TeSShub logo
  • Log In
    • Log in with UmbrellaID
    • Log in with Helmholtz AAI
    • Login
  • About
  • Events
  • Materials
  • Workflows
  • Collections
  • Learning paths
  • Spaces
  • Directory
    • Providers

PaN-Training makes use of some necessary cookies to provide its core functionality.

See our Privacy Policy for more information.

You can modify your cookie preferences at any time here, or from the link in the footer.

Allow necessary cookies
  1. Home
  2. Materials

Filter

  • Sort

  • Filter Clear filters

    • Keyword
    • MIGHTS
    • Python79
    • biodiversity73
    • microgalaxy51
    • work-in-progress42
    • jupyter-notebook39
    • metadata37
    • FAIR data32
    • elixir32
    • jbrowse131
    • gmod30
    • research data management30
    • R27
    • prokaryote26
    • one-health23
    • covid1922
    • NEPHEWS project21
    • NFDI4Chem21
    • fair21
    • ai-ml20
    • plants20
    • FAIR19
    • PaNOSC18
    • eukaryote18
    • introduction18
    • python-modular18
    • NFDI4Biodiversity and GfÖ Winter School 202217
    • ecologyanalysis17
    • interactive-tools17
    • Data management16
    • NFDI16
    • data management16
    • data management plan16
    • Reproducibility15
    • Data science13
    • cyoa13
    • earth-system13
    • paper-replication13
    • research data13
    • synchrotron13
    • assembly12
    • fair-data12
    • pedagogy12
    • train-the-trainers12
    • PaN11
    • Research Data Management11
    • data stewardship11
    • eLabFTW11
    • id-quant11
    • next-steps11
    • virology11
    • Large Language Model10
    • Photon science10
    • RDM10
    • application profile10
    • clinical-metaproteomics10
    • expands10
    • gai-llm10
    • label-TMT1110
    • nanopore10
    • workflows10
    • DDA9
    • Python biologists9
    • TANGO9
    • advanced9
    • analyses9
    • illumina9
    • label-free9
    • metabarcoding9
    • metagenomics9
    • ocean9
    • phylogenetics9
    • tango-controls9
    • ELN8
    • Elettra synchrotron8
    • Machine learning8
    • Programming8
    • SciCat8
    • multi-omics8
    • open data8
    • terminology8
    • 10x7
    • Data Management7
    • Data visualization7
    • EBV dataset7
    • EBV workflow7
    • Image segmentation7
    • Materials Science7
    • NMR7
    • Photon and Neutron7
    • chemistry7
    • core7
    • data life cycle7
    • discovery7
    • dmp7
    • end-to-end7
    • gnmx7
    • life sciences7
    • neoantigen7
    • prepare7
    • Show N_FILTERS more
    • Difficulty level
    • Not specified11
    • Show N_FILTERS more
    • Licence
    • License Not Specified
    • Show N_FILTERS more
  • Only show materials from current space
  • Show disabled materials
  • Show materials with broken links
  • Show archived materials
    • Date added
    • In the last 24 hours
    • In the last 1 week
    • In the last 1 month

Training materials

  • Subscribe via email

Email Subscription

Register training material

Keywords: MIGHTS

and Across all spaces: true

and Licence: License Not Specified

11 materials found
  • Inferring single cell trajectories with Monocle3 (R)

    paper-replication MIGHTS rmarkdown-notebook jupyter-notebook single-cell-CS-code
  • Inferring single cell trajectories with Monocle3

    paper-replication MIGHTS single-cell-CS
  • Inferring single cell trajectories with Scanpy

    paper-replication MIGHTS single-cell-CS
  • Inferring single cell trajectories with Scanpy (Python)

    paper-replication MIGHTS jupyter-notebook single-cell-CS-code
  • Filter, plot, and explore single cell RNA-seq data with Seurat

    paper-replication MIGHTS single-cell-CS
  • Filter, plot and explore single-cell RNA-seq data with Scanpy (Python)

    paper-replication MIGHTS jupyter-notebook single-cell-CS-code
  • Filter, plot and explore single-cell RNA-seq data with Scanpy

    paper-replication MIGHTS single-cell-CS
  • Combining single cell datasets after pre-processing

    paper-replication MIGHTS single-cell-CS
  • Filter, plot, and explore single cell RNA-seq data with Seurat (R)

    paper-replication MIGHTS rmarkdown-notebook jupyter-notebook single-cell-CS-code
  • Generating a single cell matrix using Alevin and combining datasets (bash + R)

    10x paper-replication MIGHTS jupyter-notebook single-cell-CS-code
  • 1
  • 2
Training eSupport System
pan-training@hzdr.de
Imprint
Contribute
About PaN-Training
Funding & acknowledgements
Privacy
Cookie preferences
Version: 1.5.1
Source code
API documentation

The training portal for the photon & neutron community is supported through the European Union's Horizon 2020 research and innovation programme, under grant agreement 857641, 823852, the Horizon Europe Framework under grant agreement 101129751, and the consortium DAPHNE4NFDI in the context of the work of the NFDI e.V. under the DFG - project number 460248799.