Downsampled data from FlowRepository: FR-FCM-Z3WR
- figshare.com
csvUpdated Dec 2, 2024
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EmailClick to copy linkLink copiedCiteDaniel Tyrrell (2024). Downsampled data from FlowRepository: FR-FCM-Z3WR [Dataset]. http://doi.org/10.6084/m9.figshare.27940719.v1csvAvailable download formatsUnique identifierhttps://doi.org/10.6084/m9.figshare.27940719.v1Dataset updatedDec 2, 2024AuthorsDaniel TyrrellLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionSpectral flow cytometry provides greater insights into cellular heterogeneity by simultaneous measurement of up to 50 markers. However, analyzing such high-dimensional (HD) data is complex through traditional manual gating strategy. To address this gap, we developed CAFE as an open-source Python-based web application with a graphical user interface. Built with Streamlit, CAFE incorporates libraries such as Scanpy for single-cell analysis, Pandas and PyArrow for efficient data handling, and Matplotlib, Seaborn, Plotly for creating customizable figures. Its robust toolset includes density-based down-sampling, dimensionality reduction, batch correction, Leiden-based clustering, cluster merging and annotation. Using CAFE, we demonstrated analysis of a human PBMC dataset of 350,000 cells identifying 16 distinct cell clusters. CAFE can generate publication-ready figures in real time via interactive slider controls and dropdown menus, eliminating the need for coding expertise and making HD data analysis accessible to all. CAFE is licensed under MIT and is freely available at https://github.com/mhbsiam/cafe.
- h
tulu-3-unfiltered
- huggingface.co
Updated Feb 26, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCiteHamish Ivison (2025). tulu-3-unfiltered [Dataset]. https://huggingface.co/datasets/hamishivi/tulu-3-unfilteredCroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.Dataset updatedFeb 26, 2025AuthorsHamish IvisonLicensehttps://choosealicense.com/licenses/odc-by/https://choosealicense.com/licenses/odc-by/
DescriptionTulu 3 Unfiltered
This is an 'unfiltered' version of the Tulu 3 SFT mixture, created by collating the original Tulu 3 sources and avoiding downsampling.
DetailsThe dataset consists of a mix of :
CoCoNot (ODC-BY-1.0) (Brahman et al., 2024) FLAN v2 (Apache 2.0) (Longpre et al., 2023) No Robots (CC-BY-NC-4.0) (Rajani et al. 2023) OpenAssistant Guanaco (Apache 2.0) (Kopf et al., 2024) Tulu 3 Persona MATH (ODC-BY-1.0) Tulu 3 Persona GSM (ODC-BY-1.0) Tulu 3 Persona Python… See the full description on the dataset page: https://huggingface.co/datasets/hamishivi/tulu-3-unfiltered.
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FacebookTwitterEmailAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Spectral flow cytometry provides greater insights into cellular heterogeneity by simultaneous measurement of up to 50 markers. However, analyzing such high-dimensional (HD) data is complex through traditional manual gating strategy. To address this gap, we developed CAFE as an open-source Python-based web application with a graphical user interface. Built with Streamlit, CAFE incorporates libraries such as Scanpy for single-cell analysis, Pandas and PyArrow for efficient data handling, and Matplotlib, Seaborn, Plotly for creating customizable figures. Its robust toolset includes density-based down-sampling, dimensionality reduction, batch correction, Leiden-based clustering, cluster merging and annotation. Using CAFE, we demonstrated analysis of a human PBMC dataset of 350,000 cells identifying 16 distinct cell clusters. CAFE can generate publication-ready figures in real time via interactive slider controls and dropdown menus, eliminating the need for coding expertise and making HD data analysis accessible to all. CAFE is licensed under MIT and is freely available at https://github.com/mhbsiam/cafe.