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Open Data: Home

Open data is data that can be freely used and redistributed by anyone. Open data is subject at most to the requirement of attribution.

What is Open Data?

Open data are freely and publicly available data structured in a way to be fully accessible and usable, without copyright, patents or control. This is important because data that is open, available, and accessible will help spur innovation and inform how agencies should evolve their programs to better meet the public's needs.The Open, Public, Electronic, and Necessary Government Data Act or the OPEN Government Data Act requires federal agencies to publish their information as open data using standardized, non-proprietary formats. In order to meet the guidelines set forth in the US Government Open Data Initiative and data management requirements set forth by DOE, you may be asked to deposit your data in an open access data repository.

Data Repositories help you: 

  • manage your data
  • organize and deposit your data
  • cite your data by supplying a persistent identifier
  • facilitate discovery of your data
  • make your data more valuable for current and future research
  • preserve your data for the long-run

Data Dose

Data Dose is a monthly LANL newsletter reminder about data-related events and information to help you manage and love your data, supporting the LANL Information, Science, and Technology Science Pillar. To subscribe to the mailing list, email listmanager@lanl.gov with “subscribe datadose_newsletter” in the body of your message.

Data Dose

FAIR

Where possible, we seek to adhere to the FAIR principles for scientific data management and stewardship. These principles promote the ideas of managing data so that it is fully contextualized, allowing the raw data to be easily used and reused. The FAIR principles are:

  • Findability: The data and associated metadata is persistent, richly described and can be queried through machine access (Application Programmable Interfaces or API) or through manual interaction.
  • Accessibility: The data can be accessed easily and does not use proprietary access methods, even when allowing for authentication and authorization.
  • Interoperability: Data is not stored in proprietary formats, and the data formats are widely accepted and easily read by a variety of programming interfaces. Common community vocabularies will be used to help describe any data and metadata.
  • Reusability: The metadata shall be clear and concise, following community standards and establishing provenance, that allows for interpretation and utilization in other methods beyond the original conceived scope.

Making Data Open via a General Data Repository

LANL researchers' deposit their data in a variety of places: domain-specific data repositories, general purpose data repositories, and DOE-specific Institutional data repositories. The value of these repositories are in the availability and familiarity with researchers within the discipline and the subject specific metadata searching capabilities and ontologies.General repositories are often recommended by publishers as well as granting agencies for the deposit of data related to research studies. Some recommended open repositories include:

Compare recommended repositories to each other in the LANL Data Repositories Matrix.

Making Data Open via a Discipline-Specific Data Repository

There are many ways to share your data openly and freely in open data repositories, which can be found via re3data, a registry of research data repositories). You can search the re3data.org to find appropriate academic discipline repositories.

Below are a selection of DOE and NNSA data repositories:

  • The Open Energy Data Initiative (OEDI) is a free, searchable online software discovery platform and knowledge-sharing platform, developed by NREL, and powered by OpenEI. Sponsored by the Department of Energy, and developed by the National Renewable Energy Lab, in support of the Open Government Initiative, OpenEI strives to make energy-related data and information searchable, accessible, and useful to both people and machines  https://openei.org
  • Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center: https://daac.ornl.gov/
  • Sandia National Laboratory: https://energy.sandia.gov/programs/energy-water/data-modeling-analysis/
  • OSTI DOE CODE Repository Service https://www.osti.gov/doecode/ for links to various DOE code/software/data resources
  • National Energy Technology Laboratory https://edx.netl.doe.gov/
  • Energy Information Administration: The Energy Information Administration (EIA) is the statistical agency of the U.S. Department of Energy. Find data under Sources and Uses, next by type. More open data for download is available at https://www.eia.gov/opendata/
  • Data.gov  Public access to high value, machine readable datasets generated by the Executive Branch of the Federal Government.
  • Nuclear Energy Agency Contains both experimental and evaluated nuclear data including nuclear reaction ((the properties of interacting nuclei, e.g. cross sections) and nuclear structure (the properties of single nuclei) data
  • U.S. Energy Information of Administrations Data Company level data on the supply and disposition of natural gas in the United States, Electric power data collected by surveys, international energy statistics, energy country profiles for 217 countries, state and territory energy profiles for the U.S., financial data collected from major energy producers, short-term and historical energy outlook data & projections, and real energy prices.

Making Data Open via a Data Journal

Data journals bring data publishing into the mainstream of scholarly communication, since data reports are authored, published, indexed, cited and used in much the same way as conventional journal articles. The purpose of a data journal is to provide quick access to high-quality datasets that are of broad interest to the scientific community. They are intended to facilitate reuse of the dataset, which increases its original value and impact, and speeds the pace of research by avoiding unintentional duplication of effort.

Data papers within these journals are typically peer-reviewed in the same manner as articles, providing authorship credit for journal articles, and can accumulate citations just like traditional journal articles, While the field of data papers continues to evolve, the datasets presented in data papers include much more description than datasets deposited to a repository, even if those datasets were deposited to support a manuscript. There is a growing list of data journals, but the most known are:

Citations:

Walters, W. H. (2020). Data journals: incentivizing data access and documentation within the scholarly communication system. Insights, 33(1), 18. DOI: http://doi.org/10.1629/uksg.510

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