Filter
Reset all

Subjects

Content Types

Countries

AID systems

API

Certificates

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 3 result(s)
eLaborate is an online work environment in which scholars can upload scans, transcribe and annotate text, and publish the results as on online text edition which is freely available to all users. Short information about and a link to already published editions is presented on the page Editions under Published. Information about editions currently being prepared is posted on the page Ongoing projects. The eLaborate work environment for the creation and publication of online digital editions is developed by the Huygens Institute for the History of the Netherlands of the Royal Netherlands Academy of Arts and Sciences. Although the institute considers itself primarily a research facility and does not maintain a public collection profile, Huygens ING actively maintains almost 200 digitally available resource collections.
OpenML is an open ecosystem for machine learning. By organizing all resources and results online, research becomes more efficient, useful and fun. OpenML is a platform to share detailed experimental results with the community at large and organize them for future reuse. Moreover, it will be directly integrated in today’s most popular data mining tools (for now: R, KNIME, RapidMiner and WEKA). Such an easy and free exchange of experiments has tremendous potential to speed up machine learning research, to engender larger, more detailed studies and to offer accurate advice to practitioners. Finally, it will also be a valuable resource for education in machine learning and data mining.