Source-linked AI summary
10 Simple Rules for the Care and Feeding of Scientific Data
Alyssa Goodman, Alberto Pepe, Alexander W. Blocker, Christine L. Borgman, Kyle Cranmer, Mercè Crosas, Rosanne Di Stefano, Yolanda Gil, Paul Groth, Margaret Hedstrom, David W. Hogg, Vinay Kashyap, Ashish Mahabal, Aneta Siemiginowska, Aleksandra Slavkovic
TL;DR
The article addresses how researchers can keep data and associated analyses valuable, recognizable, and usable beyond publication. It offers practical guidance on documenting, preserving, linking, publishing, crediting, and supporting data, workflows, code, and repositories. Its supported conclusion is that treating these materials as reusable research objects improves transparency, reproducibility, access, and recognition.
Problem
Researchers often need practical guidance for keeping data and associated analyses valuable, recognizable, and reusable beyond the publication of a journal article.
Method
The article presents a short set of practical rules covering data management, repositories, provenance, workflows, data and code publication, attribution, and community support.
Results
The guide identifies practices that support long-term access, interpretation, reuse, transparency, reproducibility, and credit for data, code, and associated analyses.
Takeaways & Limitations
Researchers should treat data and code as documented, citable, reusable research materials and help their communities build supporting infrastructure and norms.
Abstract
from arXiv · showhide
This article offers a short guide to the steps scientists can take to ensure that their data and associated analyses continue to be of value and to be recognized. In just the past few years, hundreds of scholarly papers and reports have been written on questions of data sharing, data provenance, research reproducibility, licensing, attribution, privacy, and more, but our goal here is not to review that literature. Instead, we present a short guide intended for researchers who want to know why it is important to "care for and feed" data, with some practical advice on how to do that.
Rule 1. Love your data, and help others love it too.
Data management requires documenting and publishing data for durable access and reuse, while encouraging colleagues and institutions to adopt the same practices.
- Document and publish your data now so others can reuse it and you can later find and reuse it yourself.
- Researchers should also encourage reviewers, institutions, and communities to support data documentation, access, repositories, and management expertise.
- Deposit data in a trustworthy field archive that provides persistent identifiers, documentation, metadata, and curation.
- Plan data, metadata, and process information together according to the level of reuse you realistically expect.Full reproducibility requires provenance information such as working analysis code, an execution platform, and verifiable data versions.
Rule 4. Publish workflow as context.
Publishing workflow details supplies essential context for interpreting and reusing data, connecting collection, processing, and analysis to the resulting research materials.
- Publish the processing steps that explain how data were collected, manipulated, and analyzed.These steps constitute the project’s workflow and contribute to the data’s provenance.
- Make tables, spreadsheets, images, graphs, databases, code, documentation, metadata, and related links as available as practical with the paper.
- Share data and code early when helpful, and link each with its own persistent identifier in the publication.
Rule 6. Publish your code (even the small bits).
Even small analysis scripts can clarify how results were obtained, so code should be published with version history and appropriate attribution.
- Publish analysis code regardless of how short, buggy, or insignificant it seems.
- Source-code repositories support collaboration and version tracking, making code development and provenance easier to follow.
- Publishing source code and its version history enhances transparency and reproducibility, while open source can reduce barriers for subsequent users.
- Describe how you want data and code to be acknowledged because citation-based attribution often breaks down for these materials.
Rule 8. Foster and use data repositories.
Repositories provide practical infrastructure for placing, preserving, discovering, and reusing shared data and code, while communities can strengthen research capacity by rewarding these practices.
- Choose a repository based on deposit ease, community uptake, accessibility, discoverability, curation, preservation, persistence, and format support.
- Use a field-standard repository when available, or seek guidance from data specialists, librarians, or archivists when selecting alternatives.
- Reward colleagues who share data and code by praising good practices, providing feedback, and citing the sources you use.
- Organized, accessible collections improve a community’s research capacity as shared data are used, cited, and improved.
- Scientists should help institutions and disciplines move toward open, discoverable, reproducible data and research, including through support for data specialists.
Links to Useful Resources
These repositories help researchers preserve, share, showcase, and cite research data and multidisciplinary outputs.
- Dataverse supports long-term preservation, archival practices, researcher control, sharing, and recognition for research data.
- FigShare makes research outputs available in a citable, shareable, and discoverable manner.
- Zenodo enables researchers, projects, and institutions to share and showcase multidisciplinary data and publications.
- Dryad aims to make data archiving simple and rewarding through services beyond those provided by publishers or institutional websites.
B: Directories of Research Data Repositories
These directories and catalogs help users identify, locate, and search repositories and other resources for open and scholarly data.
- DataBib helps users identify and locate online research-data repositories through searchable curated records.
- Re3data provides a global registry of research-data repositories across academic disciplines for researchers and scholarly stakeholders.
- The Open Access Directory lists repositories and databases for open data.
- The Force 11 Catalog inventories web-based scholarly resources, including publication systems, databases, organizations, software, services, standards, formats, and training tools.
C: Workflow Management Systems
These workflow systems support the design, execution, sharing, provenance tracking, exploration, visualization, and reporting of scientific analyses.
- Taverna provides tools to design and execute scientific workflows and support in silico experimentation.
- Kepler helps scientists and programmers create, execute, and share models and analyses across scientific and engineering disciplines.
- Wings assists scientists with designing computational experiments through a semantic workflow system.
- VisTrails supports data exploration and visualization while managing scientific workflows and provenance.
- Knime provides a graphical workbench spanning data access, transformation, investigation, predictive analytics, visualization, and reporting.
D: Source Code Repositories
These source-code repositories and version-control systems support hosting, managing, and collaborating on software projects of varying sizes.
- GitHub hosts software development projects that use Git revision control, including many open-source projects.
- Git is a distributed version-control system designed to manage projects from small to very large with speed and efficiency.
- Mercurial is a distributed source-control tool that handles projects of any size and offers an intuitive interface.
- BitBucket hosts projects using either Mercurial or Git revision-control systems.
E: Systems to Package, Access, and Execute Data and Code
These tools package data, code, text, mathematics, plots, and rich media into executable or repository-connected scholarly workflows.
- iPython Notebooks combine code execution, text, mathematics, plots, and rich media in one interactive document.
- ROpenSci provides R packages for accessing data repositories through the R statistical programming environment.
- Authorea supports collaborative, web-native, living, dynamic, executable articles containing text, mathematical notation, images, and data.
- Dexy automates work with documents through a command-line interface.
F: Software Tools to Run Your Own Document Repository
Researchers can run document repositories using software ranging from moderate-to-large-scale digital-library suites to simpler institutional repository applications.
- Invenio enables users to run web-based digital libraries or document repositories containing several millions of records.
- EPrints offers a fast, easy way to establish small- to medium-sized repositories for open-access literature, data, theses, reports, and multimedia.
- DSpace is a turnkey institutional repository application developed by the Duraspace organization.
G: Licensing and Privacy
Open-source licensing supports free software use, modification, and sharing, while privacy tools help researchers share personal data while protecting individual subjects.
- Open-source licenses permit software to be freely used, modified, and shared.Examples include Apache, BSD, GNU GPL, MIT, and Mozilla Public License.
- Privacy tools support collecting, analyzing, and sharing personal research data while providing privacy for individual subjects.