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…ture-highlight * 'master' of github.com:scalableminds/webknossos: Hide unreported datasets (#3883) Update puppeteer and refresh screenshots (#3914) only show team names of own organization (#3928) Enable merger mode in skeleton and hybrid tracings (#3619) allow uploading nml for public dataset of different orga (#3929) Always make wheel listeners not passive to allow preventDefault (#3939) Enhance tree search functionallity (#3878) add webknossos-connect to setup (#3913) Update README.md (#3923) Add shortcut to maximize golden layout panes (#3927) Perform bucket picking in web workers and other performance optimizations (#3902) remove alt text for abstract brain loading image (#3930) updated documentation front page (#3917)
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# webKnossos | ||
Cellular-resolution connectomics is currently substantially limited by the throughput and efficiency of data analysis. | ||
Current solutions require an efficient integration of automated image analysis with massive manual data annotation. | ||
To scale such annotation efforts it is decisive to be able to crowd source data analysis online. | ||
Here we present **webKnossos**. | ||
<img align="right" src="https://static.webknossos.org/images/oxalis.svg" alt="webKnossos Logo" /> | ||
webKnossos is an open-source tool for annotating and exploring large 3D image datasets. | ||
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> Boergens, Berning, Bocklisch, Bräunlein, Drawitsch, Frohnhofen, Herold, Otto, Rzepka, Werkmeister, Werner, Wiese, Wissler and Helmstaedter | ||
webKnossos: efficient online 3D data annotation for connectomics. | ||
[Nature Methods (2017) DOI:10.1038/NMETH.4331.](https://www.nature.com/articles/nmeth.4331) | ||
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![webKnossos logo](https://webknossos.org/images/oxalis.svg) | ||
* Fly through your data for fast skeletonization and proof-reading | ||
* Create 3D training data for automated segmentations efficiently | ||
* Scale data reconstruction projects with crowdsourcing workflows | ||
* Share datasets and annotations with collaborating scientists | ||
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[Start using webKnossos](https://webknossos.org) - [User Documentation](https://docs.webknossos.org) - [Contact us](mailto:[email protected]) | ||
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[![]( https://img.shields.io/circleci/project/github/scalableminds/webknossos/master.svg?logo=circleci)](https://circleci.com/gh/scalableminds/webknossos) | ||
[![](https://img.shields.io/github/release/scalableminds/webknossos.svg)](https://github.com/scalableminds/webknossos/releases/latest) | ||
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* User and task management for high-throughput crowdsourcing | ||
* Sharing and collaboration features | ||
* [Standalone datastore component](https://github.com/scalableminds/webknossos/tree/master/webknossos-datastore) for flexible deployments | ||
* [Supported dataset formats: WKW (Optimized), KNOSSOS cubes](https://github.com/scalableminds/webknossos/wiki/Datasets), [Neuroglancer Precomputed, and BossDB](https://github.com/scalableminds/webknossos-connect) | ||
* [Supported dataset formats: WKW, KNOSSOS cubes](https://github.com/scalableminds/webknossos/wiki/Datasets), [Neuroglancer Precomputed, and BossDB](https://github.com/scalableminds/webknossos-connect) | ||
* Supported image formats: Grayscale, Segmentation Maps, RGB, Multi-Channel | ||
* [Support for 3D mesh rendering and on-the-fly isosurface generation](https://docs.webknossos.org/guides/mesh_visualization) | ||
* [Documented frontend API for user scripts](https://webknossos.org/assets/docs/frontend-api/index.html), REST API for backend access | ||
* Open-source development with [automated test suite](https://circleci.com/gh/scalableminds/webknossos) | ||
* [Docker-based deployment](https://hub.docker.com/r/scalableminds/webknossos/) for production and development | ||
* [Detailed Documentation](https://docs.webknossos.org) | ||
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## Publication | ||
> Boergens, Berning, Bocklisch, Bräunlein, Drawitsch, Frohnhofen, Herold, Otto, Rzepka, Werkmeister, Werner, Wiese, Wissler and Helmstaedter | ||
> webKnossos: efficient online 3D data annotation for connectomics. | ||
> [Nature Methods (2017) DOI:10.1038/NMETH.4331.](https://www.nature.com/articles/nmeth.4331) | ||
[Read more about the original publication.](https://publication.webknossos.org) | ||
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## Development setup | ||
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## Development installation | ||
### Docker | ||
This is the fastest way to try webKnossos. | ||
Docker CE 17+ and Docker Compose 1.18+ is required. | ||
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Will fetch all Scala, Java and node dependencies and run the application on Port 9000. | ||
Make sure that the PostgreSQL and Redis services are running before you start the application. | ||
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## Production setup | ||
[See wiki](https://github.com/scalableminds/webknossos/wiki/Production-setup) for recommended production setup. | ||
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## Upgrades | ||
For upgrades, please check the [changelog](CHANGELOG.md) & [migration guide](MIGRATIONS.md). | ||
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## Tests | ||
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datasets.json |
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{ | ||
"server": {"host": "0.0.0.0", "port": 8000, "url": "http://localhost:8000"}, | ||
"datastore": {"name": "connect", "key": "secret-key"}, | ||
"webknossos": {"url": "http://localhost:9000"}, | ||
"backends": {"neuroglancer": {}}, | ||
"datasets_path": "data/datasets.json" | ||
} |
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