Managing and sharing data
Good data management is an integral part of good research practice. By managing your data according to established best practice and standars you can comply with funders requirements, improving research integrity, ensuring replication, enhancing data security and enabling data reuse.
The CEACS Data Library manages an instance of a Harvard Dataverse data repository to help you sharing and preserving your datasets.
For more information contact the Data Librarian.
The data life-cycle 
The data life-cycleThe life spam of data is longer than the research project that produces them. Research data follows a lifecycle, see diagram below, that starts with conceptualization and is followed up by stages such as creation, storage and access, analysis, publication, reuse and preservation.
The following sections provide information and guidance based on how researchers at CEACS can manage their data throughout their lifecycle.
Before undertaking your data collection it is important to plan your entire data management process. This is crucial as it is at this stage that you need to start taking into account things like your funder requirements, data ownership, data storage requirements, documentation, sharing and curation.
Data management plan
A data management plan produced at the outset of a research project can help researchers defining roles and responsibilities, identifying risks and documenting the process of handling the data from the moment of collection including all systems and software. Below are some examples and guidance on data management plans:
- The Digital Curation Centre´s Data Management Plan Checklist [PDF 276KB]
- The University of Edinburgh Data Library´s Data Management Plan guidelines
Funder´s data policies
Many research funders have data policies in place that require researchers to make their data available
A fundamental part of data management involves documenting the data comprehensively so that they can then be understood and used. The documentation needs to include information about the context, track the provenance and enable the data to be found and analysed.
The UK Data Archive recommends creating three types of documentation:
- Study level providing information on the study, the data collection and context
- Data level with information about the variables
- Catalogue record including core information for resource discovery, referencing and outputs
The Data Documentation Initiative (DDI) provides a standard metadata specification for social sciences data.
Secure storage space is provided through the U: drive. This network drive is backed-up regularly and it is highly recommended that you store all your important data here. You can find it by looking at your computer
The Instituto Juan March has a virtual archive in the MIT Dataverse Network to manage, selectively share and preserve research data generated by CEACS researchers.
Some of the benefits of having your data on this repository include:
- Formal citation credit for your data, including a globally unique identifier and universal numeric fingerprint.
- Establish an unbreakable link between your data and related published work.
- Easy ways for others to find your data and associated scholarship.
- Share your data with everyone, those who sign your licensing agreement, or only individuals you approve.
- Allow users to download your data in any format and run many advanced statistical methods on-line.
The Data Librarian can help you submitting your datasets or replication data.