| Documentation |
How Is Metadata Different from Data Documentation?
Metadata is a method of documentation that refers to a particular standard that has been agreed upon by a specific community or group.
Providing documentation is a crucial part of the research process; in order for data to be used effectively and efficiently, certain facts about that data must be recorded. Depending on one’s own memory is not a good solution, especially in collaborative research environments.
Describing research protocols and documenting data through the creation of metadata, lab notebooks, instrument calibrations, methodology outlines or codebooks will ensure that your data is valid and reproducible.
What to document?
To ensure maximum interoperability and reproducibility, data should be accompanied by full documentation.
| Research Project Documentation | Dataset documentation |
| Rationale and context for data collection | Variable names and descriptions |
| Data collection methods | Explanation of codes and classification schemes used |
| Structure and organization of data files | Algorithms used to transform data (may include computer code) |
| Data sources used (see citing data) | File format and software (including version) used |
| Data validation and quality assurance | |
| Transformations of data from the sanitized data through analysis | |
| Information on confidentiality, access and use conditions |
General aspects of your data that you should document
✦ Title - Name of the dataset or research project that produced it
✦ Creator - Names and addresses of the organizations or people who created the data; preferred format for personal names is surname first (e.g., Smith, Jane)
✦ Identifier - Unique number used to identify the data, even if it is just an internal project reference number
✦ Date - Key dates associated with the data, including: project start and end date; release date; time period covered by the data; and other dates associated with the data lifespan, such as maintenance cycle, update schedule; preferred format is yyyy-mm-dd, or yyyy.mm.dd-yyyy.mm.dd for a range
✦ Method - How the data were generated, listing equipment and software used (including model and version numbers), formulae, algorithms, experimental protocols, and other things one might include in a lab notebook
✦ Processing - How the data have been altered or processed (e.g., normalized)
✦ Source - Citations to data derived from other sources, including details of where the source data is held and how it was accessed
✦ Funder - Organizations or agencies who funded the research
Adapted from the Curtin University Documenting Research library guide
✦ Subject - Keywords or phrases describing the subject or content of the data
✦ Place - All applicable physical locations
✦ Language - All languages used in the dataset
✦ Variable list - All variables in the data files, where applicable
✦ Code list - Explanation of codes or abbreviations used in either the file names or the variables in the data files (e.g. "999 indicates a missing value in the data")
✦ File inventory - All files associated with the project, including extensions (e.g. "NWPalaceTR.WRL", "stone.mov")
✦ File formats - Formats of the data, e.g., FITS, SPSS, HTML, JPEG, etc.
✦ File structure - Organisation of the data file(s) and layout of the variables, where applicable
✦ Version - Unique date/time stamp and identifier for each version
✦ Checksum - A digest value computed for each file that can be used to detect changes; if a recomputed digest differs from the stored digest, the file must have changed
✦ Necessary software - Names of any special-purpose software packages required to create, view, analyze, or otherwise use the data
✦ Rights - Any known intellectual property rights, statutory rights, licenses, or restrictions on use of the data
✦ Access information - Where and how your data can be accessed by other researchers
| How to record metadata |
Metadata should be stored alongside your research data.
There are a number of ways you can add documentation to your data.
| Embedded documentation | Supporting Documentation |
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Supporting documentation
You may wish to consider creating a README file which documents the contents, naming convention and structure of files and folders and the project they relate to, as well as a description of file formats and software needed to use the files. You can also document details of licence or any restrictions placed on the data. |
This is information in separate files that accompany data in order to provide context, explanation, or instructions on confidentiality and data use or reuse. Examples of supporting documentation include:
Supporting documentation should be structured, so that it can be used to identify and locate the data via a web browser or web based catalogue. |
Forms of Documentation
| README |
A README File is a text file located in a project-related folder that describes the contents and structure of the folder and/or a dataset the project they relate to, so that a researcher can locate the information they need. A readme file provides information about a data file and is intended to help ensure that the data can be correctly interpreted, by yourself at a later date or by others when sharing or publishing data. Information should include a description of file formats and software needed to use the files. You can also document details of licence or any restrictions placed on the data. Cornell University has an excellent guide on Readme files. |
| Data dictionary/ codebook |
Also known as a codebook, a data dictionary defines and describes the elements of a dataset so that it can be understood and used at a later date. It informs the data user about the study, data file(s), variables, categories, etc., that make up a complete dataset. The following tools can be useful for creating a codebook/data dictionary
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| Protocol |
A protocol describes the procedure(s) or method(s) used in the implementation of a research project or experiment. If you need to maintain protocols, we can a tool like protocols.io - a collaborative platform and preprint server for methods and protocols. Learn more about and watch a seminar on Protocols and Methods delivered by Harvard Biomedical Data Management. |
| Lab Notebook aka ELN |
For research groups that use them, lab notebooks are often the primary record of the research process. They are used to document hypotheses, experiments, analyses, and interpretations of experiments. In an Electronic Lab Notebooks (ELN) you can enter protocols, observations, notes, and other data using your computer or mobile device. The following are some examples of Open-Source ELN:
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From Knowledge clip: Data Documentation [Video], by UGent Open Science, 2021, Ghent University. (https://www.youtube.com/watch?v=7Ogbkx74Ym8). CC BY.
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