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Your tasks: Documentation and metadata

How can you document data during the project?


Data documentation could be defined as the clear description of everything that a new “data user” or “your future-self” would need to know in order to find, understand, reproduce and reuse your data, independently. Data documentation should clearly describe how you generated or used the data, why, and where to find the related files. It could be used also as onboarding documentation for new colleagues, even if the responsible researcher leaves the project.

Due to the large variety of experiments, techniques and collaborative studies that usually occur within the same project, it is challenging to keep good documentation. However, lack of good data documentation often leads to data loss, not reproducible results and therefore, waste of money and time for scientists. Here we provide best practices and guidelines to help you properly document your data.


  • Write the documentation in such a way that someone else who is known to the field cannot misinterpret any of the data.

  • It is best practice to use one appropriate tool or an integration of multiple tools (also called tool assembly or ecosystem) for data documentation during a project. Suitable tools for data documentation are Electronic Lab Notebooks (ELNs), Electronic Data Capture (EDC) systems, Laboratory Information Management Systems (LIMS). Moreover, online platforms for collaborative research and file sharing services (such as OSF) could also be used as ELN or data management systems. Check with your institute or other relevant infrastructures to know what is offered.

  • Independently of the tools you will use, data documentation is needed at two levels: documentation about the entire study or project and documentation about individual records, observations or data points.
    • Study-level documentation describes the project title and summary, study aims, authors, institutions involved, funds, methods, licence and identifier for each dataset, folders structure, file naming conventions, versioning system, relation between files or tables and other general information.
    • Data-level documentation provides information about individual records or data point, such as the meaning of each variable name, label, ID or type (numeric, string, regular expression, date, etc.), units (i.e., cm, kg…), experimental factors, categories, controlled vocabulary or ontology terms accepted as values for each variable, missing values code and so on. An example could be a data file that contains a “sex” field: someone known to the field could try to misinterpret that from “external sex organs present at birth” to “chromosomal XX or XY” or “high or low testosterone level” or “social gender” or other. In order to avoid this, the way the assignment is made must be part of the documentation or of the data itself (controlled vocabulary).
  • Both the study- and data-level documentation must be generated as early as possible in the research process and also maintained, in order to be accurate and complete

  • Documentation is also required when publishing your data. General-purpose repositories usually require only study-level documentation, while discipline-specific repositories generally require both study-level and data-level documentation. Importantly, repositories often accept data and documentation in a very strict format: they can require a predefined set of attributes or fields (metadata checklists) to be filled, ontology terms to be used, specific (meta)data schemas (e.g., ISA model, MAGE-TAB) to be adopted. We recommend familiarising yourself with the requirements of the repositories that could be appropriate for publishing your data already at the beginning of the project, so that you can start documenting and formatting your data accordingly as early as possible.

  • Make sure the documentation is kept close to the data, so that nobody will be exposed to the data without being able to find the documentation.


  • There are many appropriate tools for data documentation during the project. Check with your institute to know what is offered.
    • Electronic Lab Notebooks (ELNs) are usually better for more disparate and unstructured information that requires flexibility. Researchers can use ELN in a personalized way and adapt it to document their every-day work.

    • Laboratory Information Management Systems (LIMS) typically follow pre-defined and highly structured experimental workflow. LIMS are used to document and track biological samples through the experimental processes and can support direct import of data from sources such as instruments.

    • Electronic Data Capture (EDC) systems are usually designated for collection of clinical trial data.

    • Online platforms for collaborative research and file sharing services, which integrate with several data management tools, could also be used for data documentation during the project. For instance, has integrations with Mendeley, Dropbox, GitHub, Figshare, etc.

    • There is a major area of overlap between the aforementioned tools for data documentation, so it is better to choose the tool(s) that best address your specific need. Some tools can be used at the same time to address different needs and they can be complementary. Comparative lists can help with the choice:

  • Independently of the tools, you should agree on and establish a data organisation system for files (or tables in a database) together with your team or Data Management Working Group:
    • Folder structure
    • File naming convention
    • Versioning system
  • The established data organization system has to be described in detail in the documentation, preferably in open and machine-readable formats (i.e., XML, JSON, CSV, RDF, HTML). The description of the data organization system has to be placed in the folder at the highest level (e.g. “Project” folder).

  • Study-level and data-level documentation can be provided as

    Each of these files can be made in several formats depending on the features available in your data documentation tool, your needs or skills. Machine-readable or -actionable formats (such as .xml, .json, .csv, .rdf) are preferred to non-machine-readable ones (.txt, .xls, .pdf). Also non-proprietary formats are preferred over proprietary ones.

  • Highly structured data documentation is called metadata. Generating metadata in a machine-readable or -actionable format makes your data more FAIR . Metadata provides structured and searchable information so that a user can find existing data, evaluate its reusability and cite it.

  • It is good practice to use international standard metadata schemas to organise and store your (meta)data in a structured way. A metadata schema describes the relations, such as hierarchy, of the elements that belong to the structure. It is also good practice to use international standard metadata checklists to describe the content your (meta)data. A (meta)data checklist is a fixed set of attributes about the data that needs to be provided. Some attributes are mandatory, some are only recommended or optional. International standard metadata schemas and checklists are developed by and accepted as standards by communities. There are many standard metadata schemas and checklists, some generic, while others discipline-specific. See the paragraph about how to find standard metadata.

  • You can use the attributes of metadata schemas and checklists in a format that is not machine-readable or -actionable (e.g., by copying the metadata fields in a README.txt file or in a Codebook.xls). However, using standard metadata in a machine-readable or -actionable format will increase the findability of your data.

  • Metadata schemas and checklists usually rely on ontologies and controlled vocabularies, which make your data more reusable and interoperable. See the paragraph about how to find ontologies and controlled vocabularies.

  • We recommend familiarising yourself with the requirements of the repositories that could be appropriate for publishing your data already at the beginning of the project, so that you can start documenting and formatting your data according to their requirements as early as possible.

How do you find appropriate standard metadata for datasets or samples?


There are multiple standards for different types of data, ranging from generic dataset descriptions (e.g. DCAT, Dublin core, (bio) to specific data types (e.g. MIABIS for biosamples). Therefore, how to find standard metadata, and how to find an appropriate repository for depositing your data are relevant questions.


  • Decide at the beginning of the project what are the recommended repositories for your data types.
    • Note that you can use several repositories if you have different data types.
    • Distinguish between generic (e.g. Zenodo) and data type (technique) specific repositories (e.g. EBI repositories).


  • If you have a repository in mind:
    • Go to the repository website and check the “help”, “guide” or “how to submit” tab to find information about required metadata.
    • On the repository website, go through the submission process (try to submit some dummy data) to identify metadata requirements. For instance, if you consider publishing your transcriptomic data in ArrayExpress, you can make your metadata spreadsheet by using Annotare 2.0 submission tool, at the beginning of the project.
    • Be aware that data type specific repositories usually have check-lists for metadata. For example, the European Nucleotide Archive provides sample checklists that can also be downloaded as a spreadsheet after log in.
  • If you do not know yet what repository you will use, look for what is the recommended minimal information (i.e. “Minimum Information …your topic”, e.g. MIAME or MINSEQE or MIAPPE) required for your type of data in your community, or other metadata, at the following resources:

How do you find appropriate vocabularies or ontologies?


Vocabularies and ontologies are describe concepts and relationships within a knowledge domain. Used wisely, they can enable both humans and computers to understand your data. There is no clear-cut division between the terms “vocabulary” and “ontology”, but the latter is more commonly used when dealing with complex (and perhaps more formal) collections of terms and relationships. Ontologies typically provide an identifier.

There are many vocabularies and ontologies available on the web. Finding a suitable one can be difficult and time-consuming.


  • Check whether you really need to find a suitable ontology or vocabulary yourself. Perhaps the repository where you are about to submit your data have recommendations? Or the journal where you plan to publish your results?
  • Understand your goal with sharing data. Which formal requirements (by e.g. by funder or publisher) need to be fulfilled? Which parts of your data would benefit the most from adopting ontologies?
  • Learn the basics about ontologies. This will be helpful when you search for terms in ontologies and want to understand how terms are related to one another.
  • Accept that one ontology may not be sufficient to describe your data. It is very common that you have to combine terms from more than one ontology.
  • Accept terms that are good enough. Sometimes you you cannot find a term that perfectly match what you want to express. Choosing the best available term is often better than not choosing a term at all. Note that the same concept may also be present in multiple ontologies.


Related pages

More information

Relevant tools and resources

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Tool or resource Description Related pages Registry
AgroPortal Browser for ontologies for agricultural science based on NBCO BioPortal. Plant Phenomics Plant sciences Tool info Standards/Databases
CEDAR CEDAR is making data submission smarter and faster, so that scientific researchers and analysts can create and use better metadata. Machine actionability Researcher Data Steward: research Tool info Standards/Databases
COPO Portal for scientists to broker more easily rich metadata alongside data to public repos. Researcher Plant sciences Machine actionability Plant Phenomics Plant Genomics Tool info Standards/Databases
Create a Codebook Examples and tools to create a codebook by the Data Documentation Initiative (DDI) Researcher Data Steward: research
Data Catalog Unique collection of project-level metadata from large research initiatives in a diverse range of fields, including clinical, molecular and observational studies. Its aim is to improve the findability of these projects following FAIR data principles. TransMed
Data Curation Centre Metadata list List of metadata standards Researcher Data Steward: research
e!DAL-PGP Plant Genomics and Phenomics Research Data Repository Plant sciences Plant Genomics Researcher Data Steward: research Data Steward: infrastructure Data publication Plant Phenomics Standards/Databases
EMBL-EBI Ontology Lookup Service EMBL-EBI’s web portal for finding ontologies Data Steward: research Researcher
Evidence and Conclusion Ontology (ECO) Controlled vocabulary that describes types of evidence and assertion methods Existing data Standards/Databases
FAIR Data Point (FDP) A FAIR Data Point stores metadata in a standardized and sharable way. Rare disease data Data Steward: infrastructure Standards/Databases Training
FAIRDOMHub Data, model and SOPs management for projects, from preliminary data to publication, support for running SBML models, etc. (public SEEK instance) Data storage Researcher NeLS Microbial biotechnology Machine actionability Data Steward: research Standards/Databases
FAIRsharing A curated, informative and educational resource on data and metadata standards, inter-related to databases and data policies. Data publication Data Steward: policy Data Steward: research Researcher Microbial biotechnology Existing data Standards/Databases Training
Gene Expression Omnibus (GEO) A repository of MIAME-compliant genomics data from arrays and high-throughput sequencing Microbial biotechnology Data publication Data transfer OMERO Bioimaging data Toxicology data
Harvard Medical School - Electronic Lab Notebooks ELN Comparison Grid by Hardvard Medical School Identifiers Researcher Data Steward: research
Image Data Resource (IDR) A repository of image datasets from scientific publications Microbial biotechnology Data publication Data transfer OMERO Bioimaging data Tool info Standards/Databases
Intrinsically disordered proteins ontology (IDPO) Intrinsically disordered proteins ontology Intrinsically disordered proteins Tool info
Linked Open Vocabularies (LOV) Web portal for finding ontologies Data Steward: research Researcher
MIADE Minimum Information About Disorder Experiments (MIADE) standard Researcher Data Steward: research Intrinsically disordered proteins
MIAPPE Minimum Information About a Plant Phenotyping Experiment Researcher Data Steward: research Plant sciences Plant Genomics Plant Phenomics Standards/Databases Training
MIGS/MIMS Minimum Information about a (Meta)Genome Sequence Researcher Data Steward: research Marine metagenomics Microbial biotechnology Standards/Databases
MIxS Minimum Information about any (x) Sequence Researcher Data Steward: research Marine metagenomics Plant Genomics Standards/Databases Training
Multi-Crop Passport Descriptor (MCPD) The Multi-Crop Passport Descriptor is the metadata standard for plant genetic resources maintained ex situ by genbanks. Researcher Data Steward: infrastructure Data Steward: policy Plant sciences Plant Phenomics Plant Genomics Standards/Databases Training
OMERO OMERO is an open-source client-server platform for managing, visualizing and analyzing microscopy images and associated metadata Data Steward: research Data Steward: infrastructure Data storage OMERO Bioimaging data Tool info Training
OnotoMaton OntoMaton facilitates ontology search and tagging functionalities within Google Spreadsheets. Researcher Data Steward: research Data Steward: infrastructure Identifiers
Ontobee A web portal to search and visualise ontologies Data Steward: research Researcher Standards/Databases
OTP One Touch Pipeline (OTP) is a data management platform for running bioinformatics pipelines in a high-throughput setting, and for organising the resulting data and metadata. Human data Data management plan Data analysis Tool info
PANGAEA Data Publisher for Earth and Environmental Science Data publication Researcher Data Steward: research Tool info Standards/Databases
pISA-tree A data management solution for intra-institutional organization and structured storage of life science project-associated research data, with emphasis on the generation of adequate metadata. Microbial biotechnology Researcher Data Steward: research Data organisation Plant Phenomics Plant Genomics Tool info
RDA Standards Directory of standard metadata, divided into different research areas Researcher Data Steward: research
Research Object Crate (RO-Crate) RO-Crate is a lightweight approach to packaging research data with their metadata, using An RO-Crate is a structured archive of all the items that contributed to the research outcome, including their identifiers, provenance, relations and annotations. Data storage Data organisation Data Steward: research Researcher Microbial biotechnology Machine actionability Data provenance Standards/Databases
Rightfield RightField is an open-source tool for adding ontology term selection to Excel spreadsheets Researcher Data Steward: research Microbial biotechnology Identifiers Machine actionability Tool info
Semares All-in-one platform for life science data management, semantic data integration, data analysis and visualization Researcher Data Steward: research Data analysis Data Steward: infrastructure Data storage
The Genomic Standards Consortium (GSC) The Genomic Standards Consortium (GSC) is an open-membership working body enabling genomic data integration, discovery and comparison through international community-driven standards. Researcher Data Steward: infrastructure Data Steward: policy Human data Standards/Databases
The Open Biological and Biomedical Ontology (OBO) Foundry Collaborative effort to develob interoperable ontologies for the biological sciences Data Steward: research Researcher Standards/Databases
UniProt Comprehensive resource for protein sequence and annotation data Researcher Intrinsically disordered proteins Microbial biotechnology Proteomics Structural Bioinformatics Tool info Standards/Databases Training
University of Cambridge - Electronic Research Notebook Products List of Electronic Research Notebook Products by University of Cambridge Identifiers Researcher Data Steward: research
Zooma Find possible ontology mappings for free text terms in the ZOOMA repository. Data Steward: research Researcher Tool info Training
BioPortal A comprehensive repository of biomedical ontologies Tool info Standards/Databases Training
National resources

The German Human Genome-Phenome Archive.

Data storage Researcher Data Steward: research
CyVerse UK

The CyVerse Data Store is a cloud-based storage space, accessible via the CyVerse Discovery Environment (DE), a virtual bioinformatics lab workbench, and developer APIs such as the AGAVE API. In the DE, users can share datasets and tools to analyse data with as many or as few people as they wish.

Data Steward: research Researcher
Jisc Research data management toolkit

Guidance on the research data lifecycle that signposts resources from a wide range of organisations and websites.

Data Steward: research Researcher

Linked data schemas for the fields of agriculture, food, agri-business, plant biology.

Data Steward: research Researcher

InterMine integrates heterogenous data sources, making it easy to query and analyse data.

Data Steward: research Researcher