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FAIR Data Austria DB Repository
DBRepo
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57c8382a
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57c8382a
authored
1 year ago
by
Martin Weise
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author
:
Martin Weise
---
# Python Library
[

](https://pypi.org/project/dbrepo/){ tabindex=-1 }
## tl;dr
[
:fontawesome-solid-cube: View Docs
](
../sphinx
)
{ .md-button .md-button--primary }
[
:fontawesome-brands-python: PyPI
](
https://pypi.org/project/dbrepo/
)
{ .md-button .md-button--secondary }
## Installing
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author
:
Martin Weise
---
[

](https://artifacthub.io/packages/helm/dbrepo/dbrepo){ tabindex=-1 }
## TL;DR
To install DBRepo in your existing cluster, download the
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[

](https://gitlab.phaidra.org/fair-data-austria-db-repository/fda-services){ tabindex=-1 }
[

](https://gitlab.phaidra.org/fair-data-austria-db-repository/fda-services){ tabindex=-1 }
Documentation for version:
[
v1.4.3
](
https://gitlab.phaidra.org/fair-data-austria-db-repository/fda-services/-/releases
)
.
## Features
DBRepo is a repository for data in databases that are used from the beginning of a research project supporting data
evolution, -citation and -versioning. It implements the
[
RDA WGDC recommendations
](
https://doi.org/10.1162/99608f92.be565013
)
on precisely identifying arbitrary subsets of
data.
##
# Built-in search
##
Why use DBRepo?
DBRepo makes your dataset searchable without extra effort: most metadata is generated automatically for data in your
databases. The fast and powerful OpenSearch database allows a fast retrieval of any information. Adding semantic mapping
through a suggestion-feature, allows machines to properly understand the context of your data.
[
Learn more.
](
../system-services-search/
)
*
**aaa**
bbbb
### Citable datasets
Installing DBRepo is very easy or just
[
give it a try online
](
https://test.dbrepo.tuwien.ac.at
)
.
Adopting the recommendations of the RDA-WGDC, arbitrary subsets can be precisely, persistently identified using
system-versioned tables of MariaDB and the DataCite schema for minting DOIs. External systems i.e. metadata harvesters
(OpenAIRE, Google Datasets) can access these datasets through OAI-PMH, JSON-LD and FAIR Signposting protocols.
[
Learn more.
](
../system-services-metadata/
)
## Who is using DBRepo?
### Powerful API for Data Scientists
TBD
With our strongly typed Python Library, Data Scientists can import, export and work with data from Jupyter Notebook or
Python script, optionally using Pandas DataFrames. For example: the AMQP API Client can collect continuous data from
edge devices like sensors and store them asynchronous in DBRepo.
[
Learn more.
](
../usage-python/
)
### Cloud Native
Our lightweight Helm chart allows for installations on any cloud provider or private-cloud setting that has an
underlying PV storage provider. DBRepo can be installed from
the
[
Artifact Hub
](
https://artifacthub.io/packages/helm/dbrepo/dbrepo
)
repository. Databases are managed as MariaDB
Galera Cluster with high degree of availability ensuring your data is always accessible.
[
Learn more.
](
../deployment-helm/
)
## Demo Site
We run a small demonstration instance so you can see the latest version of DBRepo in action. The demonstration instance
is updated with new releases and should be considered ephemeral.
## How can I try DBRepo
[
:fontawesome-solid-flask: Demonstration Instance
](
https://test.dbrepo.tuwien.ac.at
)
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We present a database repository system that allows researchers to ingest data into a central, versioned repository
through common interfaces, provides efficient access to arbitrary subsets of data even when the underlying data store is
evolving, allows reproducing of query results and supports findable-, accessible-, interoperable- and reusable data.
## Features
### Built-in search
DBRepo makes your dataset searchable without extra effort: most metadata is generated automatically for data in your
databases. The fast and powerful OpenSearch database allows a fast retrieval of any information. Adding semantic mapping
through a suggestion-feature, allows machines to properly understand the context of your data.
[
Learn more.
](
../system-services-search/
)
### Citable datasets
Adopting the recommendations of the RDA-WGDC, arbitrary subsets can be precisely, persistently identified using
system-versioned tables of MariaDB and the DataCite schema for minting DOIs. External systems i.e. metadata harvesters
(OpenAIRE, Google Datasets) can access these datasets through OAI-PMH, JSON-LD and FAIR Signposting protocols.
[
Learn more.
](
../system-services-metadata/
)
### Powerful API for Data Scientists
With our strongly typed Python Library, Data Scientists can import, export and work with data from Jupyter Notebook or
Python script, optionally using Pandas DataFrames. For example: the AMQP API Client can collect continuous data from
edge devices like sensors and store them asynchronous in DBRepo.
[
Learn more.
](
../usage-python/
)
### Cloud Native
Our lightweight Helm chart allows for installations on any cloud provider or private-cloud setting that has an
underlying PV storage provider. DBRepo can be installed from
the
[
Artifact Hub
](
https://artifacthub.io/packages/helm/dbrepo/dbrepo
)
repository. Databases are managed as MariaDB
Galera Cluster with high degree of availability ensuring your data is always accessible.
[
Learn more.
](
../deployment-helm/
)
## Demo Site
We run a small demonstration instance so you can see the latest version of DBRepo in action. The demonstration instance
is updated with new releases and should be considered ephemeral.
\ No newline at end of file
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