Tools & infrastructures
The organisation, storage and reuse of research data requires sound knowledge and suitable tools. On this page, you will find useful infrastructures, tips and training materials to help you manage your research data effectively.
Repositories
Open Data LMU
The repository Open Data LMU provides a platform for the publication of digital research data according to the principles of Open Access. The allocation of a Digital Object Identifier (DOI) ensures long-term citability. Long-term archiving of research data is also guaranteed.
Open Data LMU – Physics
Open Data LMU – Physics is a subject-specific research data repository designed to facilitate the publication of extensive datasets generated from projects within the Faculty of Physics. This service is operated as a collaborative project shared between the Faculty of Physics, the University Library of LMU Munich, and the Leibniz Supercomputing Centre (LRZ).
Discover
The Discover platform offers access to research data from projects related to LMU Munich. It provides a one-stop search entry point and enables a targeted search for relevant research data. Discover is an important resource for researchers and students, allowing them to access a wide range of data covering the broad spectrum of research at LMU Munich. In addition, the research data from the Open Data LMU repository are indexed on Discover.
Data management plans
Research Data Management Organiser (RDMO)
A data management plan (DMP) structures the handling of research data within a scientific project. To support you in creating a DMP for your research project, the University Library of LMU Munich hosts an instance of the web-based software Research Data Management Organiser (RDMO). RDMO not only simplifies the process of creating a DMP but also ensures that your data management tasks are well-organized and compliant with the requirements of the research funding organisations. LMU Munich members can register for the software free of charge.
RDMO supports you in planning projects and managing data management tasks over the entire data life cycle. The question catalogues contained in RDMO form the basic framework of the data management plan. The questions refer to various aspects that need to be considered when creating data management plans, while the questionnaires are based on the specifications of the research funding organisations.
Metadata
DataCite metadata generator
It is advisable to use a standard metadata schema to describe the data is advisable to make research data more accessible and thus increase its added value. The DataCite Metadata Schema published by the consortium of the same name has now established itself as a globally adopted model.
For researchers who are new to metadata and want to streamline the process of cataloguing their research data, the metadata generator is a helpful online tool. It simplifies the structured recording of metadata in the DataCite schema.
The tool follows the structure of the DataCite metadata schema, currently in version 4.4. For each input field, there is a link to the corresponding section in the DataCite Best Practice Guide – a guide to support researchers in filling in the format. The guide explains to users step by step how they can achieve good visibility of their data and promote reuse by standardising the way they fill in the individual metadata fields as far as possible.
As researchers enter information, the generator creates a DataCite XML file. This file can be downloaded and shared with the chosen repository along with the research data. The generator is also available for reuse via GitHub.
DataCite best practice guide
It is advisable to use a standard metadata schema to describe the data, in order to make research data more accessible and thus increase their added value. The DataCite Metadata Schema, published by the consortium of the same name, has proven to be an established model worldwide.
However, consensus on using a standardised metadata schema for recording metadata information alone is not enough to ensure interoperability. Clear rules for recording metadata are needed to avoid variability and to enable automated information processing by research data infrastructures.
A best practice guide for DataCite was developed as one of the tasks of a working group involving stakeholders from the fields of data generation, data curation and data aggregation. The guide aims to increase the interoperability of (meta-)data through greater standardisation of input.
Electronic lab books
Chemotion
The electronic laboratory notebook “Chemotion ELN” provides LMU members with software allowing them to capture, manage, store, process, and share research data. The application is specially tailored to the needs of the fields of chemistry and pharmacy and offers a range of functions, including:
- Structure editors for chemical structures
- Recording of measurement data from molecular chemistry
- Metadata for data enrichment
- Processing and visualization of measurement data