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DataDock: An Open Source Data Hub for Research

Lexington Whalen, Homayoun Valafar

TL;DR

DataDock is a specialized file transfer service crafted for specifically for researchers that can be customized to suit the unique requirements of any research team, and is able to evolve to meet the needs of the research community.

Abstract

Every research project necessitates data, often requiring sharing and collaborative review within a team. However, there is a dearth of good open-source data sharing and reviewing services. Existing file-sharing services generally mandate paid subscriptions for increased storage or additional members, diverting research funds from addressing the core research problem that a lab is attempting to work on. Moreover, these services often lack direct features for reviewing or commenting on data quality, a vital part of ensuring high quality data generation. In response to these challenges, we present DataDock, a specialized file transfer service crafted for specifically for researchers. DataDock operates as an application hosted on a research lab server. This design ensures that, with access to a machine and an internet connection, teams can facilitate file storage, transfer, and review without incurring extra costs. Being an open-source project, DataDock can be customized to suit the unique requirements of any research team, and is able to evolve to meet the needs of the research community. We also note that there are no limitations with respect to what data can be shared, downloaded, or commented on. As DataDock is agnostic to the file type, it can be used in any field from bioinformatics to particle physics; as long as it can be stored in a file, it can be shared. We open source the code here: https://github.com/lxaw/DataDock

DataDock: An Open Source Data Hub for Research

TL;DR

DataDock is a specialized file transfer service crafted for specifically for researchers that can be customized to suit the unique requirements of any research team, and is able to evolve to meet the needs of the research community.

Abstract

Every research project necessitates data, often requiring sharing and collaborative review within a team. However, there is a dearth of good open-source data sharing and reviewing services. Existing file-sharing services generally mandate paid subscriptions for increased storage or additional members, diverting research funds from addressing the core research problem that a lab is attempting to work on. Moreover, these services often lack direct features for reviewing or commenting on data quality, a vital part of ensuring high quality data generation. In response to these challenges, we present DataDock, a specialized file transfer service crafted for specifically for researchers. DataDock operates as an application hosted on a research lab server. This design ensures that, with access to a machine and an internet connection, teams can facilitate file storage, transfer, and review without incurring extra costs. Being an open-source project, DataDock can be customized to suit the unique requirements of any research team, and is able to evolve to meet the needs of the research community. We also note that there are no limitations with respect to what data can be shared, downloaded, or commented on. As DataDock is agnostic to the file type, it can be used in any field from bioinformatics to particle physics; as long as it can be stored in a file, it can be shared. We open source the code here: https://github.com/lxaw/DataDock
Paper Structure (16 sections, 6 figures)

This paper contains 16 sections, 6 figures.

Table of Contents

  1. Introduction
  2. The Problem
  3. Current Alternatives
  4. Issues with Current Approaches
  5. DataDock Architecture
  6. Backend
  7. Frontend
  8. Integration
  9. Current Product
  10. Data Upload and Management
  11. User Authentication and Account Management
  12. Data Discoverability and Review
  13. Collaboration and Communication
  14. Community Building
  15. Notifications and Updates
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Downloading data. The download page is designed to be similar to that of an e-commerce website.
  • Figure 2: Uploading data. You are able to add a name, description, visibility settings, organization settings, and tags.
  • Figure 3: Searching data. You can filter by name, file type, tags, and author.
  • Figure 4: Reviewing data. You can rate the dataset from 1-5, and comment on its utility.
  • Figure 5: Conversations. You can start a conversation with a user in order to find out more about the data, methods, etc.
  • ...and 1 more figures