FAIR data page #374
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FAIR data page #374
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bedroesb
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Nov 26, 2024
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bedroesb
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@EvaGarciaAlvarez I don't have time to add the tools now, either I do it later, or if you want to start, always welcome! |
data-description/fair-data.md
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| ## Findability | ||
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| Findability is a crucial aspect of infectious diseases research, as it ensures that relevant data and resources can be easily located and accessed by researchers and other stakeholders. |
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Maybe easily discovered and located instead of located and accessed as this already talks about Accessibility.
data-description/fair-data.md
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| * Provide detailed metadata for infectious disease datasets, including the source, collection date, location, and any performed protocols (e.g. nasal swab being the method of isolation: [EFO:0010741](http://www.ebi.ac.uk/efo/EFO_0010741)). Even when the granularity of the (meta)data varies, you should always use descriptive fields with broadly understandable values. | ||
| * Use controlled vocabularies and ontologies to describe human data and infectious diseases (e.g. [EFO:0007182](http://www.ebi.ac.uk/efo/EFO_0007182) for Brill-Zinsser disease). Furthermore, do not forget contextual data that must meet intercommunity standards, for example: time, temperature, pressure, chemical components… | ||
| * Controlled vocabulary refers to a set of terms, standardised by the field community, used to describe and categorise concepts, ensuring consistency and accuracy in data organisation and retrieval. For example, when a disease (e.g. Alport syndrome) has multiple used names (e.g. Alport deafness-nephropathy), it is recommended to use the designated one in the ontologies, so the redundancy is kept to a minimum. |
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I think Alport syndrome is not an infectious disease. Then I'd either replace it by an example of a infectious disease or drop it.
data-description/fair-data.md
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| Examples of ontologies related to infectious diseases and human data and diseases are EFO (Experimental Factor Ontology), MONDO (Mondo Disease Ontology), HP (Human Phenotype Ontology), CIDO (Ontology of Coronavirus Infectious Disease), IDO (Infectious Disease Ontology), IDO-COVID-19 (The COVID-19 Infectious Disease Ontology), VIDO (The Virus Infectious Disease Ontology), DOID (Human Disease Ontology), the OBI (Ontology for Biomedical Investigations), and VO (Vaccine Ontology). | ||
| * It is possible to disseminate any recommendation on how to choose “good” ontologies, participating in the better understanding of well used and better recognized terminologies in related fields. To do it, some ideas can be found in: [Identifying, naming and interoperating data in a Phenotyping platform network : the good, the bad and the ugly.](https://doi.org/10.5281/zenodo.3539259) | ||
| * To aid with the taxonomy classification of your samples (human source, xenografts, tissue cultures, viral agents, etc.) you can make use of the [NCBI's taxonomybrowser](https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi). | ||
| * Please refer to RDA covid19 recommendation (and others) to help you to use most recognized terminologies adapted to your case: RDA COVID-19 Working Group. (2020). [RDA COVID-19 Recommendations and Guidelines on Data Sharing (1.0)](https://doi.org/10.15497/rda00052) |
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covid19 --> COVID-19
data-description/fair-data.md
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| * Redacting and interpreting data reuse policies is a complex and tedious task, especially when time is the main bottleneck of the research. For this reason, Data Use Conditions ({% tool "the-data-use-ontology" %}) were created (search for yours at {% tool "ols" %}). These allow to annotate datasets with usage restrictions, enabling: | ||
| * Automatic discovery of the data based on user authorization level or intended use. | ||
| * A quick and easy interpretation, from the perspective of the users, of the conditions to be met for data usage. (e.g. use very well and open licences like [Creative Commons](https://creativecommons.org/) and repositories that permit public licences and embargos like {% tool "zenodo" %}) |
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use very well known (?) and open
data-description/fair-data.md
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| * Automatic discovery of the data based on user authorization level or intended use. | ||
| * A quick and easy interpretation, from the perspective of the users, of the conditions to be met for data usage. (e.g. use very well and open licences like [Creative Commons](https://creativecommons.org/) and repositories that permit public licences and embargos like {% tool "zenodo" %}) | ||
| * Make these controls in an iterative way and publish your metadata! | ||
| * Keep track of data o reuses, and if publicly available, give a perspective of what was done with your dataset |
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data of reuses (?)
data-description/fair-data.md
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| * A quick and easy interpretation, from the perspective of the users, of the conditions to be met for data usage. (e.g. use very well and open licences like [Creative Commons](https://creativecommons.org/) and repositories that permit public licences and embargos like {% tool "zenodo" %}) | ||
| * Make these controls in an iterative way and publish your metadata! | ||
| * Keep track of data o reuses, and if publicly available, give a perspective of what was done with your dataset | ||
| * Make your dataset citable! |
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just an idea: Maybe it would be great to say how it can be made citable
Implemented Eva's review comments
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@EvaGarciaAlvarez, @hedi-ee has I think fixed everything you spotted, can you approve the PR when you think it is good to go? Then we also update the date in the news item |
EvaGarciaAlvarez
approved these changes
Dec 16, 2024
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