Filter
Reset all

Subjects

Content Types

Countries

AID systems

API

Data access

Data access restrictions

Database access

Database licenses

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 7 result(s)
Country
Research Data Online (RDO) provides access to research datasets held at the University of Western Australia. RDO is managed by the University Library. The information about each dataset has been provided by UWA research groups. Information about the datasets in this service is automatically harvested into Research Data Australia (RDA: https://researchdata.ands.org.au/). Language: The user interface language of the research data repository.
The figshare service for The Open University was launched in 2016 and allows researchers to store, share and publish research data. It helps the research data to be accessible by storing metadata alongside datasets. Additionally, every uploaded item receives a Digital Object Identifier (DOI), which allows the data to be citable and sustainable. If there are any ethical or copyright concerns about publishing a certain dataset, it is possible to publish the metadata associated with the dataset to help discoverability while sharing the data itself via a private channel through manual approval.
DR-NTU (Data) is the institutional open access research data repository for Nanyang Technological University (NTU). NTU researchers are encouraged to use DR-NTU (Data) to deposit, publish and archive their final research data in order to make their research data discoverable, accessible and reusable.
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.
Academic Commons is a freely accessible digital collection of research and scholarship produced at Columbia University or one of its affiliate institutions (Barnard College, Teachers College, Union Theological Seminary, and Jewish Theological Seminary). The mission of Academic Commons is to collect and preserve the digital outputs of research and scholarship produced at Columbia and its affiliate institutions and present them to a global audience. Academic Commons accepts articles, dissertations, research data, presentations, working papers, videos, and more.