Introduction

Overview

Teaching: 10 min
Exercises: 5 min

Questions

• 1 Does FAIR data means open data?

• 2 What are Digital Objects and Persistent Identifiers?

• 3 Different types of PIDs

Objectives

• The participant will understand that the FAIR principles are fundamental for Sustainable Science.

• The participant will learn what human and machine-friendly digital objects are.

1. Does FAIR data means open data?

No.
FAIR means human and machine-friendly data sources which aim for transparency in science and future reuse

What does it mean to be machine-readable vs human-readable?

Human Readable “Data in a format that can be conveniently read by a human. Some human-readable formats, such as PDF, are not machine-readable as they are not structured data, i.e. the representation of the data on disk does not represent the actual relationships present in the data.”
Machine Readable “Data in a data format that can be automatically read and processed by a computer, such as CSV, JSON, XML, etc. Machine-readable data must be structured data. Compare human-readable. Non-digital material (for example, printed or hand-written documents) is not machine-readable by its non-digital nature. But even digital material need not be machine-readable. For example, consider a PDF document containing tables of data. These are definitely digital but are not machine-readable because a computer would struggle to access the tabular information - even though they are very human readable. The equivalent tables in a format such as a spreadsheet would be machine-readable. As another example, scans (photographs) of text are not machine-readable (but are human readable!) but the equivalent text in a format such as a simple ASCII text file can machine readable and processable.”

Machine friendly = Machine-readable + Machine-actionable + Machine-interoperable

During this sourcebook, we will be using “Machine-readable” and “Machine friendly” interchangeably. We like the term “friendly” since it can also include “machine-actionability” and “machine-interoperability.”

Exercise - Level Medium 🌶🌶

Understanding the jargon of data “things” Data collection varies depending the field of research, therefore the digital object that is discribed.

• Task: Perform with your group a search of the following data “things” and discuss the meaning, similarity and differences of each one of them.
• Dataset
• Database
• Data collection
• Data type
• Data model

2. What are Digital Objects and Persistent Identifiers?

A Digital Object is a bit sequence located in a digital memory or storage that has, on its own, informational value. For example:

• A Scientific Publication
• A Dataset
• A Rich Metadata file
• A README file containing Terms of use & Access Protocols

To learn more about FAIR Digital Objects

FAIR Digital Objects: Which Services Are Required? (Schwardman, Ulrich 2020)

FAIR Digital Object Framework Documentation (BdSS, Luiz Olavo 2020)

A Persistent Identifier (PID) is a long-lasting reference to a (digital or physical) resource:

• Designed to provide access to information about a resource even if the resource it describes has moved location on the web
• Requires technical, governance and community to provide the persistence
• There are many different PIDs available for many different types of scholarly resources, e.g. articles, data, samples, authors, grants, projects, conference papers, and so much more

Video: The FREYA project explains the significance of PID: LINK TO SOURCE

Different types of PIDs

PIDs have community support, organisational commitment and technical infrastructure to ensure the persistence of identifiers. They often are created to respond to a community’s needs. For instance, the International Standard Book Number or ISBN was created to assign unique numbers to books, is used by book publishers, and is managed by the International ISBN Agency. Another type of PID, the Open Researcher and Contributor ID or ORCID (iD), was created to help with author disambiguation by providing unique identifiers for authors. The ODIN Project identifies additional PIDs along with Wikipedia’s page on PIDs.

In Episode 6 (Data Archiving), you will explore one type of PID, the DOI (Digital Object Identifier), which is usually the standard PID for Datasets and Publications

Exercise - Level Easy 🌶

1. arXiv is a preprint repository for physics, math, computer science and related disciplines.
2. It allows researchers to share and access their work before it is formally published.
3. Visit the arXiv new papers page for Machine Learning.
4. Choose any paper by clicking on the ‘pdf’ link. Now use control + F or command + F and search for ‘HTTP’. Did the author use DOIs for their data?

Solution

Authors will often link to platforms such as GitHub where they have shared their software, and/or they will link to their website hosting the data used in the paper. The danger is that platforms like GitHub and personal websites are not permanent. Instead, authors can use repositories to deposit and preserve their data and software while minting a DOI. Links to software sharing platforms or personal websites might move, but DOIs will always resolve to information about the software and/or data. See DataCite’s Best Practices for a Tombstone Page.

DOIs are everywhere, examples:

• Resource IDs (articles, data, software, …)
• Researcher IDs
• Organisation IDs, Funder IDs
• Projects IDs
• Instrument IDs
• Physical sample IDs,
• DMP IDs…
• Media: videos, images, 3D models

Key Points

• FAIR means human and machine friendly data sources which aim for transparency in science and future reuse.

• DOI (Digital Object Identifier) is a type of PID (Persistent Identifier)

Data Terms of Use

Overview

Teaching: 10 min
Exercises: 15 min

Questions

• 1 What are Data Terms of Use?

• 2 What a Data Terms of Use statement must contain?

• 3 What format should Data Terms of Use be?

• 4 Are there standard Licenses we can pick up from?

Objectives

• The participant will understand what data terms of use are with examples.

• The participant will be able to create basic data terms of use.

FAIR principles used in Data Terms of Use:

Accessible

FM-A2 (Metadata Longevity) → doi.org/10.25504/FAIRsharing.A2W4nz

Reusable

FM-R1.1 (Accessible Usage License) → doi.org/10.25504/FAIRsharing.fsB7NK

1 What are Data Terms of Use?

Data Terms of Use is a textual statement that set the rules, terms, conditions, actions, legal considerations and licences that delineate data use.

An example is:
The World Bank - Terms of Use for Datasets → LINK TO EXAMPLE

Looking at the example, we can identify general elements in the Data Terms of Use statement. For instance, a broad description of the data is referred to, but also under what type of license the user is allowed to reuse.

Sometimes as part of our research, we use commercial databases. We should be careful always to read the conditions for using it for research purposes An example is:
Terms of Use - Numbeo.com → LINK TO EXAMPLE
We can see that clause 3 of Licensing of content requests work attribution.

2 What a Data Terms of Use statement must contain?

As a general rule is that a Data Terms of Use statement must contain at least the following:

Nevertheless, depending on the use case, the “Data Terms of Use” statement can be extended by adding specific clauses when complex data, multiple databases or sensitive data are involved. After all, the “Data Terms of Use” statement is the legal basis of the referred data source. i.e. in the light of public law, you can make someone liable for not complying with specific clauses of the statement.

Moreover, sometimes our work is conducted within the context of a greater scientific funded project. Therefore, it is always recommended to check with the Principal Investigator or Project Manager whether the Data Terms of Use statement is to be defined or if there is already a Data Policy framework.

An example is:
Policy for use and oversight of samples and data arising from the Biomedical Resource of the 1958 Birth Cohort (National Child Development Study) → LINK TO EXAMPLE

• Link to the original Data Policy

This policy framework creates comprehensive guidelines on handling data for that specific study involving children’s data. Therefore, the researchers working underneath the project do not have to make new Data Terms of Use.

“Data Terms of Use” statement is the legal basis of the referred data source

In the light of public law, you can make someone liable for not complying with specific clauses of the statement.

3 What format should Data Terms of Use be?

The Data Terms of Use is a plain text statement. This text has the length and depth that the data owner or data manager sees fit. There is no right or wrong when drafting it. Usually is recommended to store this textual statement in a README file, using an accessible format, for example, .txt, .md or .html so that any user can read it without needing any additional software.

The Data Terms of Use is a plain text statement written in a machine-friendly format

The “Data Terms of Use” can be drafted using any application (e.g. MS Word). However, it’s important to store it in a machine-friendly format such as .txt or .md

Any text editor software would do the trick, such as Notepad++ or Sublime Text, but also you can write it using Microsoft Word or Google Docs and save it as .txt

Exercise - Level Medium 🌶🌶

1. Visit the landing page of the following terms of use github.com/CityOfPhiladelphia/terms-of-use/blob/master/LICENSE.md
2. Can you tell what type of data it is about?
3. Can you tell in what format the terms of use are written?
4. What platform are they using to put it?

Solution

• Refers to the public code on which the large city of Philadelphia government is based (https://www.phila.gov/)
• The format is Markdown (.md)
• They used Github to put the LICENSE.md file, which is the Data Terms of Use.

The Data Terms of Use needs to be in the same root folder as the data source. When it comes to a database - like the World Bank example - it should be findable on the project’s website. Moreover, if there is no official project website, you should include it in .md format in a Github repository like the following example: → LINK TO EXAMPLE

In Episode 4 (Data Archiving), we will explore that some data repositories such as DataverseNL allow you to create a Data Terms of Use statement directly on the platform when you create a data project.
By default, you get a waiver License CC0 “No Rights Reserved”. Putting a database or dataset in the public domain under CC0 is a way to remove any legal doubt about whether researchers can use the data in their projects. Although CC0 doesn’t legally require data users to cite the source, it does not affect the ethical norms for attribution in scientific and research communities. Moreover, you can change this waiver to a tailored Terms of Use you have created for your data.

Exercise - Level Easy 🌶

Is it possible to edit the Terms of Use in DataverseNL?

Go to DataverseNL/ to the FAQ section t find out.

Solution

Yes, it is possible. However, you can’t choose it at the beginning. After creating a dataset, go to the ‘Terms’ tab on your dataset page and click ‘Edit Terms requirements’. Next, select the radio button ‘No, do not apply CC0 public domain dedication’, and fill in the text fields with your terms and conditions.

Dataverse also provides Sample Data Usage Agreement → LINK

4 Are there standard Licenses we can pick up from?

Yes, there are two general License frameworks that can work for data.

• Creative Commons (CC)
• Open Data Commons (ODC)

Creative Commons (CC) provides several licenses that can be used with a wide variety of creations that might otherwise fall under copyright restrictions, including music, art, books and photographs. Although not tailored for data, CC licenses can be used as data licenses because they are easy to understand. Its website includes a summary page HERE outlining all the available licenses, explained with simple visual symbols.

Open Data Commons (ODC) provides three licenses that can be explicitly applied to data. The web pages of each of these licenses include human-readable summaries, with the ramifications of the legalese explained in a concise format.

Exercise - Level Easy 🌶

Pick up a License at creativecommons.org with the following conditions:

• Others cannot make changes to the work since it’s simulation data
• If someone wants to use the simulation data for a startup, they can Question: What type of license is it?

Solution

Attribution-NoDerivatives 4.0 International

Discussion

Real case:
The Swedish DPA (Integritetsskyddsmyndigheten) fined Umeå University SEK 550,000 (EUR 54,000) as a result of its failure to apply appropriate technical and organizational measures to protect data. As part of a research project on male rape, the university had stored several police reports on such related incidents in the cloud of a U.S. service provider. The reports contained the names, ID numbers and contact details of the data subjects, as well as information about their health and sex lives, alongside information about the suspected crime LINK TO CASE

• Discuss with your team how using legal instruments (in the context of the research) such as data terms of use or data protection impact assessment can be helpful in such situations.
• Do you consider GDPR as a hinder to collecting qualitative data and making it FAIR?

Key Points

• The Data Terms of Use statement is the legal basis of the referred data source

• A License is the bare minimum requirement for Data Terms of Use.

• If a standard License does not fit your project then you can use Terms of Use layouts e.g. Sample Data Usage Agreement

Data Descriptions

Overview

Teaching: 10 min
Exercises: 15 min

Questions

• 1 What are Data Descriptions?

• 2 How to reuse Data Descriptions?

• 3 Are there standard ways for doing Data Descriptions?

• 4 What is the relation between Data Descriptions and Linked Data?

Objectives

• The participant will learn that Data Descriptions are named differently in various fields.

• The participant will be able to create a machine-friendly Data Descriptions file.

FAIR principles used in Data Descriptions:

Interoperable

FM-I1 (Use a Knowledge Representation Language) → doi.org/10.25504/FAIRsharing.jLpL6i
FM-I2 (Use FAIR Vocabularies) → doi.org/10.25504/FAIRsharing.jLpL6i

Reusable

FM-R1.3 (Meets Community Standards) → doi.org/10.25504/FAIRsharing.cuyPH9

1. What are Data Descriptions?

Data descriptions are a detailed explanation and documentation of each data attribute or variable in a dataset.

Depending on the use case and field of research, these data descriptions are also known as:

• Codebooks (e.g. in Statistics or Social Sciences)
• Data Dictionaries (e.g. in Computer and Data Science)
• Labels or Data Tags (e.g. in Crowdsourcing or Humanities)
• Data Glossary (e.g. in Business Administration & Finance)
• Metadata (wrong use of this term in this context)

For example:

“Data Descriptions” is sometimes named differently depending on the field

No matter what terminology you use, “Data Descriptions” always refers to a detailed explanation and documentation of each data attribute or variable in a dataset.

2. How to reuse Data Descriptions?

Documentation of any kind always takes time. However, we shall always aim to reuse existing data descriptions generally accepted in the community. i.e. the variable Litigation is a concept that has been widely used in legal and politcal studies; therefore, we don’t need to redefine it every time.

For example, in EU Vocabularies, we can find existing descriptions of Litigation. These descriptions belong to an Ontology, i.e. a community-accepted online dictionary for curated terms and definitions. Moreover, it provides a globally unique identifier to the description. → LINK TO EXAMPLE

You will get several results when searching for a term and its definition. These results regard the different ontologies that define these terms. For example, think of the description of a musical instrument. It might be defined differently in a British dictionary than in an American one.

Finally, using this Ontology, you can get a standard definition that community experts curate has a global identifier.

Describe your data by reusing Ontology terms

By resuing Ontology terms or community-accepted vocabularies, we aim to create a culture of recycling definitions by default.

Advantages

• We don’t have to redefine the terms every time
• We get a permanent link to the resource
• Since it uses a global identifier becomes easier for others to integrate with different data sources
  Disadvantages
• Sometimes, you might not find an Ontology or vocabulary that fits your variable.

3. Are there standard ways for doing Data Descriptions?

There are no standard ways of doing Data Descriptions.
The minimum elements you need to describe your dataset are the Attribute Name and the Link to Description. You can do that in a tabular format. However, following the FAIR principles of Interoperability and Reusability, we must ensure that the data is described using community standard FAIR vocabularies. Here are some Ontologies for general use that can cover a wide variety of data attributes

There are also public registries where you can find Ontologies

CLARIAH - Curated list of Ontologies for Digital Humanities → github.com/CLARIAH/awesome-humanities-ontologies
EU Vocabularies: → op.europa.eu/en/web/eu-vocabularies
Linked Open Vocabularies → lov.linkeddata.es/dataset/lov/
BioPortal: → bioportal.bioontology.org/
AgroPortal: → agroportal.lirmm.fr/
EcoPortal → ecoportal.lifewatchitaly.eu/ Ontology Lookup Service by the EBI → ebi.ac.uk/ols/index

Exercise - Level Easy 🌶

1. Visit EU Vocabularies. EU Vocabularies is the reference website for curated vocabularies maintained by the Publications Office of the European Union.
2. Search for an Ontology term for Sector In the “Search our catalogue” search box.
3. Select one result that describes Sector that is sound to you.
4. What are the definition and ID?

Solution

Definition: A sector can be a subgroup of an economic activity - as in “coal mining sector” - or a group of economic activities - as in “service sector” - or a cross-section of a group of economic activities - as in “informal sector”. “Sector” is also a specific term used in the 1993 United Nations System of National Accounts to denote one of the five mutually exclusive institutional sectors that group together institutional units on the basis of their principal functions, behaviour and objectives, namely: nonfinancial corporations, financial corporations, general government, non-profit institutions serving households (NPISHs) and households.
ID: http://publications.europa.eu/resource/authority/sdmxglossary2018/SECTOR

The Data Descriptions are usually manually written in a tabular format. This document has the length and depth that the data owner sees fit. The general rule of thumb is to describe the dataset related to a publication. Any accessible format like .csv, .xls, or similar is acceptable.

In case it is a database, a data model must be included in machine-readable format (e.g. .sql) and a human-friendly diagram (e.g. ER model on .pdf)

There are examples where data descriptions are made available in a human-relatable manner, such as the dataset nutrition labels style (Holland et al., 2018).
An example is A Statutory Article Retrieval Dataset → LINK TO EXAMPLE
Moreover, there are tools and software packages to generate automated “Codebooks” by only looking at the dataset
An example is Automatic Codebooks from Metadata Encoded in Dataset Attributes → LINK TO EXAMPLE.

These initiatives are helping us standardise data descriptions and are “Human Friendly”, which works perfectly. However, the FAIR principles FM-I1, FM-I2 and FM-R1.3 explicitly mention the need for Linked Data formats in order to gain the maximum level of Interoperability.

4. What is the relation between Data Descriptions and Linked Data?

When we create comprehensive Data Descriptions reusing terminologies of existing Ontologies, we could make available our dataset in a Linked Data format which makes it Interoperable with other datasets out there.

Imagine your dataset can be helpful to another researcher in the future. The future researcher can reuse your dataset and combine both by matching variable names with the Ontology identifiers.

There are several tools that help you to convert your dataset from a conventional format into a Linked Data format (e.g. RDF format)

Exercise - Level Hard 🌶🌶🌶

1. Transform a dataset from XLSX format to RDF format using UM LDWizard

2. Download the following mock dataset: MOCK DATA

3. What ontology terms did you reuse to describe the data attributes?

Solution

There is no one single answer 🤓

Discussion

Scenario:
You are a digital history researcher, and your group will digitalize technology gadgets and artefacts that are not used anymore (e.g. VHS player), and it’s time to create data descriptions. However, there are no available Ontologies to describe the data records, given that they are contemporary history

Discuss with your team what the researcher should do given that apparently there are no available Ontologies to describe their data

Key Points

• ‘Codebook’ or ‘data glossary’ are some other ways to name Data Descriptions.

• Ontologies (in information science) are like public online vocabularies of community curated terms and their definitions.

• By resuing Ontology terms or community accepted vocabularies, we aim to create a culture of recycling terminology by default.

Data Access Protocols

Overview

Teaching: 10 min
Exercises: 15 min

Questions

• 1 What are data access protocols?

• 2 Is Open Access a data access protocol?

• 3 Can I expose my data as a service using FAIR API protocols?

Objectives

• The participant will learn what data access protocols are

• The participant will explore the aspects of an access protocol for humans and machines.

FAIR principles used in Data Access Protocols:

Accessible

FM-A1.1 (Access Protocol) → doi.org/10.25504/FAIRsharing.yDJci5
FM-A1.2 (Access Authorization) → doi.org/10.25504/FAIRsharing.EwnE1n

Interoperable

FM-I3 (Use Qualified References) → doi.org/10.25504/FAIRsharing.B2sbNh

1. What are Data Access Protocols?

Data Access Protocols are a set of formatting and processing rules for data communication. In practice, Data Access Protocols are the explicit instructions for humans and machines to access a data source.

Imagine you enter a security room. You need to follow the specific steps or possess specific keys for accessing the room. The same is with data. Moreover, if the door of the room is open we can say it is Open Access

Following the premise that “Data Access Protocols” are a common set of rules on a standard language, they exist in various ranges. For example, some messages are directed to humans, and some protocols are meant for machines.

Access Protocol	Example	Note
Communication between machines		A PC requesting information using HTTP protocol
Communication between humans		Data owner requesting users to contact deriectly for data access

Image: Mapping EU Company Mobility & Abuse-Detection → LINK TO EXAMPLE

In a strict sense, data access protocols relate to network protocol definitions. However, regarding Research Data, the human factor plays a part. Therefore we can explicitly mention the rules and instructions for accessing data depending on the use case. Sometimes the data can’t be publicly available, and there is no particular repository for it. Therefore you request the user to contact you to get access. We could say it is a human-friendly access protocol.

For example:

In Data Request Form UMC Utrecht → LINK TO EXAMPLE
The University Medical Center at Utrecht (UMC Utrecht) has an open data request form for users to access clinical data for research purposes. The form asks general things related to the researcher’s identification and affiliation, research context and methodologies. Furthermore, they explicitly mention that the Data Access Committee will review and consider applications and respond within 4 weeks.

Protocols are like standard rules communicated in a standard language for humans and machines.

We humans use predominantly the English language for communication in science.

Likewise, Machines need a “medium” to talk to each other such as an API (Application Programming Interface), and they use a “communication language” such as HTTP Hypertext Transfer Protocol to share information between one another.

Relevant API protocols are:

• SOAP API
• SPARQL API
• REST API

2. Is Open Access a data access protocol?

Open Access is a policy framework in the strict sense but yes! We could say it’s a data access protocol. Within the Open Science recommendations, it is endorsed to standardise open access datasets when it is possible to make them publicly available and does not violet legal or ethical considerations. More information at Open Access

Depositing datasets in public data repositories can grant them open access protocol automatically. In addition, data repositories typically work as a data archiving instrument, which we cover in Episode 6 (Data Archiving).

Moreover, Open Acess data sources can be made available using FAIR protocols such as SPARQL API endpoints.

An example is:

The European Union Public Data → LINK TO EXAMPLE
Which makes available all public datasets. the following endpoint (permanent link) https://data.europa.eu/sparql

Exercise - Level Easy 🌶

1. Go to ZENODO Covid 19 Community.
2. Can you tell what is the default “Data Access Protocol” for the Digital Objects displayed?

Solution

It is Open Access. It is indicated in the green tag on top of the titles.

Open Access is a human and machine-friendly Data Access Protocol

Humans see a “Download” button
Machines see an HTTPS request

3. Can I expose my data as a service using FAIR API protocols?

Yes, it is possible; however, it is not an easy road and requires technical skills.
We must remember that exposing data as a service would mean we need a server to host it, which we don’t always have. Moreover, one of the main motivations to expose our data using FAIR protocols is to make it accessible for other data sources to integrate. But for that, you would need some basic understanding of Knowledge Graphs technologies. More information about you can find in the FAIR Elixir Cookbok

Important!

When you expose your data using FAIR API protocols, you must register your service in a registry for FAIR APIs such as SMART API

Exercise - Level Hard 🌶🌶🌶

• Expose your RDF data to a service endpoint using a FAIR API protocol

  1. Use the RDF data you generated in Episode 2 (data descriptions) else you can download it here
  2. In your terminal, install the following library using the default Python installation pip install rdflib-endpoint@git+https://github.com/vemonet/rdflib-endpoint@main
  3. Next, execute the following command to locally expose your data rdflib-endpoint serve data-file.nt

Solution

This exercise is optional

Discussion

Access Protocols for Focus Groups Data?
Focus groups are a data collection method. Data is collected through a semi-structured group interview process. A quick overview of the method is found in Cohen D, Crabtree B. “Qualitative Research Guidelines Project.” - 2006.

• Discuss with your team all the different kinds of digital objects processed when conducting Focus Groups. For example, think of the Interview guidelines, the raw transcripts or the consent forms of the participants.
• Discuss what could it be the suitable Access Protocol those digital objects collected in the Focus Group.

Key Points

• Data Access Protocols are a set of formatting and processing rules for data communication. For example, imagine you enter a security room. You must follow certain steps or possess keys to access the room.

• When you expose your data using FAIR protocols, you must register your service in a registry for FAIR APIs such as SMART API

Data Archiving

Overview

Teaching: 10 min
Exercises: 15 min

Questions

• 1 What is Data Archiving?

• 2 What are Data Repositories?

• 3 What is a DOI, and why is it important?

Objectives

• The participant will understand the importance of archiving datasets on trusted data repositories.

• The participant will learn the significance of the Digital Object Identifier (DOI).

FAIR principles used in Data Archiving

Findable

FM-F1A (Identifier Uniqueness) → doi.org/10.25504/FAIRsharing.r49beq
FM-F3 (Resource Identifier in Metadata) → doi.org/10.25504/FAIRsharing.o8TYnW

Accessible

FM-A2 (Metadata Longevity) → doi.org/10.25504/FAIRsharing.A2W4nz

1. What is Data Archiving?

“Data Archiving” is the practice of placing a digital source in a preservation phase. i.e. the long-term storage of research data.

The various academic journals have different policies regarding how much of their data and methods researchers are required to store in a public archive. Similarly, the major grant-giving institutions have varying attitudes toward public archival of data. In general, publications must have attached sufficient information to allow fellow researchers to replicate and test the research.

2. What are Data Repositories?

Datasets are archived in Data repositories. They are storage locations for digital objects. Data repositories can help make a researcher’s data more discoverable by search engines (e.g. Google) and ultimately lead to potential reuse. Therefore, using storage can lead to increased citations of your work. Data repositories can also serve as backups during rare events where data are lost to the researcher and must be retrieved.

Note

Data Archiving is the long-term storage of research data Data repositories can help make a research data more discoverable by search engines (e.g. Googlebots)

Examples of data repositories (Including Qualitative Datasets)

Data Repository	About
DataverseNL	DataverseNL is a community-specific repository it focuses on Dutch universities and research centers
4TU Data	4TU Data is a community repository which was originally created by three technical universities in the Netherlands
The Qualitative Data Repository	Hosted by the Center for Qualitative and Multi-Method Inquiry, a unit of the Maxwell School of Citizenship and Public Affairs at Syracuse University
DARIAH-DE	The DARIAH-DE repository is part of the DARIAH Thematic Service funded by EOSC hub (Available in german
National Archive of Criminal Justice Data (NACJD)	Archives data on crime and justice for secondary analysis. It contains data from over 3,100 curated studies or statistical data series. Inter-university Consortium for Political and Social Research (ICPSR) University of Michigan
isidore	Search engine providing access to digital data from the Humanities and Social Sciences (SSH). Open to all and in particular to teachers, researchers, PhD students and students, it is based on the principles of Linked Data and provides open access to data. ISIDORE is a project of the Very Large Research Infrastructure Huma-Num (CNRS, Aix-Marseille University, Campus Condorcet).

Just like DataverseNL, many data repositories are for general use. They provide a low entry barrier to making data Findable addressing FM-F1A (Identifier Uniqueness) and FM-F3 (Resource Identifier in Metadata).

Recommendations for general purpose data repositories

ZENODO administrated by CERN
SURF Repository administrated by SURF
DataverseNL administrated by DANS
FigShare administrated by Figshare LLC
Wolfram Data Repository administrated by Wolfram Research, Inc.

Quick characteristics of general purpose repositories:

• Safe — your research is stored safely for the future in a Data Centre for as long as CERN and/or DANS exist.
• Citeable — every upload is assigned a Digital Object Identifier (DOI) to make them citable and trackable.
• No waiting time — Uploads are made available online as soon as you hit publish, and your DOI is registered within seconds.
• Open or closed — Share, e.g. anonymised clinical trial data with only medical professionals via our restricted access mode.
• Versioning — Easily update your dataset with our versioning feature.
• GitHub integration — Easily preserve your GitHub repository in Zenodo.
• Usage statistics — All uploads display standards-compliant usage statistics.

Image: Harvard Medical School, RDM - Data Repositories. Accessed Jul-2022 - *datamanagement.hms.harvard.edu/share/data-repositories

Original Hardvard Dataverse

Some public registries where you can find lists of trusted repositories for Data Archiving

Registry of Research Data Repositories (re3data) → re3data.org//
PLOS ONE Recommended Repositories → journals.plos.org/plosone/s/recommended-repositories
NIH Recommended Repositories: → sharing.nih.gov/repositories-for-sharing-scientific-data
Nature - Scientific Data Guidelines: → nature-com.mu.idm.oclc.org/sdata/policies/repositories
OpenDOAR - Directory of Open Access Repositories → v2.sherpa.ac.uk/opendoar/
Digital Research Infrastructure for the Arts and Humanities (DARIAH) → ariah.openaire.eu/

Exercise - Level Medium 🌶🌶

1. Go to dataverse.org and scroll down until you see a map. Respond to the following:
2. How many installations of Dataverse are?
3. How many Dataverse installations are in the Netherlands?
4. DataverseNL is the data repository hosted by DANS. It supports all higher education institutions in the Netherlands. How many datasets exist now in DataverseNL? (Aug 2022)

Solution

1. There are 83 installations worldwide. This means that 83 Organizations have a copy of the original Harvard Dataverse layout and have hosted it on their own servers to support researchers.
2. There are 3 installations in the Netherlands: DataverseNL, IISH Dataverse, and NIOZ Dataverse.
3. There are 6,075.

3. What is a DOI, and why is it important?

The DOI is a common identifier used for academic, professional, and governmental information such as articles, datasets, reports, and other supplemental information. The International DOI Foundation (IDF) is the agency that oversees DOIs. CrossRef and Datacite are two prominent not-for-profit registries that provide services to create or mint DOIs. Both have membership models where their clients are able to mint DOIs distinguished by their prefix. For example, DataCite features a statistics page where you can see registrations by members.

A DOI has three main parts:

• Proxy or DOI resolver service
• Prefix which is unique to the registrant or member
• Suffix, a unique identifier assigned locally by the registrant to an object

In the example above, the prefix is used by the Australian National Data Service (ANDS) now called the Australia Research Data Commons (ARDC) and the suffix is a unique identifier for an object at Griffith University. DataCite provides DOI display guidance so that they are easy to recognize and use, for both humans and machines.

Exercise - Level Hard 🌶🌶🌶

1. Upload a dataset in the DEMO DataverseNL repository

2. Download the following mock dataset: MOCK DATA

3. What is the DOI of your dataset?

Solution

There is no one single answer 🤓

Discussion

Scenario:
You are a researcher of Migration studies, and you conducted personal interviews. So naturally, you want your research data to be visible in your community, given the impact on the topic. Still, obviously, you can’t upload the transcripts, not even in a restricted form, since it’s an ethnographic study.

Discuss with your team how it would be best to handle data archiving in situations like this type of data.

Key Points

• Data repositories can make a research data more discoverable by machines (e.g. Google search engine).

• Always aim for a repository that fits your community (e.g. DataverseNL). Else, deposit your dataset on generic repositories (e.g. Zenodo).

• If the data is about human subjects or includes demographics, you can always choose to make it private or deposit an aggregated subset.

Rich Metadata

Overview

Teaching: 10 min
Exercises: 15 min

Questions

• 1 What is the difference between Metadata and Rich metadata?

• 2 How to create a Rich Metadata file?

• 3 Where to put a Rich Metadata file?

Objectives

• The participant will understand the difference between Metadata and Rich Metadata.

• The participant will be able to pull Rich Metadata files and harness them.

FAIR principles used for Rich Metadata:

Findable

FM-F2 (Machine-readability of Metadata) → doi.org/10.25504/FAIRsharing.ztr3n9

Interoperable

FM-I1 (Use a Knowledge Representation Language) → doi.org/10.25504/FAIRsharing.jLpL6i
FM-I2 (Use FAIR Vocabularies) → doi.org/10.25504/FAIRsharing.0A9kNV

1. What is the difference between Metadata and Rich metadata?

Metadata is the “data about the data”. It is a detailed description of the Digital Object referring to, for example, the documentation of dataset properties.

For example:

In Data for: Women Insurgents, Rebel Organization Structure and Sustaining the Rebellion: The Case of Kurdistan Workers’ Party → LINK TO EXAMPLE
We can see how a data source is described for future usage.

Metadata by itself is plain text; to be meaningful for further reuse, it needs to be in a machine-readable format. When data is submitted to a trusted data repository, the machine-readable metadata is generated by the data repository. If the information is not in a repository, a text file with machine-readable metadata can be added as part of the documentation.

Rich Metadata is

• Standardised
• Structured
• Machine- and human-readable
• A subset of documentation

  Metadata alone is plain text

There are many examples and resources where you can learn more about metadata. The idea is straightforward: we want to document the scientific data we generate for future use.

More resources

Metadata definition (The Turing Way) → LINK
What is Metadata, and how do I document my data? (CESSDA Training) → LINK
Rich Metadata and indexability by Search Engines (Google) LINK

S. Bonaretti (2019) Video: A walk-through video to describe Metadata in ZENODO.

2. How to create a Rich Metadata file?

There are several ways, but the main takeaway is that you don’t need to do it manually. The idea is to fill a form using a tool for Rich Metadata generation (table below) and export it (or copy-paste it) on a machine-readable format such as JSON-LD for interoperability reasons

Image: With Dataverse Export Button, you can quickly get your Rich Metadata file

3. Where to put a Rich Metadata file?

A straightforward rule: Everywhere your data is stored.

• In the project’s root folder
• In the data repository
• In the Github repository
• On the project’s webpage

Example:

More and more services are using common schemas such as DataCite’s Metadata Schema or Dublin Core to foster greater use and discovery. A schema provides an overall structure for the metadata and describes core metadata properties. While DataCite’s Metadata Schema is more general, there are discipline specific schemas such as Data Documentation Initiative (DDI) and Darwin Core.

Thanks to schemas, the process of adding metadata has been standardised to some extent but there is still room for error. For instance, DataCite reports that links between papers and data are still very low. Publishers and authors are missing this opportunity.

Discussion

Real case:
The project Practicing Legitimation: How Chinese State Capital is Transnationalized in Europe doi:10.34894/BFZF0Y** is a clear example of a study where the interviews can’t be made public. Nevertheless, the metadata is made available so search engines and visible can index the study.

• Discuss with your team the value (or not) of publishing and describing the study even though the actual data is not disclosed
• Discuss with your team if there is something in the study that would be worth making publicly available

Key Points

• Rich Metadata = Metadata + using FAIR Vocabularies (e.g. Dublin Core) + in an Interoperable format (e.g. JSON-LD)

• There are tools for creating Rich Metadata files. Researchers do not have to do it manually. For example: FAIR Metadata Wizard

Data Reusing

Overview

Teaching: 10 min
Exercises: 15 min

Questions

• 1 How to cite data when reusing a data source?

• 2 How do we make sure data will be reused?

Objectives

• The participant will understand the importance of data citation

• The participant will learn tools to test discoverability for data to be reused

FAIR principles used in Data Reusing:

Findable

FM-F1B (Identifier persistence) → doi.org/10.25504/FAIRsharing.TUq8Zj
FM-F4 (Indexed in a Searchable Resource) → doi.org/10.25504/FAIRsharing.Lcws1N

Reusable

FM-R1.2 (Detailed Provenance) → doi.org/10.25504/FAIRsharing.qcziIV

1. How to cite data when reusing a data source?

“Data Reusing” are activities for recycling existing research data sources.

By default, we should make the research data documentation in citable and reusable formats

The minimum citation elements recommended by DataCite are:

• Creator
• Publication Year
• Title
• Resource Type or Version
• Publisher
• Digital Object Identifier

For example:

Neff, Roni A.; L. Spiker, Marie; L. Truant, Patricia (2016): Wasted Food: U.S. Consumers’ Reported Awareness, Attitudes, and Behaviors. PLOS ONE. Dataset. https://doi.org/10.1371/journal.pone.0127881

Original dataset at Wasted Food: U.S. Consumers’ Reported Awareness, Attitudes, and Behaviors → LINK TO EXAMPLE

Nowadays, data repositories have a friendly interface on which one can export the data citation directly from the webpage. Such as ZENODO, Dataverse or FigShare.

Citing datasets increases publication visibility on 25%.

When we cite a dataset using a standard format (e.g. recommended DataCite), then we allow data aggregators to “pick up” on the citations just like publications. Therefore, they immediately become discoverable for scientific sources

2. How do we make sure data can be reused?

Remember that a dataset or a data source is a digital object. So Digital Objects “live” on the web. Imagine they are like fishes in the sea 🐠🐠. The way to get picked up by a “fisherman” 🎣 i.e. a search engine (e.g. Google), is by describing these Digital Objects with Rich Metadata (Episode 5). Moreover we can check the quality of discoverability of the Digital Objects by conducting automated tests.

There are different available applications to test the discoverability of datasets for future reuse.

For example:
In Google Rich Results search.google.com/test/rich-results, you can test whether a dataset is discoverable by other machines (e.g. Google bots)→ LINK TO EXAMPLE

After testing the URL of the data source, you will get the response from the Google bots. We aim to determine if the Google bots have found a structured dataset with the link we provided.

Following the FAIR principles, you ensure Rich Metadata

Rich metadata is necessary to be discoverable on the internet. Without testing it, your data can be virtually invisible on the web

Exercise - Level Easy 🌶

1. The following website is a database of crime news related to cultural objects
2. Go to news.culturecrime.org
3. Perform Google’s rich test on this data source
4. You can copy the URL of the browser and put it on the search.google.com/test/rich-results
   Question: Did the Google bots find the dataset?

Solution

Nope 🙁
They did not. This is a clear example that a data source can be human-friendly but not necessarily machine friendly

If you want to learn more about the Rich Results Test, you can find it here: support.google.com/webmasters/.

The FAIR principles rely in the idea of structured data following Semantic Web guidelines and formats. All search engines (e.g. Google) and data aggregators (e.g. Elsevier)work with the same standards making it easier to discover datasets. They can understand structured data in web pages about datasets using either schema.org, Dataset markup or equivalent structures represented in W3C’s Data Catalog Vocabulary (DCAT) format.

When you have a project website, or project folder accessible to third parties, you must include the Rich Metadata format in the root folder for the data source to be discoverable and therefore Reusable

For example:

<html>
<head>
    <title>This Dataset is a FAIR example</title>
    <script type="application/ld+json">
    {
    "@context":"https://schema.org/",
    "@type":"Dataset",
    "name":"Dummmy Data",
    "description":"This is an example for the Bootcamp",
    "url":"https://example.com",
    "identifier": ["https://doi.org/XXXXXX",
                    "https://identifiers.org/XXXX"],
    "keywords":[
        "DUMMY DATA",
        "EXAMPLE FORMAT",
    ],
    "license" : "https://creativecommons.org/publicdomain/zero/1.0/",
    "isAccessibleForFree" : true,
    "funder":{
        "@type": "Organization",
        "sameAs": "https://example.com",
        "name": "NATIONAL FUNDING"
        }
    }
    </script>
</head>
<body>
</body>
</html>

Exercise - Level Easy 🌶

1. Visit the dataset you uploaded in the Data Archiving episode
2. Download the Rich Metadata file (JSON-LD) format
   Question: What similarities do you find with the above example?

Solution

The format is JSON-LD: type=”application/ld+json”
The vocabulary is Schema.Org: “@context”:”https://schema.org/”
The type is a Dataset: “@type”:”Dataset”

Discussion

Secondary use of Qualitative Data?

The UK Data Service provides an overview on Approaches to reusing qualitative data such as Comparative research, Reanalysis, or Learning and teaching

• Discuss with your team how we can extend these categories for secondary use of Qualitative data

Key Points

• Digital Objects (e.g. Datasets) “live” in the web, imagine they are like fishes in the sea 🐠🐠. The way to get picked up by a “fisherman” 🎣 i.e. a search engine (e.g. Google) is by describing these Digital Objects with Rich Metadata