadata-guide/">I3D:bio website</a>.</p>
Section 5.4 "Task Area 4: Bioimage informatics and analysis" Figure 11
<p>Figure 11. <em><span>FAIR pipelines for image analysis to balance the current status quo of hardly recordable manual processing (point-and-click) with the goal of increasing reproducibility of workflows</span></em>.</p> <p>from NFDI Grant Application, "<strong>National Research Data Infrastructure for Microscopy and Bioimage Analysis</strong>" (NFDI4BIOIMAGE)</p>
BioImages - the Virtual Fieldguide (UK): Malcolm Storey text objects
Archival; not in use
BioImages - the Virtual Fieldguide (UK)
A large collection of photographs of wildlife from Great Britain, and covering a wide range of groups of organisms. <p></p>http://www.bioimages.org.uk/
Bioimages (Vanderbilt)
To provide educational information to the public on biologically related topics, as well as to be a source of biological images for personal and non-commercial use. Collection GUID: urn:lsid:biocol.org:col:35115. <p></p>http://bioimages.vanderbilt.edu/
Introduction to Bioimage Analysis using QuPath (supporting image)
<p>Example image supporting the workshop "Introduction to Bioimage Analysis using QuPath", in-person at HMS.</p>
Data sets for "Automated cell segmentation for reproducibility in bioimage analysis"
<p>This is the raw data sets used in "Automated cell segmentation for reproducibility in bioimage analysis", published in Synthetic Biology (Oxford Academic)</p>
Safety and Bioimaging Trial of DS-8895a in Patients With Advanced EphA2 Positive Cancers
ClinicalTrials.gov study NCT02252211. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Dataset for interactive course on BioImage Analysis with Python (BIAPy)
<p>This dataset can be used to run the course on image processing with Python available here: <a href="https://github.com/guiwitz/neubias_academy_biapy">https://github.com/guiwitz/neubias_academy_biapy</a></p> <p>It combines microscopy images from different publicly available sources. All files are either in the Public Domain (PD) or released with a CC-BY license. The list of the original location of the data as well as their licenses can be found in the LICENSE file.</p>
Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses
<p>Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation, ground truth estimation, and model training can mitigate this risk. To evaluate this integrated process, we compared different DL-based analysis approaches. With data from two model organisms (mice, zebrafish) and five laboratories, we show that ground truth estimation from multiple human annotators helps to establish objectivity in fluorescent feature annotations. Furthermore, ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible DL-based bioimage analyses.</p>
Bioimages (Vanderbilt): Bioimages Vanderbilt (200) DwCA
<p></p>
Elemental bioimaging and transcriptomics – no evidence of altered gene expression after single injection of Gadolinium-based contrast agents in mice cerebellum
<p>single read RNA data for GBCA study</p>
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.