The research data lifecycle

The Research Data Lifecycle is tool for planning and organizing Research Data Management (RDM) tasks, and follows closely the research lifecycle. Different variants exist, with different numbers of steps and ways of describing the research process from a data perspective. The variant we use in this guide has seven steps.

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Planning RDM can start very early, or even before a research activity. A great tool for planning, organizing and documenting RDM needs and actions is a Data Management Plan (DMP).&nbsp;
Planning actions:
<ul>
<li>Create a Data Management Plan on dsw.chalmers.se</li>
<li>Think about what data will be collected, generated or reused.</li>
<li>How will this data be stored and shared. Are there any legal or other restrictions to how you use this data?</li>
</ul>
<h3 id="docs-internal-guid-9fda523b-7fff-40b4-ea0f-6910d31b7817" style="line-height: 1.295; margin-top: 8pt; margin-bottom: 4pt;" dir="ltr">What are Data Management Plans?</h3>
DMPs document how data is handled during a research project and what happens to them afterwards. They cover topics such as organizing, structuring, storing and sharing research data throughout the whole research process. DMPs complement provenance information (such as notebooks and methodology descriptions) by documenting decisions around data – for instance the selection of storage, questions about personal and sensitive information, issues around sharing data, etc.&nbsp;
DMPs are living documents – they need to be updated throughout a research activity. It follows that you are not expected to have all the answers available at the beginning of a research activity.
Chalmers University of Technology requires the creation of a DMP for all research activities (C-2023-0615-3, “Guiding principles for good research practice”) as do certain funding bodies. The Swedish Research Council, Formas, and the Horizon Europe framework require the creation of a DMP.&nbsp; They may also have some requirements or recommendations on the contents of the DMP.
General contents of a DMP:
<ul>
<li>What data will be reused or created? How, and in which formats and volumes?</li>
<li>How will data be documented? Which metadata will be created?</li>
<li>How will data be stored?</li>
<li>What legal and ethical considerations should you take for your data?</li>
<li>How will your data be stored and made accessible (if possible) after it is created?</li>
<li>Whose responsibility is it to manage this data? How much will it cost?</li>
</ul>
&nbsp;

The process of gathering data for research depends on field and research topic. In general, however, research data can be collected, generated, or reused.&nbsp;
By data&nbsp;collection we mean obtaining data in any way: measurement data from the field, experimental data, generated data, reused data, and so on.
In the collection phase it is critical to document research data, its provenance, and to organize your data so as to be able to access it and understand it later on.&nbsp;
<h3 style="line-height: 1.295; margin-top: 8pt; margin-bottom: 4pt;" dir="ltr">RDM tools for data collection</h3>
Some tools are necessary for managing and documenting data during collection. These vary between fields – Field journals, Electronic Lab Notebooks, text files, provenance journals, Electronic Data Capture Systems, and Laboratory Information Management Systems are a few.
Digital tools can be used to make documentation easier to access and understand. Electronic Lab Notebooks (ELN), collaborative research platforms, secure data storage and sharing services , Electronic Data Capture (EDC) systems&nbsp; and Laboratory&nbsp; Information Management Systems (LIMS) are some examples of more advanced tools which can be used.&nbsp; While the tools may vary, the following basic information is important to collect/document:
<ul>
<li>Data provenance</li>
<li>Data quality assurance measures</li>
<li>Data acquisition methodology/design</li>
<li>If working with personal or sensitive personal information, lawful basis for handling the information.</li>
<li>Potential data security, sharing and ownership issues.</li>
<li>Data storage solution - how much data will be produced, where will it be stored during different periods of its lifetime?</li>
<li>Identify a suitable&nbsp;data repository </li>
</ul>
When reusing&nbsp;previously created data (your own or from other sources) it is important to check that&nbsp;good quality documentation is available, such as:
<ul>
<li>Data collection method</li>
<li>Documentation of file/database structure and content</li>
<li>Purpose and context of data collection</li>
</ul>

The processing phase is a preparatory phase before&nbsp;analysis, where data is formatted, converted, cleaned, coded, combined or otherwise prepared for analysis.
The processing step is especially important since it&nbsp;sets the formats used in analysis and possibly even&nbsp;in the preservation and sharing phase.s.&nbsp; What&nbsp;to keep in mind when processing research data
<ul>
<li>Use formats which make it easier for data to be reused later on.&nbsp;</li>
<li>Structure your data in a way that makes it easier to navigate</li>
<li>Make sure you <a href="https://intranet.chalmers.se/en/working-at-chalmers/security-fire-safety/information-security/processing-personal-data-gdpr/">take necessary steps to&nbsp;protect personal and sensitive personal information.</a> Do no keep personal information unnecessarily. Anonymisation, pseudonymisation, safe storage and encryption can be useful tools here. When in doubt, get in touch with <a href="https://intranet.chalmers.se/en/search/?query=11408&amp;category=person" data-anchor="?query=11408&amp;category=person">Chalmers' Data Protection Officer.</a></li>
<li>Document provenance: all steps during processing, as well as any encoding, versions, significance of special values, etc.</li>
</ul>
Selecting data formats:
Choice of data format, or file format usually depends on&nbsp;software and formats commonly used by you, your colleagues or in your field, by your needs. Some other things can be taking into account when&nbsp;choosing between formats:
<ul>
<li>openly-documented formats which are independent of particular software have better chance of being usable in the future.</li>
<li>converting from one format to another (e.g., when sharing data) may lead to&nbsp;loss of information.</li>
</ul>
More information on file formats can be found on <a href="https://researchdata.se/en/manage-data/organize-data/file-formats">Researchdata.se</a>
Structuring research data:
<ul>
<li>Document decisions on file naming,&nbsp;organisation (folder structure, database structure, etc.) and versioning early on. Keep this document and provenance documentation up to date.&nbsp;</li>
<li>Make sure file names&nbsp; are unique, ideally within the entire research activity.&nbsp;</li>
<li>Keep file names short.</li>
<li>Link file names to their contents.</li>
<li>Keep short but descriptive names for folders.</li>
<li>Include a version number in file names (see below).</li>
<li>Create a documented structure of folders to reflect the project's needs.</li>
</ul>
Versioning research data:
A simple way of versioning files is to add a sequential version number to the file names (_v0, _v1, _v2, etc.) Additional information, such as 'original' or 'cleaned' can also be added.&nbsp;
You can also make use of version-control tools such as Git if applicable.
Most importantly: have a plan for naming, organising and versioning data and stick to it!

The analysis phase is&nbsp;pivotal to the research process, since it is here that new knowledge and insights are generated.&nbsp;In order for research to&nbsp;be transparent it is important to make sure that the analysis phase is reproducible.
The collection and processing stages will have set the stage for how new data, be it intermediate data or a completely new dataset is organized at this stage.&nbsp;A lot of the advice set down in the previous section&nbsp;is also applicable here.
Things to consider at this stage:
<ul>
<li>Data storage and location become very relevant at this point, and also rely on previous decisions. Depending on data size and computational needs you may need to keep research data (or at least a copy) on a computing environment to run analyses. Different environments lend themselves to different situations:
<ul>
<li>a laptop can work well for light computing without the need for collaboration.</li>
<li>heavier machines may be for more computationally demanding tasks.</li>
<li>&nbsp;computing clusters or cloud services may be more useful for demanding tasks, or where collaboration is important. Questions on what data can be processed on these platforms can become relevant, as well as who has access to them.&nbsp;</li>
<li>Virtual research environments for collaborative efforts and tools.</li>
</ul>
</li>
<li>Selecting computing environments and tool is also important, especially&nbsp;if collaboration is required.&nbsp;</li>
<li>A very important aspect, already presented at the processing stage, is&nbsp;keeping in mind reproducibility. For result to be reproducible it is also important to be able to reproduce operations on data during the analysis phase.&nbsp;The key components are transparency and documentation:
<ul>
<li>Document the steps taken during analysis - steps taken or workflows used.</li>
<li>Share any code that was created.</li>
<li>Document&nbsp;the computing environment used for analysis.&nbsp;</li>
</ul>
</li>
</ul>
&nbsp;

Research data is a valuable resources, and&nbsp;part of the research data management process is securing its future survivability and reusability.
Preserving Research Data&nbsp;
Preserving research data is about making sure it survives on the long term, and that it can be accessed and read in the future. The future value of research data is difficult to foresee, as is the need of future verification.&nbsp; Preserving your data means securely storing well-documented data in a suitable format.
Publishing&nbsp;Research Data
Data needs to be found&nbsp;in order to be reused, and this is where publishing your data comes into play. Publishing your data doesn't always mean making it openly available, but it does mean that it is publicly described, allowing others to find information about the data and about how to access and reuse it.
Research data is usually published in a research data catalogue. The process of publishing is different to publishing in a journal. Publishing data includes describing the data in a research data repository, selecting accessibility level (openly available or restricted access), selecting a licence, and receiving a Digital Object Identifier (DOI) with which to cite the data.&nbsp;This makes your research data findable in online data catalogues, as well as allowing others to cite it. 
Finding a research data repository
The advantages of choosing a research data repository over publishing data as supplementary material in a journal are many. By choosing a repository, data&nbsp;become citable as a separate entity from an article. Repositories are usually free at the point of use and keep data metadata freely available without claiming any rights over the data. A trustworthy repository will also have a clear preservation plan, meaning that your data will be FAIR for a long time.&nbsp;
<ul>
<li>Swedish National Data Service (SND), together with the Chalmers Data Office, offer Chalmers researchers support and storage. When you deposit a dataset through SND, it is curated and published by Chalmers Data Office.</li>
<li>The online repository <a rel="noopener" href="http://zenodo.org/" target="_blank">Zenodo</a> (European Commission’s OpenAIREplus project),&nbsp;welcomes all researchers to store their research data there, regardless of size and format.</li>
<li><a rel="noopener" href="https://www.re3data.org" target="_blank">re3data.org</a>&nbsp;is a global registry of research data repositories that covers repositories from different academic disciplines.</li>
</ul>
When you deposit and publish your dataset in a trusted repository, like the&nbsp; Swedish National Data service’s repository or in Zenodo, it is allocated a persistent identifier (PID). A PID makes the dataset findable even if the data is moved to a new web address. There are many types of PIDs, but DOI is the most widely used.
 Citing research data
The practice of publishing and citing datasets creates a formalised system of recognition and reward for data producers. When you publish or cite data, you allow for it to be located and accessed for replication and verification of your results. You thereby improve the overall transparency of your study.
Data is cited in the same way as other information sources and a citation should include author, title, year of publication, version, data archive and DOI.
You can find information&nbsp;<a href="/en/infrastructure/ecommons/storage-and-data-management/publish-and-cite-research-data/" title="Publish and cite research data">about support&nbsp;for publishing research data here.</a>

Research data reuse implies the use of research data for other purposes or in other research activities than originally intended. Although data reuse occurs early in the research lifecycle, it can also be seen as taking place during the last stages of the research data lifecycle, when research data has been documented, stored and shared for reuse. This section can be seen as the last and first step of a data lifecycle. To be reused effectively, research data need to have good documentation, an accessible format, and clear terms and conditions.
Why not just create new data?
There are clear benefits for reusing research data when this fits research goals. Reusing data:
<ul>
<li>Saves&nbsp;time and money</li>
<li>Can&nbsp;provide a reference dataset for your data</li>
<li>Can be used to aggregate results obtained from different methods or samples</li>
<li>Can lead to new perspectives and insights</li>
<li>Can give more robust findings using verified previously reported findings.</li>
</ul>
What&nbsp;to keep in mind when considering data for reuse?
<ul>
<li>You can search for reusable data in&nbsp;aggregated databases (e.g., Researchdata.se, Google Dataset Search), general repositories (e.g., Zenodo, Figshare, Open Science Framework), or field-specific repositories (e.g., Bolin Centre Database for climate data, SciLifeLab Data Repository for Life Science data).&nbsp; Information on research data availability can also be found in scientific articles. Re3Data is&nbsp;a good source of information on existing data repositories.</li>
<li>Is the source of the data (author and repository) trustworthy?</li>
<li>Is there enough documentation for reuse?</li>
<li>Is data quality good enough for reuse?&nbsp;</li>
<li>Which conditions is data being shared with? Are there conditions for access and reuse?</li>
<li>Are there different versions of the data? Which version&nbsp;will you be using?</li>
<li>Are there any ethical or legal restrictions&nbsp;for reuse?</li>
</ul>

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The research data lifecycle

The Research Data Lifecycle

Plan

Collect

Process

Analyse

Reuse

The research data lifecycle

The Research Data Lifecycle

Plan

Collect

Process

Analyse

Preserve and Share

Reuse