Filter
Reset all

Subjects

Content Types

Countries

AID systems

API

Certificates

Data access

Data access restrictions

Database access

Data licenses

Data upload

Data upload restrictions

Enhanced publication

Institution responsibility type

Institution type

Keywords

Metadata standards

PID systems

Provider types

Quality management

Repository languages

Software

Syndications

Repository types

Versioning

  • * at the end of a keyword allows wildcard searches
  • " quotes can be used for searching phrases
  • + represents an AND search (default)
  • | represents an OR search
  • - represents a NOT operation
  • ( and ) implies priority
  • ~N after a word specifies the desired edit distance (fuzziness)
  • ~N after a phrase specifies the desired slop amount
  • 1 (current)
Found 11 result(s)
Sharing and preserving data are central to protecting the integrity of science. DataHub, a Research Computing endeavor, provides tools and services to meet scientific data challenges at Pacific Northwest National Laboratory (PNNL). DataHub helps researchers address the full data life cycle for their institutional projects and provides a path to creating findable, accessible, interoperable, and reusable (FAIR) data products. Although open science data is a crucial focus of DataHub’s core services, we are interested in working with evidence-based data throughout the PNNL research community.
Open Power System Data is a free-of-charge data platform dedicated to electricity system researchers. We collect, check, process, document, and publish data that are publicly available but currently inconvenient to use. The project is a service provider to the modeling community: a supplier of a public good. Learn more about its background or just go ahead and explore the data platform.
Content type(s)
A machine learning data repository with interactive visual analytic techniques. This project is the first to combine the notion of a data repository with real-time visual analytics for interactive data mining and exploratory analysis on the web. State-of-the-art statistical techniques are combined with real-time data visualization giving the ability for researchers to seamlessly find, explore, understand, and discover key insights in a large number of public donated data sets. This large comprehensive collection of data is useful for making significant research findings as well as benchmark data sets for a wide variety of applications and domains and includes relational, attributed, heterogeneous, streaming, spatial, and time series data as well as non-relational machine learning data. All data sets are easily downloaded into a standard consistent format. We also have built a multi-level interactive visual analytics engine that allows users to visualize and interactively explore the data in a free-flowing manner.
NKN is now Research Computing and Data Services (RCDS)! We provide data management support for UI researchers and their regional, national, and international collaborators. This support keeps researchers at the cutting-edge of science and increases our institution's competitiveness for external research grants. Quality data and metadata developed in research projects and curated by RCDS (formerly NKN) is a valuable, long-term asset upon which to develop and build new research and science.
Country
The research data centre at the Federal Motor Transport Authority provides anonymised microdata on driver, vehicles, and road freight transport free of charge for non-commercial and independent scientific research.
The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. It is used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times.
Constellation is a digital object identifier (DOI) based science network for supercomputing data. Constellation makes it possible for OLCF researchers to obtain DOIs for large data collections by tying them together with the associated resources and processes that went into the production of the data (e.g., jobs, collaborators, projects), using a scalable database. It also allows the annotation of the scientific conduct with rich metadata, and enables the cataloging and publishing of the artifacts for open access, aiding in scalable data discovery. OLCF users can use the DOI service to publish datasets even before the publication of the paper, and retain key data even after project expiration. From a center standpoint, DOIs enable the stewardship of data, and better management of the scratch and archival storage.