Title: How Research Data could be made FAIR?

Watch the following video carefully

F as in Findability

Credits: source https://libapps-au.s3-ap-southeast-2.amazonaws.com/accounts/205152/images/FAIR_2.png

Let's remember how Data Findability was presented in the video:

For data to be findable:

- Metadata and data are assigned a globally unique and persistent identifier.
- Data are described with rich metadata.
- Metadata clearly and explicitly include the identifier of the data it describes.
- Metadata and data are registered or indexed in a searchable resource.

How could these principles apply to the data produced during the TAPIR project?

How to make your data available?

Q: As a researcher, you investigated possible connections between tapir droppings and a virus potentially dangerous to human populations. You have collected a considerable amount of data, presented in tabular form, and published an article on the subject in a reputed journal. How are you going to make this data available?
- You deposit the data in a warehouse that gives you the possibility of obtaining a unique and perennial identifier. The link is: https://doi.org/10.10654_dat_zoo (correct) (Right answer. When you need to store data somewhere you should look for warehouses that are specific for datasets rather than other Cloud solutions. Warehouses (we more often use the word "repositories" designed to store data will allow you to describe your dataset more easily and follow standards that meet scholars' requirements in Data Findability)
- You trust the publisher to host the data and link to it from the magazine site. The link will be as follows: https://www.awsome_publishing.com/zoonose_review/2023/34/1/data/tapir.xls (This is not a very good idea. Generally publishers offer to host datasets together with (closely related) articles in the form of supplementary material, it is preferable to choose another third-party data directory for three reasons: - data directories give you more space to comment on the data - the publisher's hosting of your data may be detrimental to its openness or reusability - multinational scientific publishers may use the data in a way that is contrary to the spirit of Open Science)
- You deposit the data on one of your lab's servers, so that you retain control over it. The link is this:
https://tropical_labo.org/campaigns/2023/south_america/project_tapir/data (Why not, but there is a better option: use a discipline-centric or institutional repository. What will happen if you leave this laboratory and the people who remain lack space on the shared server? Do you think they will take some time to warn you (if you have changed your affiliation) that you should back up your data elsewhere because they may need to erase them in order to have enough space for future experiments? Once sent to a data repository, your data will be kept available for a long time and you will be able to track their reuse.)

What could you add to your dataset to make them more findable? (Multiple Choice)

Q: What could you add to your dataset to make them more findable?
- biographies of the researchers who collected them (No need of bibliographical records here! Linking a dataset to the researcher's profile on ORCID who collected it will be enough. To see the researcher's affialiation, you may use the researcher's ORCID (if she filled one). For instance Peter W Stalh (https://orcid.org/0000-0003-2267-1676) is the one who collected Tapirus bones in Ecuador (https://staging.opencontext.org/types/62800558-2bd8-4288-a31c-985c8c4d2798))
- keywords (correct) (Yes ! Keywords are the keys to findability! you should describe your data with two kinds of keywords: 1. words that you use to present your own data. 2 words chosen in a thesaurus which is standart in your discipline. For instance, medical subjets will be described by medical keywords selected from the MeSH thesaurus (this is the MeSH word for Tapir : https://www.ncbi.nlm.nih.gov/mesh/?term=tapir ) which you can use to find other medical studies involving tapirs) (if not chosen: Keywords are the keys to findability! you should describe your data with two kinds of keywords: 1. words that you use to present your own data. 2 words chosen in a thesaurus which is standart in your discipline. For instance, medical subjets will be described by medical keywords selected from the MeSH thesaurus (this is the MeSH word for Tapir : https://www.ncbi.nlm.nih.gov/mesh/?term=tapir ) which you can use to find other medical studies involving tapirs)
- The narration of your travel in the middle of the jungle. Associated with such a story, your data will become most cited in a short period of time. (Except for the details with which you describe how you collected the data (which your audience may find very boring, if they are not scientists), telling your adventures in the jungle are certainly of high literary value but do not convey much scientific interest.)

A as in Accessibility

Let's remember how Data Accessibility was presented in the video:

For data to be accessible,

- Metadata and data are retrievable by the identifier using a standardized
communications protocol.

- The protocol is open, free and universally implementable,

- and the protocol allows for an authentication and authorization procedure where necessary.

An authorization procedure is necessary when required data are sensitive, which means that they are linked to somebody's health, political opinions, union affiliation or activity, religion, or sexual identity.

How could these principles apply to the data produced during the TAPIR project?

Data communication

Q: You make sure that the first dataset (the one in which localizations of tapirs are registered) can be found, but how are you going to make it accessible to your fellow researchers around the world? What communication protocol will you propose to them?
- As a simple communication protocol as possible: they just will just have to click on a button on the description page of your dataset to download the data (correct) (Why should it be more complex for everybody to access your data? Are personal data at stake? intellectual property? Publication strategy? Your data are about tapirs, not people. They are not covered by intellectual property (while works are) and you have already published your papers based on these data. They may be communicated without restraint.)
- Make them downloadable through a FTPS client. (correct) (FTP is a venerable protocol, particularly suitable for situations where bandwidth is limited, such as when away from home or the lab. Adding to its appeal is its modern security layer, FTPS. Since FTP clients tailored to various operating systems are readily available, users can quickly adapt without extensive learning curves, especially considering that these tools now often come with graphical user interfaces. Hence, FTPS emerges as a compelling choice. Do not forget however to write instructions on how to use a FTP client to download the dataset.)
- In the data repository, from the page where the dataset is described, Create a form in which, the researchers who ask for the data will write their name, position, mail adress, research team, disclose their scientific project, and their funders. If you are satisfied with the information given, you will send the dataset by email. (Why so much complexity? No personal data are at stake, neither intellectual property issue; your paper based on these data have already been published, you do not have to fear somebody else reusing these data to prevent you from publishing once more. Besides, the dataset may be too large to be sent by email. Keep the communication protocol as simple as possible.)

Patients Data

Q: Doctors working with rural populations in Bolivia and Colombia who live close to tapirs have noticed that they are more prone to fevers than other populations living in a comparable ecosystem but where tapirs are less numerous. The local people took part in further experiments and sampling. The age, sex, locality and profession of the participants were collected and form part of the dataset. What can we make of it?
- Process questions in such a way that no single record is unique, and share the dataset (correct) (This is a method for processing data to anonymize it. Often, simply deleting identifying attributes such as names, addresses, or social security numbers, or replacing this information with random identifiers, will not sufficiently prevent potential re-identification. For instance, if a dataset reveals that in a specific locality, a bus driver contracted a chronic illness, and there are no other bus drivers residing in that area, the combination of profession and city name alone could be enough to re-identify them. Similarly, cross-referencing datasets can yield the same outcome. Anonymizing data to ensure that the dataset contains no records with unique combinations of attributes enables safe data sharing without significant risks to the population under study.)
- Remove the columns in which direct identifiers such as family names and first names are provided and share the dataset from a repository (This is a method for processing data to pseudonomyze it. Often, pseudonomyzing data versus simply deleting identifying attributes such as names, addresses, or social security numbers, or replacing this information with random identifiers, will not sufficiently prevent potential re-identification. For instance, if a dataset reveals that in a specific locality, a bus driver contracted a chronic illness, and there are no other bus drivers residing in that area, the combination of profession and city name alone could be enough to re-identify them. Similarly, cross-referencing datasets can yield the same outcome. Anonymizing data to ensure that the dataset contains no records with unique combinations of attributes enables safe data sharing without significant risks to the population under study. Let's assume you are a researcher from Europe. Your study is covered by European legislation on Personal Data which are sumed up in the General Data Protection Regulation (GDPR). According to these regulations, you cannot simply expose pseudonomyzed data on a server to everybody. Since Health data are sensitive personal data, you should ask for every patient's consent and then moderate the access to the data and restrict to the researchers who work on the same field.)
- Share the datasets to a wide audience from a university server after having replaced family names and first names by a single random number. Create a correspondance file to keep record of which number corresponds to which person and keep this file in a secret place in the server. (Would these data only have been "personal", this way of sharing data would have been appropriated, but in our case, the data as considered as "sensitive". In this case, you need to carefully share your data in a secure data wharehouse and give access only to motivated requests. By motivated, we mean that the scientific investigator must be transparent in their request about how they intend to manage the data once they have downloaded it.)

I as in Interoperability

Let's remember how Data Accessibility was presented in the video:

The data usually need to be integrated with other data.

In addition, the data need to interoperate with applications or workflows for analysis, storage and processing.

For data to be interoperable, metadata and data use a formal, accessible, shared and broadly applicable language for knowledge representation.

Metadata and data use vocabularies that follow FAIR principles.

Metadata and data include qualified references to other metadata and data.

The ultimate goal of FAIR is to optimize the reuse of data.

To achieve this, metadata and data should be well described so that they can be replicated and or combined in different settings.

How these principles could apply to the data produced during the TAPIR project?

Vocabularies

Q: Researchers have studied tapir populations in the forests of Bolivia. Several species seem to live in the same area. Researchers want to make their dataset interoperable with other datasets collected from tapir populations in the same region.
What vocabulary can they use to do this?
- Wikidata elements (For instance Q215997 is Baird's tapir)
Wikidata elements are easy to find and to access on the web. This ontology can easily be reused through dataset analysis tools (in order to enrich those datasets with persistent identifiers). This language is pivotal with other descriptive languages. (correct) (Good answer. Wikidata can be used as pivots between different ontologies. For instance, Middle East respiratory syndrome (Q16654806) can be linked to MeSH MERS, or MER syndrome in COVID-19 thesaurus.)
- The classification developed by the expedition leader's grandfather, but unfortunately not accepted by the Academy of Sciences. For instance some tapirs are white and black and are described in this work as "tapirus piebald" (This taxonomy may have been followed by a majority of fellow citizens of the expedition leader's grandfather but today the main science academies no longer consider it relevant (because science keeps on integrating new species or sub-species every decade). Therefore, you would be better using taxonomies that have more authority for current scientists, such as wikidata elements.)
- Local language words for the different types of tapir found in the region. For instance they call Local black and white tapirs "yana yuraq". (To gain visibility, indigenous knowledge should employ identifiers that adhere to standards recognized by the worldwide scientific community. Utilizing scientific taxonomies is preferable to categorizing tapirs based solely on the names given to them by indigenous peoples. Furthermore, certain aspects of indigenous heritage, such as dances, medical practices, songs, and ceremonies, lack standardized identifiers. To address this gap, some scientific communities are endeavoring to develop ontologies capable of accommodating these diverse cultural artifacts. For example, the Africa PID Alliance is dedicated to advancing African indigenous knowledge.)

How to deal with Personal Data

Q: In order to better track and represent human habitats, fever-infected areas and tapir movements, a computer scientist on the research team has developed an extension to the geographic information system used in her laboratory. This code gives a very convincing visualization of the data. What should the computer scientist do with this code?
- From the forge where the computer scientist created the code, she should send it to a digital archive specialized in archiving code, and link this archived code to the dataset via a link (correct) (Great Job! In order to interoperate with a lot of other datasets, your code should be made available as long as possible. Archiving it in a dedicated archive, such as Software Heritage Foundation's is the best way to achieve it. Do not forget that software Forges sometimes are closed by their owners (remember How Google Code ended).)
- She should move the directory in which she developed this code from public to private: who knows, maybe she'll manage to sell this package to the publisher of the geographic information system, which will surely never happen if everyone can access her work freely. (This is not how Open Science deals with it. Open Science relies on Open Standards, easily accessible for scholars and individuals. She may put a dual licence on her code in order to allow scientists to freely use it, but if corporations want to reuse it in a proprietary software, then they will have to pay for it. She should consider the fact that if she is employed by a public service, she must comply with the following rule: her code should be attributed to her, but the decision of the licence attribute to this code is not only her business but also her employer's decision.)
- Ok, the code works with this data, but it's really been done by hand, and not at all professionally (didn't have the time anyway). Better not to share this code, which she's not too sure about, and which her colleagues might find too poorly done. (She is not an IT engineer, which could be noted somewhere in the description file attached to the source code. Other scholars won't assess her code as poetry, but rather for its functionality with the provided dataset or their own. Any piece of code that processes a dataset, especially if the dataset is large and crucial for publication, regardless of its size, is important to share for verification and replicability)

R as in reusability

For data to be reusable:

- Metadata and data are richly described with a plurality of accurate and
relevant attributes.

- Metadata and data are released with a clear and accessible data usage license and they are associated with detailed provenance.

- Lastly, metadata and data meet domain relevant community standards.

How these principles could apply to the data produced during the TAPIR project?

Licensing

Q: The researchers have chosen to share their tapir and tapir fever patient datasets with researchers around the world. However, they do not want this work to be monopolized by actors who might misuse it. What kind of license can they put on this data?
- A CC0 license to allow the widest possible reuse of the dataset while guaranteeing that the dataset will not be monopolized by a single player (correct) (CC0 is great: it allows the maximum reuse (commercial included) but prevents any actor from closely accessing the data. However, you may prefer to be credited any time your data are reused, or your local legislation may require your name as the person responsible for the data. In that case, consider using CC-by instead of CC0.)
- Well, you are lucky, because, in your laboratory, you know that there is a team of players who will write the perfect licence that fits all possible major cases in your field for you. After having read this two page licence, anybody will be able to know if they are entitled to reuse your data for their intended purpose or not. (No, don't let them do that. Open Science relay on licencing standards. If your licence is not chosen among the few that are widely used around the world (Open Database Licence, CC0, CC:by), other researchers will have hard times trying to determine wether the allowed reuses are equivalent to the way they intend to process your data. Be simple ; choose a licence that is recognized by the majority of scientists.)
- No license at all: any license would restrict the reusability of the data, so it's best not to license datasets at all. (Not having a license is not an option; nobody will take the risk of using your data if doing so could later subject them to legal actions for reusing your data without explicit permission. Not having a license means that the researcher or their employer retains full ownership of the data and must be asked for permission for any reuse.)

Open Standards and Formats

Q: The researchers placed cameras with sensors to film the tapirs as they passed by. The software equipping the cameras allows the videos to be broadcast in a variety of formats. Which do you think should be chosen?
- The MP4 format, which is widely used and standardized. Take this option rather than the default one, even if the conversion could take some time... (correct) (You're right. Though it is a proprietary software, a MP4 file may be read on every operating system through open source software such as VLC. For that reason, you should stick with this format.)
- The camera is an Apple device, and Mov is the default output format. Apple devices are standard in the field of research, so let's stick to this option (Not a very inclusive way of practicing open science. How Windows Or Linux users will deal with it? buying a new computer? they would rather look for another dataset as relevant as yours but more open.)
- A format is available that allows people to read the files whether on a Mac or on a Microsoft operating system. Mac and Microsoft users working together! A beautiful symbol isn't it? (GNU\Linux users won't give a damn. They use transparent and open operating systems. Transparency is important for OpenScience. Most of the code produced by researchers are thought to work on that kind of OS for reproducibility matters. You should never share data or files in proprietary formats, except if they can also be read on every existing OS (PDF, MP4). Instead, consider installing a GNU/linux distribution on your Mac or Windows computer.)

FAIR in a few words

As you may have noticed while making decisions regarding our fictive tapir cases, Open Science suggests that data and source code should be made as open as possible, and no more closed than necessary.

FAIR principles are essential to master in order to achieve this.

Sometimes, intellectual property rights (for example, when you have an agreement with a corporation to reuse its data) or personal data protection necessitate keeping data behind gates. In such cases, you are the one who decides who can pass through these gates based on their capacity to handle the data responsibly.

It is also crucial to provide rich metadata when describing your data. Who collected the data? Which research project does this dataset belong to? What other datasets or publications are linked to it? Utilizing specific vocabularies such as ontologies makes your data more understandable from the perspective of other researchers.

From the moment you produce the data, you should consider how to make them reusable, either by your team later or by other researchers who may come along, sometimes several years after your project has ended. This principle also applies to the source code accompanying the data.

Describe the entirety of your project, as description serves as the "glue that binds its pieces together and ensures its longevity."

These changes help to improve readability and clarity.