You should think about how you are going to organise your files and folders from the outset. Files can very quickly become disorganised and unmanageable if file names and folder structures are not organised in a consistent and logical way. Well organised files and folders make it easier to locate and retrieve your data and save time and frustration.
Naming files in a consistent and logical way will help you to distinguish between similar files and make finding your data easier. To ensure consistency and avoid confusion you should choose a system of naming conventions at the outset of your project and stick with it.
Your files will probably go through various drafts and versions. If you are engaged in collaborative research your files may be revised by more than one person. How will you keep track of who made which changes or identify which is the current or final version? Version control allows you to manage and record the changes your documents go through as they are redrafted and amended.
Data documentation provides information about how and why data files were created, their content and structure, and what processes and transformations the data have undergone during the lifetime of the project. Data documentation also provides information that enables the data to be accessed and interpreted by future users.
A crucial part of making data user friendly, shareable and with long lasting usability is to ensure they can be understood and interpreted by any user. This requires clear data description, annotation, contextual information and documentation.– Document Your Data, UK Data Service
Metadata is sometimes defined as "data about data" or "information about data". Although the terms documentation and metadata are sometimes used interchangeably, metadata is also used in the more restricted sense of structured information that is both human and machine readable.
Metadata is structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource.– Understanding Metadata, NISO, 2004
Providing adequate documentation and metadata to your data is essential. Documentation and metadata add context to your data and provide information necessary for its discovery and reuse. Adding metadata makes it easier for you to find and understand your own data as well as enabling your data to be accessed and shared - where appropriate - with others.
Without adequate documentation and metadata your data are potentially meaningless. Ideally, you should begin documenting your data as it is created or collected rather than leaving it until the end of the project.
This will vary according to the type of data being described and the level of description. For most datasets you will usually be required to provide at least some basic descriptive information. Ideally, you should provide enough contextual information to allow others to discover, understand, access and reuse your data.
... the metadata must be sufficient to allow others to understand what research data exists, why, when and how it was generated, and how to access it.– EPSRC, Clarifications on Research Data Management
Source: Archaeology Data Service: Guide to Good Practice
Source: Van Van den Eynden, et al (2012) Managing and Sharing Data, UK Data Archive, p9.
If you deposit your data in a repository you will almost certainly be expected to provide a minimal amount of project level metadata and some repositories might ask you to add file level descriptions as well.
For example, researchers who wish to deposit their data with the King's RDM System are asked to fill in a Data Deposit Request form. Information collected in the form will be used to create a public metadata record that will make it easier for others to discover and make sense of the date.
Documentation and metadata can be added to data in a variety of ways:
If you are depositing your data in a domain or disciplinary specific repository you might also be asked to provide information about your data that is specific to your domain or discipline. It is a good idea to make sure that you are familiar with any metadata standards that are widely used within your area of research.
See the Digital Curation Centre's web site for a more comprehensive list of disciplinary metadata standards as well as information about disciplinary metadata tools.
Examples of published metadata records for datasets and data files can be found by browsing the catalogues of data repositories or data centres (e.g. UK Data Archive, Dryad, Archaeology Data Centre). Re3data.org is a registry of research data repositories.
Quality assurance and quality control are the measures which researchers can adopt to prevent errors from entering or remaining in a dataset. Ensuring the quality and integrity of research data is an integral part of good research data management across the whole research life cycle, from collecting data to preparing data for analysis and publication. In addition, many funders expect researchers to include details of the measures they will adopt to safeguard data quality and integrity in their data management plan.
Here are some examples of best practices for assuring data quality and integrity adapted from guidance provided by the UK Data Archive.
You can use the following procedures to make sure that the data recorded reflect the actual facts, events, responses and observations:
You can use the following methods to ensure accurate, standardised and consistent data transcription, digitisation or entry in a database or spreadsheet:
Guidance on interview transcription methods and quality control can be found on the UK Data Archive website.
Checking your data is a vital stage of ensuring quality usually performed after data are edited, cleaned, verified, cross-checked and validated. The following procedures can apply at this stage:
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