Merge branch 'preview' into cil

doc.zih.tu-dresden.de/docs/archive/install_jupyter.md

@@ -148,7 +148,7 @@ c.NotebookApp.allow_remote_access = True
 #SBATCH --time=02:30:00
 #SBATCH --mem=4000M
 #SBATCH -J "jupyter-notebook" # job-name
-#SBATCH -A p_marie
+#SBATCH -A p_number_crunch
 
 unset XDG_RUNTIME_DIR   # might be required when interactive instead of sbatch to avoid 'Permission denied error'
 srun jupyter notebook

doc.zih.tu-dresden.de/docs/contrib/content_rules.md

@@ -134,7 +134,7 @@ We follow this rules regarding prompts:
   an example invocation, perhaps with output, should be given with the normal `console` code block.
   See also [Code Block description below](#code-blocks-and-syntax-highlighting).
 * Using some magic, the prompt as well as the output is identified and will not be copied!
-* Stick to the [generic user name](#data-privacy-and-generic-user-name) `marie`.
+* Stick to the [generic user name](#data-privacy-and-generic-names) `marie`.
 
 ### Code Blocks and Syntax Highlighting
 
@@ -245,16 +245,17 @@ _Result_:
 
 ![lines](misc/highlight_lines.png)
 
-### Data Privacy and Generic User Name
+### Data Privacy and Generic Names
 
-Where possible, replace login, project name and other private data with clearly arbitrary placeholders.
-E.g., use the generic login `marie` and the corresponding project name `p_marie`.
+Where possible, replace login, project name and other private data with clearly arbitrary
+placeholders.  In particular, use the generic login `marie` and the project title `p_number_crunch`
+as placeholders.
 
 ```console
 marie@login$ ls -l
-drwxr-xr-x   3 marie p_marie      4096 Jan 24  2020 code
-drwxr-xr-x   3 marie p_marie      4096 Feb 12  2020 data
--rw-rw----   1 marie p_marie      4096 Jan 24  2020 readme.md
+drwxr-xr-x   3 marie p_number_crunch      4096 Jan 24  2020 code
+drwxr-xr-x   3 marie p_number_crunch      4096 Feb 12  2020 data
+-rw-rw----   1 marie p_number_crunch      4096 Jan 24  2020 readme.md
 ```
 
 ### Placeholders

doc.zih.tu-dresden.de/docs/data_lifecycle/intermediate_archive.md

@@ -20,7 +20,7 @@ Some more information:
 ## Access the Intermediate Archive
 
 For storing and restoring your data in/from the "Intermediate Archive" you can use the tool
-[Datamover](../data_transfer/datamover.md). To use the DataMover you have to login to ZIH systems.
+[Datamover](../data_transfer/datamover.md). To use the Datamover you have to login to ZIH systems.
 
 ### Store Data
 

doc.zih.tu-dresden.de/docs/data_lifecycle/longterm_preservation.md

@@ -74,13 +74,65 @@ Below are some examples:
 
 ## Where can I get more information about management of research data?
 
-Go to [http://www.forschungsdaten.org/en/](http://www.forschungsdaten.org/en/) to find more
-information about managing research data.
-
-## I want to store my research data at ZIH. How can I do that?
-
-You can use the following services for long-term preservation of research data:
-
- - [Long-term archive](https://tu-dresden.de/zih/dienste/service-katalog/arbeitsumgebung/backup_archiv/archivierung_am_zih)
- - [Long-term Archiving and Publication with OpARA (Open Access Repository and Archive)](https://tu-dresden.de/zih/dienste/service-katalog/arbeitsumgebung/backup_archiv/archivierung_am_zih#section-2-2)
- - [intermediate archive](https://tu-dresden.de/zih/dienste/service-katalog/arbeitsumgebung/backup_archiv/archivierung_am_zih#section-2-1)
+Please visit the wiki [forschungsdaten.org](https://www.forschungsdaten.org/en/) to learn more about
+all of the different aspects of research data management.
+
+For questions or individual consultations regarding research data management in general or any of
+its certain aspects, you can contact the
+[Service Center Research Data](https://tu-dresden.de/forschung-transfer/services-fuer-forschende/kontaktstelle-forschungsdaten?set_language=en)
+(Kontaktstelle Forschungsdaten) of TU Dresden.
+
+## I want to archive my research data at ZIH safely. How can I do that?
+
+For TU Dresden there exist two different services at ZIH for archiving research data. Both of
+them ensure high data safety by duplicating data internally at two separate locations and
+require some data preparation (e.g. packaging), but serve different use cases:
+
+### Storing very infrequently used data during the course of the project
+
+The intermediate archive is a tape storage easily accessible as a directory
+(`/archive/<HRSK-project>/` or `/archive/<login>/`) using the
+[export nodes](../data_transfer/export_nodes.md)
+and
+[Datamover tools](https://doc.zih.tu-dresden.de/data_transfer/datamover/) to move your data to.
+For detailed information please visit the
+[ZIH intermediate archive documentation](https://tu-dresden.de/zih/dienste/service-katalog/arbeitsumgebung/backup_archiv/archivierung_am_zih#section-2-1).
+
+!!! note
+
+    The usage of the HRSK-project-related archive is preferable to the login-related archive, as
+    this enables assigning access rights and responsibility across multiple researchers, due to the
+    common staff turnover in research.
+
+The use of the intermediate archive usually is limited by the end of the corresponding
+research project. Afterwards data is required to be removed, tidied up and submitted to a
+long-term repository (see next section).
+
+The intermediate archive is the preferred service when you keep large, mostly unused data volumes
+during the course of your research project; if you want or need to free storage capacities, but
+you are still not able to define certain or relevant datasets for long-term archival.
+
+If you are able to identify complete and final datasets, which you probably won't use actively
+anymore, then repositories as described in the next section may be the more appropriate selection.
+
+### Archiving data beyond the project lifetime, for 10 years and above
+
+According to good scientific practice (cf.
+[DFG guidelines, #17](https://www.dfg.de/download/pdf/foerderung/rechtliche_rahmenbedingungen/gute_wissenschaftliche_praxis/kodex_gwp.pdf))
+and
+[TU Dresden research data guidelines](https://tu-dresden.de/tu-dresden/qualitaetsmanagement/ressourcen/dateien/wisprax/Leitlinien-fuer-den-Umgang-mit-Forschungsdaten-an-der-TU-Dresden.pdf),
+relevant research data needs to be archived at least for 10 years. The
+[OpARA service](https://opara.zih.tu-dresden.de/xmlui/) (Open Access Repository and Archive) is the
+joint research data repository service for Saxon universities to address this requirement.
+
+Data can be uploaded and, to comply to the demands of long-term understanding of data, additional
+metadata and description must be added. Large datasets may be optionally imported beforehand. In
+this case, please contact the
+[TU Dresden Service Desk](mailto:servicedesk@tu-dresden.de?subject=OpARA:%20Data%20Import).
+Optionally, data can also be **published** by OpARA. To ensure data quality, data submissions
+undergo a review process.
+
+Beyond OpARA, it is also recommended to use discipline-specific data repositories for data
+publications. Usually those are well known in a scientific community, and offer better fitting
+options of data description and classification. Please visit [re3data.org](https://re3data.org)
+to look up a suitable one for your discipline.

doc.zih.tu-dresden.de/docs/data_transfer/datamover.md

 # Datamover - Data Transfer Inside ZIH Systems
 
-With the **datamover**, we provide a special data transfer machine for transferring data with best
-transfer speed between the filesystems of ZIH systems. The datamover machine is not accessible
+With the **Datamover**, we provide a special data transfer machine for transferring data with best
+transfer speed between the filesystems of ZIH systems. The Datamover machine is not accessible
 through SSH as it is dedicated to data transfers. To move or copy files from one filesystem to
 another filesystem, you have to use the following commands:
 
@@ -45,7 +45,7 @@ To identify the mount points of the different filesystems on the data transfer m
 !!! example "Copying data from `/beegfs/global0` to `/projects` filesystem."
 
     ``` console
-    marie@login$ dtcp -r /beegfs/global0/ws/marie-workdata/results /projects/p_marie/.
+    marie@login$ dtcp -r /beegfs/global0/ws/marie-workdata/results /projects/p_number_crunch/.
     ```
 
 !!! example "Moving data from `/beegfs/global0` to `/warm_archive` filesystem."
@@ -57,7 +57,7 @@ To identify the mount points of the different filesystems on the data transfer m
 !!! example "Archive data from `/beegfs/global0` to `/archiv` filesystem."
 
     ``` console
-    marie@login$ dttar -czf /archiv/p_marie/results.tgz /beegfs/global0/ws/marie-workdata/results
+    marie@login$ dttar -czf /archiv/p_number_crunch/results.tgz /beegfs/global0/ws/marie-workdata/results
     ```
 
 !!! warning
@@ -66,7 +66,7 @@ To identify the mount points of the different filesystems on the data transfer m
 !!! note
     The [warm archive](../data_lifecycle/warm_archive.md) and the `projects` filesystem are not
     writable from within batch jobs.
-    However, you can store the data in the `warm_archive` using the datamover.
+    However, you can store the data in the `warm_archive` using the Datamover.
 
 ## Transferring Files Between ZIH Systems and Group Drive
 

doc.zih.tu-dresden.de/docs/data_transfer/overview.md

@@ -14,9 +14,9 @@ copy data to/from ZIH systems. Please follow the link to the documentation on
 
 ## Data Transfer Inside ZIH Systems: Datamover
 
-The recommended way for data transfer inside ZIH Systems is the **datamover**. It is a special
+The recommended way for data transfer inside ZIH Systems is the **Datamover**. It is a special
 data transfer machine that provides the best transfer speed. To load, move, copy etc. files from one
 filesystem to another filesystem, you have to use commands prefixed with `dt`: `dtcp`, `dtwget`,
 `dtmv`, `dtrm`, `dtrsync`, `dttar`, `dtls`. These commands submit a job to the data transfer
 machines that execute the selected command.  Please refer to the detailed documentation regarding the
-[datamover](datamover.md).
+[Datamover](datamover.md).

doc.zih.tu-dresden.de/docs/jobs_and_resources/checkpoint_restart.md

@@ -63,9 +63,13 @@ To use it, first add a `dmtcp_launch` before your application call in your batch
 of MPI applications, you have to add the parameters `--ib --rm` and put it between `srun` and your
 application call, e.g.:
 
-```bash
-srun dmtcp_launch --ib --rm ./my-mpi-application
-```
+???+ my_script.sbatch
+
+    ```bash
+    [...]
+
+    srun dmtcp_launch --ib --rm ./my-mpi-application
+    ```
 
 !!! note
 
@@ -79,7 +83,7 @@ Then just substitute your usual `sbatch` call with `dmtcp_sbatch` and be sure to
 and `-i` parameters (don't forget you need to have loaded the `dmtcp` module).
 
 ```console
-marie@login$ dmtcp_sbatch --time 2-00:00:00 --interval 28000,800 my_batchfile.sh
+marie@login$ dmtcp_sbatch --time 2-00:00:00 --interval 28000,800 my_script.sbatch
 ```
 
 With `-t, --time` you set the total runtime of your calculations. This will be replaced in the batch

doc.zih.tu-dresden.de/docs/jobs_and_resources/slurm_examples.md

@@ -109,7 +109,7 @@ for `sbatch/srun` in this case is `--gres=gpu:[NUM_PER_NODE]` (where `NUM_PER_NO
     #SBATCH --cpus-per-task=6      # use 6 threads per task
     #SBATCH --gres=gpu:1           # use 1 GPU per node (i.e. use one GPU per task)
     #SBATCH --time=01:00:00        # run for 1 hour
-    #SBATCH --account=p_marie      # account CPU time to project p_marie
+    #SBATCH --account=p_number_crunch      # account CPU time to project p_number_crunch
 
     srun ./your/cuda/application   # start you application (probably requires MPI to use both nodes)
     ```

doc.zih.tu-dresden.de/docs/software/building_software.md

@@ -17,16 +17,16 @@ For instance, when using CMake and keeping your source in `/projects`, you could
 
 ```console
 # save path to your source directory:
-marie@login$ export SRCDIR=/projects/p_marie/mysource
+marie@login$ export SRCDIR=/projects/p_number_crunch/mysource
 
 # create a build directory in /scratch:
-marie@login$ mkdir /scratch/p_marie/mysoftware_build
+marie@login$ mkdir /scratch/p_number_crunch/mysoftware_build
 
 # change to build directory within /scratch:
-marie@login$ cd /scratch/p_marie/mysoftware_build
+marie@login$ cd /scratch/p_number_crunch/mysoftware_build
 
 # create Makefiles:
-marie@login$ cmake -DCMAKE_INSTALL_PREFIX=/projects/p_marie/mysoftware $SRCDIR
+marie@login$ cmake -DCMAKE_INSTALL_PREFIX=/projects/p_number_crunch/mysoftware $SRCDIR
 
 # build in a job:
 marie@login$ srun --mem-per-cpu=1500 --cpus-per-task=12 --pty make -j 12

doc.zih.tu-dresden.de/docs/software/data_analytics.md

@@ -29,7 +29,7 @@ can be installed individually by each user. If possible, the use of
 recommended (e.g. for Python). Likewise, software can be used within [containers](containers.md).
 
 For the transfer of larger amounts of data into and within the system, the
-[export nodes and datamover](../data_transfer/overview.md) should be used.
+[export nodes and Datamover](../data_transfer/overview.md) should be used.
 Data is stored in the [workspaces](../data_lifecycle/workspaces.md).
 Software modules or virtual environments can also be installed in workspaces to enable
 collaborative work even within larger groups.

doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md

@@ -219,7 +219,7 @@ from dask_jobqueue import SLURMCluster
 cluster = SLURMCluster(queue='alpha',
   cores=8,
   processes=2,
-  project='p_marie',
+  project='p_number_crunch',
   memory="8GB",
   walltime="00:30:00")
 
@@ -242,7 +242,7 @@ from dask import delayed
 cluster = SLURMCluster(queue='alpha',
   cores=8,
   processes=2,
-  project='p_marie',
+  project='p_number_crunch',
   memory="80GB",
   walltime="00:30:00",
   extra=['--resources gpu=1'])
@@ -294,7 +294,7 @@ for the Monte-Carlo estimation of Pi.
 
     #create a Slurm cluster, please specify your project
 
-    cluster = SLURMCluster(queue='alpha', cores=2, project='p_marie', memory="8GB", walltime="00:30:00", extra=['--resources gpu=1'], scheduler_options={"dashboard_address": f":{portdash}"})
+    cluster = SLURMCluster(queue='alpha', cores=2, project='p_number_crunch', memory="8GB", walltime="00:30:00", extra=['--resources gpu=1'], scheduler_options={"dashboard_address": f":{portdash}"})
 
     #submit the job to the scheduler with the number of nodes (here 2) requested:
 

doc.zih.tu-dresden.de/docs/software/fem_software.md

@@ -59,7 +59,7 @@ Slurm or [writing job files](../jobs_and_resources/slurm.md#job-files).
     #SBATCH --job-name=yyyy         # give a name, what ever you want
     #SBATCH --mail-type=END,FAIL    # send email when the job finished or failed
     #SBATCH --mail-user=<name>@mailbox.tu-dresden.de  # set your email
-    #SBATCH --account=p_marie       # charge compute time to project p_marie
+    #SBATCH --account=p_number_crunch       # charge compute time to project p_number_crunch
 
 
     # Abaqus has its own MPI

doc.zih.tu-dresden.de/docs/software/machine_learning.md

@@ -155,7 +155,7 @@ The following HPC related software is installed on all nodes:
 There are many different datasets designed for research purposes. If you would like to download some
 of them, keep in mind that many machine learning libraries have direct access to public datasets
 without downloading it, e.g. [TensorFlow Datasets](https://www.tensorflow.org/datasets). If you
-still need to download some datasets use [datamover](../data_transfer/datamover.md) machine.
+still need to download some datasets use [Datamover](../data_transfer/datamover.md) machine.
 
 ### The ImageNet Dataset
 

doc.zih.tu-dresden.de/docs/software/papi.md

@@ -105,11 +105,11 @@ multiple events, please check which events can be measured concurrently using th
     The PAPI tools must be run on the compute node, using an interactive shell or job.
 
 !!! example "Example: Determine the events on the partition `romeo` from a login node"
-    Let us assume, that you are in project `p_marie`. Then, use the following commands:
+    Let us assume, that you are in project `p_number_crunch`. Then, use the following commands:
 
     ```console
     marie@login$ module load PAPI
-    marie@login$ salloc --account=p_marie --partition=romeo
+    marie@login$ salloc --account=p_number_crunch --partition=romeo
     [...]
     marie@compute$ srun papi_avail
     marie@compute$ srun papi_native_avail
@@ -121,12 +121,12 @@ Instrument your application with either the high-level or low-level API. Load th
 compile your application against the  PAPI library.
 
 !!! example
-    Assuming that you are in project `p_marie`, use the following commands:
+    Assuming that you are in project `p_number_crunch`, use the following commands:
 
     ```console
     marie@login$ module load PAPI
     marie@login$ gcc app.c -o app -lpapi
-    marie@login$ salloc --account=p_marie --partition=romeo
+    marie@login$ salloc --account=p_number_crunch --partition=romeo
     marie@compute$ srun ./app
     [...]
     # Exit with Ctrl+D

doc.zih.tu-dresden.de/docs/software/private_modules.md

@@ -27,12 +27,12 @@ marie@compute$ cd privatemodules/<sw_name>
 ```
 
 Project private module files for software that can be used by all members of your group should be
-located in your global projects directory, e.g., `/projects/p_marie/privatemodules`. Thus, create
+located in your global projects directory, e.g., `/projects/p_number_crunch/privatemodules`. Thus, create
 this directory:
 
 ```console
-marie@compute$ mkdir --verbose --parents /projects/p_marie/privatemodules/<sw_name>
-marie@compute$ cd /projects/p_marie/privatemodules/<sw_name>
+marie@compute$ mkdir --verbose --parents /projects/p_number_crunch/privatemodules/<sw_name>
+marie@compute$ cd /projects/p_number_crunch/privatemodules/<sw_name>
 ```
 
 !!! note
@@ -110,7 +110,7 @@ marie@login$ module use $HOME/privatemodules
 for your private module files and
 
 ```console
-marie@login$ module use /projects/p_marie/privatemodules
+marie@login$ module use /projects/p_number_crunch/privatemodules
 ```
 
 for group private module files, respectively.

doc.zih.tu-dresden.de/util/check-spelling.sh

@@ -7,7 +7,7 @@ basedir=`dirname "$scriptpath"`
 basedir=`dirname "$basedir"`
 wordlistfile=$(realpath $basedir/wordlist.aspell)
 branch="origin/${CI_MERGE_REQUEST_TARGET_BRANCH_NAME:-preview}"
-files_to_skip=(doc.zih.tu-dresden.de/docs/accessibility.md doc.zih.tu-dresden.de/docs/data_protection_declaration.md doc.zih.tu-dresden.de/docs/legal_notice.md)
+files_to_skip=(doc.zih.tu-dresden.de/docs/accessibility.md doc.zih.tu-dresden.de/docs/data_protection_declaration.md doc.zih.tu-dresden.de/docs/legal_notice.md doc.zih.tu-dresden.de/docs/access/key_fingerprints.md)
 aspellmode=
 if aspell dump modes | grep -q markdown; then
   aspellmode="--mode=markdown"

doc.zih.tu-dresden.de/util/grep-forbidden-patterns.sh

@@ -46,9 +46,9 @@ i	^[ |]*|$
 Avoid spaces at end of lines.
 doc.zih.tu-dresden.de/docs/accessibility.md
 i	[[:space:]]$
-When referencing projects, please use p_marie for consistency.
+When referencing projects, please use p_number_crunch for consistency.
 
-i	\<p_	p_marie
+i	\<p_	p_number_crunch
 Avoid \`home\`. Use home without backticks instead.
 
 i	\`home\`

doc.zih.tu-dresden.de/wordlist.aspell

@@ -51,7 +51,7 @@ Dask
 dataframes
 DataFrames
 Dataheap
-datamover
+Datamover
 DataParallel
 dataset
 Dataset