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

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 6 result(s)
<<<!!!<<< The repository is no longer available - Data previously on the site are now available at ftp://ftp.ncbi.nlm.nih.gov/pub/mhc/mhc/Final Archive. >>>!!!>>> The dbMHC database provides an open, publicly accessible platform for DNA and clinical data related to the human Major Histocompatibility Complex (MHC). The dbMHC provides access to human leukocyte antigen (HLA) sequences, HLA allele and haplotype frequencies, and clinical datasets.
Country
The Austrian NeuroCloud (ANC) is a FAIR-enabling platform for sustainable research data management in Cognitive Neuroscience. Most of the offered research data is restricted, the publicly available datasets can be seen under https://data.anc.plus.ac.at/explore The ANC offers tools and services to archive, manage, and share neurocognitive data flexibly and according to community standards. Scientists have full control over what they share (e.g., full original datasets or data derivatives), how they share it (by choosing from a selection of licensing models), and with whom (e.g., by using the ANC’s adjustable User Agreement templates). The ANC provides persistent DOIs for data releases and operates in accordance with European GDPR. Moreover, the ANC fully supports the mission of the EOSC and is committed to the EU’s open science policy, legal standards, and best open science practices. Accordingly, the ANC aspires to facilitate FAIR data operations along the entire data lifecycle, actively supporting the ongoing shift in research culture towards increased transparency, data reusability, and result reproducibility.
Country
Phaidra Universität Wien, is the innovative whole-university digital asset management system with long-term archiving functions, offers the possibility to archive valuable data university-wide with permanent security and systematic input, offering multilingual access using metadata (data about data), thus providing worldwide availability around the clock. As a constant data pool for administration, research and teaching, resources can be used flexibly, where continual citability allows the exact location and retrieval of prepared digital objects.
The Innsbruck Dissociative Electron Attachment (DEA) DataBase node holds relative cross sections for dissociative electron attachment processes of the form: AB + e– –> A– + B, where AB is a molecule. It hence supports querying by various identifiers for molecules and atoms, such as chemical names, stoichiometric formulae, InChI (-keys) and CAS registry numbers. These identifiers are searched both in products and reactants of the processes. It then returns XSAMS files describing the processes found including numeric values for the relative cross sections of the processes. Alternatively, cross sections can be exported as plain ASCII files.
Country
DOOR is the open institutional repository of the University for Continuing Education Krems, formerly known as Danube University Krems. DOOR runs on the fedora Software and is a partner repository of Phaidra Vienna and other fedora users in Austria. We provide access to OA publications, scientific data and much more for interested users and support our scientists and co-workers in their publication processis.
Country
PalDat provides a large amount of data from a variety of plant families. Each data entry ideally includes a detailed description of the pollen grain, images of each pollen grain (LM, SEM and TEM), images of the plant/inflorescence/flower and relevant literature.