BEXIS2 Workshop - Part1

Time table
2
• 9:00 – 12 -- Basics
• 12:00 – 13:00 -- Lunch
• 13:00 – 15:00 -- Advanced
• 15:00 – 15:30 -- Break
• 15:30 – 16:00 -- Q/A
• Homework (required for certification)

SPP 2089 data management
Part I
Basic information

5
Part I: Basic information
• Introduction (Doris)
• Accounts (Susanne)
• Labeling (Susanne)
• Break
• Research data management
• Store data: upload data
• Use data: download data
• Share data: data security

6
• Break

7
• Break

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ACCOUNTS • SPP 2089 website
• UFZ account
• BEXIS2 platform

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Login to SPP website
https://www.ufz.de/spp-rhizosphere/index.php?en=43202

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Login to SPP website
https://www.ufz.de/spp-rhizosphere/index.php?en=43517
Username:
First letter of given name
family name
Password:
3 letters and
3 numbers

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UFZ account
https://www.intranet.ufz.de/admin.php
Account is related to the automated emails
Username: ext family name (at least partly)
Password: Your choice
• Related to automated emails
• Account expires if you don‘t renew you password!

12
UFZ account
https://www.intranet.ufz.de/admin.php

UFZ archive
• Please contact us for help and guidance during this process
14

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UFZ cloud
https://nc.ufz.de/login
Use UFZ account login
Username: ext family name (at least partly)
Password: Your choice
Account expires if password is not renewed

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BEXIS2 account
https://bexis.ufz.de:4433/
Account does not expire.

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BEXIS2 account
Register with your work email address
Username: Your choice
Password: At least 6 characters

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BEXIS2 account
A short name of your institute
(e.g., UFZ Halle, Uni Kiel, TU Munich)
Save and wait for setting permissions

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• Break

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Labeling • Labeling samples
• Field site
• Column experiments

Universal labelling code
Please use the universal labelling code
Labelling of samples
• Substrate: Loam = L
Sand = S
don’t use clay, sandy loam….
• Genotype: Wild type = WT
hairless Mutant = rth3
don’t use mutant +/- …
• Biological replicates: REP1, REP2, REP3, ….
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Universal labelling code
• Labelling of soil column experiments
[projectnumber_SCE#_C#];
• Soil colum expriment = SCE01,…
• Column in the experiment = C01
• example: P21_SCE01_C01
• Labelling of sampling campaigns in soil plot experiment
[projectnumber_SPE_sampling date_FP#_type of sample#];
• Field plot = FP01, FP02..
• Depth 0-20 cm = D00_20
• Sampling of several points within each plot = a, b, c
• example: P21_SPE_20181105_FP01_UC#
• You may extend the name by providing further details if required
(i.e. bulk/rhizosphere/rhizoplane)…
• If you extend the details, communicate that to your cooperation partners
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Planned SPE
We are planning to sow the maize on the 26th and 27th of April 2022.
Furthermore, we are planning 4 samplings this year:
BBCH14: 08th-10th of June
BBCH19: 29th of June- 01st of July
BBCH59: 10th-12th of August
BBCH83: 28th -30th of September
The final harvest is planned on the 12th-14th of October 2022.
SPE_annual variation in precipitation
SPE_legacy
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Planned SCE
Please provide date and description that other projects can join
SCE_drought P7, P3, P6
SCE_drought-long P3, P6
SCE_compaction P21
SCE_contact
SCE_biopore
SCE_decay P19, P21
SCE_mucilage P4
SCE_nutrient deficiency P7
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New members of staff
Please provide the following information for each new member of staff
• Name
• Email address
• Postal address
• Position (PhD, PostDoc, PI…)
• Photo
Please inform the coordination if a member of staff
• is leaving the project permanently or for a longer period of time
• move to a new institute
• changes of names or email addresses
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28
• Welcome (Doris)
• Break

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• Break

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It is data collected or produced in the course of scientific research activities and
used as evidence in the research process, or commonly accepted in the research
community as necessary to validate research findings and results (European open
science cloud [1]).
Research data might include measurement data, laboratory values, audiovisual
information, texts, survey data, objects from collections, or samples that were
created, developed or evaluated during scientific work. Methodical forms of
testing such as questionnaires, software and simulations may also produce
important results for scientific research and should therefore also be categorized
as research data (DFG Guidelines on the Handling of Research Data [2]).
Research data

Generate
Store
Use
Share
Archive
Destroy
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Wrong or out-of-date data must be
permanently erased.
Note: End-of-life data destruction is the
responsibility of all stakeholders.
Research data life cycle
Data samples from the field,
Collected data from sensors or
devices like CT scan images.
Store data in a remote and
secure location like a data
repository or a hard drive
located in the library to keep
data safe for a long time.
Access, study, or process data to do
analysis and conclude.
Write data in a notebook,
Enter data in an Excel sheet,
record data in a hard drive,…
Be aware of using the agreed
labeling method.
Share data amongst internal
colleagues or partners outside
of your organization, with SPP
2089 colleagues.
Use Email, a transfer site like
NextCloud, or hard drive.

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Why using a data management platform (DMP)
1. DMP supports data throughout its life cycle.
2. All components of the research process must be available to ensure
transparency, reproducibility, and reusability [3].
3. A DMP gathers research data in one place and keeps it usable for a
long time.
4. A DMP has to deal with security and privacy concerns due to
collecting private data.
5. Using a data management system is a DFG requirement, and it is
mentioned in the SPP 2089 bylaws.

BEXIS2 URL: https://bexis.ufz.de:4433/
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SPP 2089 data management platform: BEXIS2

Generate
Store
Use
Share
Archive
Destroy
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BEXIS2 administrator can remove
incorrect or useless data forever.
Of course, it requires special
permission from the data owner.
BEXIS2 is a free and open source
software supporting researchers in
managing their data throughout
the data life cycle from data storing
to sharing research data [4].
BEXIS2 keeps track of the
evolution of a dataset and returns
to any previous version if needed.
Start to store data in BEXIS2
at this point of your work.
Data security is a major
concern for BEXIS2. It specify
fine grained data permissions
on who can view, access, or
update a dataset.
Why using BEXIS2?
BEXIS2 can be used for long-
term data archiving even as
the publication requirement.
In the near future, you can
get DOI for each dataset.

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Questions?! Generate
Store
Use
Share
Archive
Destroy

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• Break

Generate
Store
Use
Share
Archive
Destroy
1. Create a dataset
2. Provide the metadata
3. Design the data structure
4. Upload data
1. Upload a data table
2. Upload a small file
3. Upload big files
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Data store workflow in BEXIS2

1. Create a dataset
4. Upload data
1. Upload data table
3. Upload big files
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Data store workflow

Create a dataset
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1. Create a dataset

1.1. Create a new or a copy dataset
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1. Create a dataset

1.2. Create a new data structure
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1. Create a dataset

1.3. Select the SPP 2089 Metadata
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1. Create a dataset

1. Create a dataset
4. Upload data
3. Upload big files
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Data store workflow

The metadata structure is designed for SPP 2089 purposes.
Minimum meta information is required.
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1. Create a dataset
4. Upload data
3. Upload big files
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Data store workflow

Example of a data table
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Example of a data structure
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BEXIS2 assigns an empty data structure to a dataset.
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Select a variable template
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Select a variable template
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Check
• Description
• Unit
• Data type

Search for an existing variable template
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Search for
• Name
• Description
• Unit
• Data type

Create a new variable template
Enter reusable name and description
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Weight Mucilage weight
Root weight
Dried root weight

Select proper variable templates
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1
1 2 3 4
String = Text
Integer = Whole number
Double, decimal = Real number
5
2
3
4
5

Each variable needs a name and description
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Data structure needs a name and description
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Download Excel template
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Excel template contains macros to check data quality
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Excel template

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1
2
3
4
5
Excel template: enable macros

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Excel template: Copy data into the Excel template

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Excel template: Check the data area

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Excel template: Check errors

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Excel template: Format Cells
Double click

1. Create a dataset
4. Upload data
3. Upload big files
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Data store workflow

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4.1. Upload data table: Select file

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4.1. Upload data table: Upload Excel template

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4.1. Upload data table: Summary

1. Create a dataset
4. Upload data
3. Upload big files
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Data store workflow
• Check out acceptable file extensions such as
PDF, CSV, or ZIP.
• Each time you can upload only one small file.
• The maximum file size is 1 GB.

Create a file format dataset!
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4.2. Upload a small file
1
2
5

The maximum file size is 1 GB.
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4.2. Upload a small file: Select file
Acceptable file extensions

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4.2. Upload a small file: Specify dataset

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4.2. Upload a small file: Summary

1. Create a dataset
4. Upload data
3. Upload big files
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Data store workflow

1. Upload data into a data
repository
– Any data repository such as
Pangea or Zenodo
– UFZ offers its archive system
2. Enter information in BEXIS2
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4.3. Upload big files

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4.3. Upload big files: Create a tabular dataset
- Upload big files in a
data repository
- Collect links and
information in BEXIS2
Select “SPP External Data Storage” data structure

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Note: If you have more than
one link, mention it as remark
and upload the link data table.
2. Enter the link of
archived data as remark.
1. Upload big files in a data repository such as Pangea.
UFZ archive system is an offer to use.
4.3. Upload big files: Provide metadata

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Row number: An ordinal number like 1, 2, 3
SPP_ID: A combination of the project number and the purpose (e.g., P10_SCE01_Paper1)
Link to Archive: Link to the respective data in a data repository
Name of external drive: Name of the external hard drives, if applicable (e.g., SPP_P10_SCE01_Part1a)
4.3. Upload big files: Upload a list of links

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• Break

Generate
Store
Use
Share
Archive
Destroy
1. Download the whole dataset
2. Download data table
3. Access data via R
4. Contact data owners
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Use a dataset

Download metadata and data structure in a zip file
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1. Download the whole dataset

Download data in Excel or text format
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2. Download data table

install.packages ("usethis")
library (usethis)
install.packages ("devtools")
library (devtools)
install.packages ("httr")
library (httr)
install.packages ("jsonlite")
library (jsonlite)
install.packages ("XML")
library (XML)
3.1. Install necessary packages:
(usethis, devtools, jsonlite, XML)
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3.2. Download and install rBExIS package
1. rBExIS package is available in SPP Intranet data
management web page.
2. Install the package from your computer
• devtools::install (“PATH_TO_THE _rBExIS”)
3. Load “rBExIS” package
• library (rBExIS)
• load_all ("rBExIS")
• check ("rBExIS")
• require (rBExIS)
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3.3. Set options for the rBExIS package
1. Find your tocken
2. Set rBExIS options
bexis.options("token" = "YOUR_TOKEN")
bexis.options("base_url" = "https://spp2089.ufz.de:4433")
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3.4. rBExIS functions
1. A list of all dataset Ids
bexis.get.datasets ()
2. Retrieve data from a dataset
specified by the dataset Id
bexis.get.dataset_by (id = xy)
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If you cannot see the primary data,
contact data owner or contact person.
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4. Contact data owners

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• Break

Generate
Store
Use
Share
Archive
Destroy
• Adjust permission settings
• BEXIS2 administrator has
access to permission settings
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Share a dataset

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Share a dataset: Adjust permission settings
• Read: Reading and downloading
primary data
• Write: Editing metadata and
uploading/updating data
• Delete: only the BEXIS2
administrator can delete a dataset
• Grant: Seeing permission tab
• SPP2089 Group: Applying for all
SPP 2089 members

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Generate
Store
Use
Share
Archive
Destroy
Research data life cycle

Generate
Store
Use
Share
Archive
Destroy
• The SPP 2089 BEXIS2 platform
will be available forever!
• You can use the SPP 2089 BEXIS2
as the data repository required
for publications.
– Need special settings
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Archive data

Generate
Store
Use
Share
Archive
Destroy
• The BEXIS2 administrator can
delete a whole or the latest
version of a dataset permanently.
• You can delete a data structure by
yourself.
• You can delete unused variable
templates by yourself.
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Destroy a dataset

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The End of the Part I
Thank you for your attention!
We will start Part II at 1 p.m.

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[1] EOSC glossary: https://eosc-portal.eu/glossary
[2] DFG Guidelines on the Handling of Research Data:
https://www.dfg.de/download/pdf/foerderung/grundlagen_dfg_foerderung/forschungsdaten/g
uidelines_research_data.pdf
[3] Wilkinson, M. D. et al. (2016). https://www.nature.com/articles/sdata201618
[4] BEXIS Research Data Management: https://fusion.cs.uni-jena.de/bpp/
References

BEXIS2 Workshop - Part1

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