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-[](#contributors-)
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-# JZFS
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-#### 🚧 Current Status: Incubating - JZFS is not ready for production usage. The API is still evolving and documentation is lacking.
-JZFS is a version control filesystem based on Git protocol for data management and publication with a command line interface and a Python API. With JZFS, you can version control arbitrarily large data, share or consume data, record your data’s provenance, and work computationally reproducible.
-
-## Current Status and Roadmap
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Git Based & Version Control & Joint Management for code, data, model and their relationship
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+> Delivers distributed data management system that keeps track of your data from code to PB scale dataset and ensures reproducibility.
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+## Introduction
+
+**JZFS** is an open-source, cloud-native version control filesystem based on Git protocol for data management and publication with a command line interface and a Python API. With JZFS, you can version control arbitrarily large data, share or consume data, record your data’s provenance, and work computationally reproducible.
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+JZFS adapts principles of open-source software development and distribution to address the technical challenges of data management, data sharing, and digital provenance collection across the life cycle of digital objects.
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+Compared with code in software development, data tend not to be as precisely
+identified because data versioning is rarely or only coarsely practiced. Scientific computation
+is not reproducible enough, because data provenance, the information of how a digital file
+came to be, is often incomplete and rarely automatically captured. Last but not least, in
+the absence of standardized data packages, there is no uniform way to declare actionable
+data dependencies and derivative relationships between inputs and outputs of a computation. JZFS aims to solve these issues by providing streamlined, transparent management
+of code, data, computing environments, and their relationship.
+
+### Current Status and Roadmap
JZFS is still in the early development stages and is considered **incubating**. There is no commitment to ongoing maintenance or development. As the project evolves, this may change in the future. Therefore, we encourage you to explore, experiment, and contribute to JZFS, but do not attempt to use it in production.
The immediate next steps for the project are to fill obvious gaps, such as implementing error handling, removing panics throughout the codebase, supporting additional data types, and writing documentation. After that, development will be based on feedback and contributions.
+JZFS's long-term goal is to build data ecosystems that enable new innovations.
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+
### Reserach Data Management
JZFS is based on Git with extend capabilities, especially with respect to managing large files.
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all experimental metadata about the complete timeline of longitudinal and multimodal animal experiments,
including MRI, histology, electrophysiology, and behavior.
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Project planning and experimental details are recorded in an in-house relational cloud-based database.
A key element for both the database and the data storage is the
@@ -43,16 +104,12 @@ specific project is organized following the YODA principles (https://handbook.da
In preparation for publication and to facilitate
data reproducibility, the experimental raw and processed data is made publicly available on GitData.AI.
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JZFS is used as the central data management tool (Fig. above) and for version control: It keeps track of which
files were modified, when, and by whom, and provides the ability to restore previous states. To this end, JZFS
is agnostic of the data type and provides a unified interface for managing code and data files.
-#### joint management of code, data, and their relationship
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-### Added value
+### DataHub
Our central use case is the DataHub(Like Github, buf for Data),which essentially consists of a Git version control for data and a Git collaboration for data.
In the JZFS DataHub model, each node maintains a copy of the files and all of the history of each file.
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Git is designed to compute differences between versions quickly.
Generally, Git relies on human action to share and merge changes.
-Fault tolerance and trustless-ness are achieved via the separation of remotes from individual nodes.
-Each node in the network is maintaining it's own copy, its history and coordinating via one or many remotes.
-If a remote you trust gets corrupted, you have the ability to roll back to a previous good state and switch to a new remote.
+Fault tolerance and trustless-ness are achieved via the separation of remotes from individual nodes.
+Each node in the network is maintaining it's own copy, its history and coordinating via one or many remotes.
+If a remote you trust gets corrupted, you have the ability to roll back to a previous good state and switch to a new remote.
Even if you lose your copy you can rely on other nodes' copies to restore from.
This enables us to create transparency across internal and external data.
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It is crucial that data exchange works both within the company and in individually controllable data networks (data space).
+### Data Space
JZFS data space consists of so-called “DataHubs” like one or many remotes in Git, which are virtual data nodes for sharing data and building data networks.
A single DataHub manages various data connections and can join together with other hubs to form a network through targeted synchronization.
Based on data contracts mapped in the network, data can be released to other participants, enabling efficient data exchange.
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-JZFS offers git for data technology for exchanging data in data hub and data space.
+JZFS offers git for data technology for exchanging data in data hub and data space.
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The added value is clear: simple, transparent data management combined with intuitive linking and sharing of data in decentralized networks – data space.
This enables secure, trustworthy data exchange across organizational boundaries.
-JZFS's long-term goal is to build data ecosystems that enable new innovations.
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### Deploy
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