Hogeschool van Amsterdam

Data package

Preservation of research data serves two purposes. It facilitates verification and replication of your research (results), and it facilitates the reuse of research data. The FAIR principles (Findable, Accessible, Interoperable, Reusable) provide a useful framework for enabling maximum (re)use of data within ethical and legal (privacy) boundaries.

What is preserving data in a data package?

The ultimate goal of FAIR is to optimize the reuse of data. To achieve this, metadata and data should be well-described and documented so that they can be replicated, understood and/or combined in different settings. Think of variable labels, codebooks, protocols and instruments used, attaching a license, etc. By packaging data, metadata and documentation in a data package and depositing this package in a data repository like UvA/HvA figshare you already fulfill most of the preservation and FAIR requirements, although making (meta)data interoperable and machine-readable asks for additional FAIR-expertise (at the start of your project). Preservation applies to both quantitative and qualitative research data, and refers to (static) research data.

Why is it important?

Benefits for you

  • Just like publications, data you collected is research output too. By depositing it in a data repository you increase your research output (via UvA/HvA figshare the dataset will be automatically registered in PURE) and visibility of your work
  • You could get more credit for your work because besides citing your article others could also cite the dataset
  • More and more journals and funders ask how you will make the research data findable, accessible, interoperable and reusable
  • By describing your data (via metadata) and providing conditions for reuse you clarify what others can and cannot do with the data
  • The data you collected are protected against corruption and loss
  • You facilitate verification of your research results and reuse of your data
  • You prevent ad hoc hassle when you fall ill or accept another job, enabling colleagues to efficiently take over
  • Probably, in the informed consent you promised participants to take care of their (sensitive) data in a certain (careful and responsible) way
  • Enabling new (more data-driven) research questions to be answered. In the field of AI the issue is not computing power or algorithms but the availability, accessibility (under the right conditions) and interoperability of data

How and when?

Every scientific publication and PhD-dissertation is accompanied by a data package

Other situations where preservation could be relevant:

  1. Upon completion of a research project, regardless of whether or not the data were used in a publication (especially when the data may be useful for a new project)
  2. Upon completion of raw data collection to ensure secure storage and prevent loss or modification of the raw data (i.e. a locked or frozen version of your raw data)
  3. Storing copies of datasets from long-lasting cohort studies or registries that were used in a publication

These steps apply to research projects for which AUAS bears formal responsibility

  1. Falling under Dutch law (‘WMO-plichtige’) studies, the sponsor (‘verrichter’) of the study bears responsibility unless otherwise agreed (in writing)
  2. Concerning PhD-projects: this depends on the arrangements made between AUAS and the university/institution where the doctorate is obtained
The data package will be deposited in UvA/HvA figshare before publication of a scientific article so the data package can be cited in the article via the persistent identifier (DOI) of the data package
Published by  Urban Vitality 2 September 2020