Knowledge clip: Metadata

Summary

This video script emphasizes the critical role of metadata and documentation in research data management (RDM), highlighting how they enable data discovery and reuse. It distinguishes between metadata and documentation, with metadata being structured data descriptors, essential for making data FAIR. The script covers various types of metadata, including descriptive, technical, administrative, and structural, and discusses their creation, storage, and importance in data repositories. It also touches on metadata standards, which facilitate data exchange and interoperability across different research domains.

Takeaways

• 📄 **Metadata Importance**: Metadata is crucial for Research Data Management (RDM), aiding in data discovery and reuse.
• 🔍 **Metadata Definition**: Metadata is data that describes other data, structured for machine readability.
• 🔑 **Types of Metadata**: Descriptive, technical, administrative, and structural metadata serve different purposes in data management.
• 🔎 **Descriptive Metadata**: Includes elements like title, author, and keywords to facilitate data discovery.
• 🛠️ **Technical Metadata**: Covers technical aspects such as file type, size, and access methods.
• 🏛️ **Administrative Metadata**: Deals with intellectual property rights, licenses, and access restrictions.
• 🌐 **Structural Metadata**: Indicates how datasets relate to other online resources.
• 🤖 **Metadata Creation**: Can be generated automatically by instruments or manually by researchers.
• 💾 **Metadata Storage**: May be embedded within files, stored separately, or provided via data repositories.
• 📚 **Metadata in Files**: Day-to-day digital files and discipline-specific formats often include embedded metadata fields.
• 📊 **Metadata Standards**: Standards like Dublin Core and DDI ensure interoperability and consistency across different systems.

Q & A

What is metadata in the context of research data management (RDM)?

Metadata in RDM is data that describes other data, providing essential information about data in a structured way to make it machine-readable, facilitating its discovery, assessment, and reuse.

Why is metadata considered crucial for making data FAIR?

Metadata is essential for making data FAIR because it allows data to be found, accessed, and reused by providing information on how to locate and utilize the data without needing to download it first.

What are the different types of metadata mentioned in the script?

The script mentions four types of metadata: Descriptive, Technical, Administrative, and Structural metadata.

What does Descriptive metadata include and why is it important?

Descriptive metadata includes elements like title, author, and keywords that help in discovering the data. It is important for making data easily searchable and understandable.

Can you explain the role of Technical metadata in research?

Technical metadata provides information about the technical aspects of data or files, such as file type, size, and access methods, which are crucial for data processing and analysis.

How does Administrative metadata differ from other types of metadata?

Administrative metadata focuses on intellectual property rights, including license, access rights, and restrictions, which are essential for managing data usage and permissions.

What is the significance of Structural metadata in data management?

Structural metadata indicates how a dataset relates to other online resources, helping in understanding the data's context and its integration with other datasets.

How can metadata be generated and where can it be found?

Metadata can be generated automatically by instruments or software, or manually by researchers. It can be found embedded within files, in separate files, or provided when uploading data to a repository.

What are the challenges associated with manually created metadata?

Manually created metadata can face challenges like maintaining the link between metadata and data, ensuring machine readability, and adhering to standard formats for easy data discovery and reuse.

Why is it recommended to use metadata standards when documenting research data?

Using metadata standards ensures consistency and interoperability across different systems and applications, making research data more accessible and reusable.

How do data repositories facilitate the FAIRness of data?

Data repositories provide functionalities to create and manage machine-readable metadata, which increases the findability, accessibility, interoperability, and reusability of the data.

Outlines

00:00

📄 The Importance of Metadata in Research Data Management

This paragraph emphasizes the critical role of metadata and documentation in Research Data Management (RDM), ensuring data discoverability and reusability. Metadata, defined as data that describes other data, is structured to be machine-readable, thus facilitating data search, assessment, and reuse. The paragraph introduces different types of metadata: descriptive (e.g., title, author, keywords), technical (e.g., file type, size), administrative (e.g., license, access rights), and structural (indicating data set relations). It also discusses how metadata can be generated, either automatically by instruments or manually, and can be stored within files or as separate files. The importance of maintaining the link between metadata and data is highlighted, with examples of metadata in everyday digital files and the challenges of custom metadata approaches.

05:01

🗂 Metadata Creation, Storage, and Standards

The second paragraph delves into the creation and storage of metadata, explaining that metadata can be generated by research instruments, software, or manually. It mentions the risk of losing the link between metadata and data, especially when files are moved. The paragraph then transitions to discussing metadata standards, which are sets of elements used to describe resources, with examples of generic standards like Dublin Core and discipline-specific standards like Ecological Metadata Language (EML). The use of metadata standards is crucial for data exchange and interoperability. The paragraph concludes by encouraging researchers to familiarize themselves with metadata requirements of data repositories, which aid in making data more FAIR (Findable, Accessible, Interoperable, Reusable), and to document metadata throughout the research process for ease of data management and sharing.

Keywords

💡Metadata

Metadata refers to data that provides information about other data. In the context of the video, metadata is crucial for research data management (RDM) as it enables data to be found and reused. The video emphasizes that metadata is structured in a way that is machine-readable, which facilitates searching, assessing the usefulness of data without downloading it, and understanding how the data can be accessed and reused. Examples from the script include metadata fields like title, author, and keywords that help in discovering the data.

💡Machine Readability

Machine readability is the property of metadata that allows it to be read and processed by computers. The video explains that metadata is highly structured to ensure it is machine-readable, which is essential for efficient data search and retrieval. This concept is integral to the video's message about making data findable and reusable, as it ensures that metadata can be easily processed by various systems and tools used in RDM.

💡Descriptive Metadata

Descriptive metadata includes elements that aid in the discovery of data. The video mentions that this type of metadata encompasses common elements like the title of the dataset, the author, and keywords. These elements are essential for users to understand what the data is about and to locate relevant datasets, as they provide a basic description that helps in the initial assessment of the data's relevance.

💡Technical Metadata

Technical metadata pertains to the technical aspects of data or files. According to the video, this could include information on how to access the data, the file type used, or the size of the file. This type of metadata is vital for understanding the format and accessibility of the data, ensuring that users can effectively work with the data once found.

💡Administrative Metadata

Administrative metadata deals with the management and legal aspects of data, such as intellectual property rights, licenses, and access rights or restrictions. The video underscores the importance of this metadata in ensuring that data is used appropriately and in compliance with relevant laws and policies. It is a key component in making data FAIR (Findable, Accessible, Interoperable, Reusable).

💡Structural Metadata

Structural metadata indicates how a dataset relates to other online resources. The video explains that this type of metadata is important for understanding the context and relationships between different data sets. It helps in navigating complex data landscapes and ensures that data can be integrated and analyzed effectively.

💡Data Repository

A data repository is a digital archive where research data is stored and managed. The video discusses how uploading data to a repository can increase the FAIRness of data by providing functionalities to create and manage machine-readable metadata. Repositories are essential for data sharing and long-term preservation, ensuring that data remains accessible and reusable.

💡Metadata Standards

Metadata standards, also known as metadata schemas, define the set of elements used to describe a resource and the format in which they should be presented. The video mentions standards like Dublin Core and Data Documentation Initiative (DDI), which facilitate data exchange by making metadata interoperable across different systems and applications. Adhering to metadata standards is crucial for ensuring that data can be easily understood and used by a wide range of users.

💡FAIR Data Principles

The FAIR Data Principles are a set of guidelines aimed at making data more findable, accessible, interoperable, and reusable. The video discusses how metadata and data repositories contribute to these principles, particularly by making data more discoverable and usable. The FAIR principles are a key theme in the video, as they represent the ideal state for research data to be in for maximum impact and utility.

💡Readme Files

Readme files are documents that provide information about other files, often used to record metadata in a structured way. The video mentions that readme files can be a useful method for collecting metadata during a project. They help maintain the link between the data and its metadata, which is essential for data management and sharing. However, the video also cautions that custom-made approaches can make metadata less machine-readable if not done correctly.

Highlights

Metadata and documentation are crucial for Research Data Management (RDM).

Metadata is data that describes other data, often in a highly structured format for machine readability.

Metadata facilitates data searchability, assesses data usefulness, and clarifies data access and reuse.

Descriptive metadata helps in data discovery, including elements like title, author, and keywords.

Technical metadata provides information about data or file access, file type, and size.

Administrative metadata deals with intellectual property rights, licenses, and access restrictions.

Structural metadata indicates how a dataset relates to other online resources.

Metadata can be generated automatically by instruments like microscopes or manually by researchers.

Metadata can be stored embedded within files or as separate files.

Day-to-day digital files often include metadata fields for sorting and searching.

Discipline-specific file formats may have additional embedded metadata fields.

Metadata can be generated by processing or analysis software, such as statistical packages.

Metadata headers in files often follow agreed conventions or standards.

Separate files for metadata are common, especially for configuration or calibration data.

Readme files can be used to collect metadata during a project but have risks of losing data-linkage.

Data repositories provide functionalities to create and manage machine-readable metadata.

Metadata standards define the elements used to describe a resource and their required formats.

Generic metadata standards like Dublin Core can be used across different scientific domains.

Discipline-specific standards contain additional elements to meet the needs of particular scientific domains.

Using metadata standards facilitates data exchange and interoperability.

Familiarizing with metadata requirements of repositories is essential for making data FAIR.

Transcripts

[Music]

metadata and documentation play an

important role in rdm

enabling data to be found and reused

metadata is often defined as data that

describes other data

if you have seen our knowledge clip

about documentation you might remember

that the key difference between them

is that metadata records essential

information about data in a highly

structured way

using a set of defined information

fields or elements

the reason why metadata is highly

structured is because it is meant to be

readable and exchangeable by computers

something often referred to as machine

readability

metadata is needed for many things it

facilitates the process

of searching and finding data metadata

can help us to assess whether the data

we find is useful for us or not

without having to download it first it

also lets us know how the data can be

accessed and how can it be reused

because of this

metadata is essential to make your data

fair let's now have a look at some

metadata concepts to understand why it

is so important

first of all there are different types

of metadata

a first type is called descriptive

metadata this type includes common

elements or fields that help us to

discover the data

this can be for instance things like

title of the data set the author

keywords describing the subject and so

on

when we talk about technical metadata we

mean information about technical aspects

of the data or files

this could be for instance information

about how to access the data

the file type used or the size of the

file

administrative metadata contains

elements or fields that deal with

intellectual property rights such as the

license

or access rights or restrictions

finally there is also structural

metadata this type of metadata indicates

how the data set relates to other online

resources

so how is metadata created and where can

we find it

metadata can be associated to many

different research objects and appear in

many different ways

sometimes metadata is generated

automatically

some instruments such as microscopes

telescopes or digital cameras create

metadata when data is collected

but this is not always the case other

times metadata needs to be manually

created

for instance by taking notes in a

laboratory notebook or by filling out a

form or data listing

the second question is how is metadata

stored

metadata can be stored embedded within

the files or it can be stored as

separate files

and another way to provide metadata

comes when you upload your data to a

data repository or archive

let's have a look at some details and

examples

most day-to-day digital files include a

range of metadata fields

these allow you for example to search

and sort files according to date created

file type author size etc

often discipline specific file formats

might also have additional embedded

metadata fields

for example microscopy images normally

include the objective settings within

the file

besides research instrumentation

metadata can also be generated by

processing or analysis software

for example statistical packages such as

spss

embed rich metadata within the file like

formats or additional variable

information

it is important to find out whether the

file formats you use of metadata fields

embedded

and if these are needed to use the data

if you plan to convert a file with

embedded metadata to a different file

format you should check whether these

metadata will also be present in the new

format

in some domains another place where

metadata can be found is in the header

of the files

typically this is a section at the top

of the document preceding the data

containing a summary of the data or

information about the instrumentation

settings

about the variables etc

often this metadata header follows

agreed conventions or standards

and the information it contains can be

read by applications processing software

or algorithms

in other cases a metadata header can be

manually created by a researcher

for example to provide contextual

details about an interview in the

transcription file

when metadata is generated by research

instrumentation or software

it might also be stored on a separate

file

for example sensors and measurement

devices often provide configuration or

calibration files

and software used to process

geographical data might store

geospatial metadata such as the

coordinate system in separate files

but these separate files can also be

manually generated by the researcher

for example in a readme file or a

spreadsheet

recording metadata in such way can also

be done in a structured way

and often templates are available to

help you

using readme files can be a useful way

to collect metadata during the course of

the project

however this approach has some downsides

for example

there is a risk that the link between

metadata and the data they represent is

lost

for example when files are moved

keeping some kind of metadata is

certainly better than collecting no

metadata at all

but as a general rule custom-made

approaches make difficult for metadata

to be machine readable

and your data become less findable and

reusable

and this takes us to our last point

providing metadata on a data repository

or archive

depositing your data on a repository

might be required by your institution or

research fund or policies

or by the journal in which you want to

publish your results even if not

required

it is a good research practice and will

increase the fairness of your data

because data repositories provide

functionalities to make your data more

fair

including services to create and manage

metadata

to upload your data to a repository you

will be required to fill in a

user-friendly form to describe your data

all the fields in this form are in fact

metadata fields pre-configured to meet a

specific metadata standard

allowing the result to become machine

readable then

what are metadata standards when the

information fields captured within a

specific metadata set become widely used

and accepted

it often evolves into a metadata

standard

to put it simply a metadata standard or

metadata schema defines the set of

elements that can or must be used to

describe a resource

the standard also tells you how these

elements should be named

and also which values are allowed or

what the required format is for each of

the elements

some metadata standards are designed to

be used across different scientific

domains

examples of such generic standards are

the dublin core standard

or data site but there are also

discipline specific standards which

typically contain additional elements

to satisfy the needs of a particular

scientific domain

for example the ecological metadata

language is used in ecology research and

has additional elements

such as taxonomic coverage to indicate

which species are included in the data

set

another example of a specialized

metadata standard is the data

documentation initiative

or ddi this standard contains elements

such as questionnaire specification for

research that involves surveys

the use of metadata standards

facilitates data exchange by different

systems or applications

in other words it makes research

metadata interoperable

one of the fair data principles

to recap during the research process

metadata can be created in different

ways and appear in multiple forms

an important use of metadata is to make

your data findable and let others know

how they can access it

and reuse it data repositories provide

you the functionalities to create and

manage machine-readable metadata

and therefore make your data more fair

that is why it is a good idea to

familiarize yourself with the kind of

metadata that repositories require

during the course of the project make

sure to document this information

so that when the time comes to provide

the metadata you are not only relying on

your fading memory

for more information about metadata and

data repositories

have a look at our website