adding tools

_data/tool_and_resource_list.yml

@@ -383,7 +383,7 @@
   url: https://www.gsea-msigdb.org/gsea/index.jsp
 - description: The Genotype-Tissue Expression (GTEx) project is an ongoing effort to build a comprehensive public resource to study tissue-specific gene expression and regulation. Samples were collected from 53 non-diseased tissue sites across nearly 1000 individuals, primarily for molecular assays including WGS, WES, and RNA-Seq. Remaining samples are available from the GTEx Biobank. The GTEx Portal provides open access to data including gene expression, QTLs, and histology images.
   id: gtex
-  name: GTEx
+  name: Genotype-Tissue Expression (GTEx)
   registry:
     biotools: gtex
     fairsharing: 293c15
@@ -612,7 +612,7 @@
   url: https://streamflow.di.unito.it/
 - description: The Cancer Genome Atlas (TCGA) is a comprehensive, collaborative effort led by the National Institutes of Health (NIH) to map the genomic changes associated with specific types of tumors to improve the prevention, diagnosis and treatment of cancer. Its mission is to accelerate the understanding of the molecular basis of cancer through the application of genome analysis and characterization technologies.
   id: tcga
-  name: TCGA
+  name: The Cancer Genome Atlas (TCGA)
   registry:
     fairsharing: m8wewa
     tess: TCGA
@@ -976,7 +976,7 @@
   url: https://viral.ai/collections
 - description: EMBL-EBI's web portal for finding ontologies
   id: ols
-  name: Ontology Lookup Service
+  name: Ontology Lookup Service (OLS)
   registry:
     biotools: ols
     fairsharing: Mkl9RR

data-description/fair-data.md

@@ -53,12 +53,12 @@ Moreover, by making infectious disease data more findable, researchers promote t
 * Use (globally) unique and persistent [identifiers](https://rdmkit.elixir-europe.org/identifiers) (e.g. [biosample:SAMEA6864906](https://www.ebi.ac.uk/biosamples/samples/SAMEA6864906)) for each of your records, asserting they are unambiguously resolvable from anywhere in the world.
 * Use standard naming conventions for human and disease data (e.g. [Brill-Zinsser](https://www.ebi.ac.uk/ols/ontologies/efo/terms?short_form=EFO_0007182) disease), as well as for taxonomic classifications (e.g. [taxonomy:9606](https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode=Info&id=9606) for humans or [taxonomy:2697049](https://identifiers.org/taxonomy:2697049) for COVID-19). 
 * Describe your data with clear variable names with possible searchable keywords and comprehensive descriptions: choose field standards if possible. Prioritise primary and usual users' standards, but do not forget that metadata may be used by novel users to the field, to which you can cater with generic and understandable. Metadata must be sufficient and appropriate.
-* Register your data as open and accessible as possible through repositories and data portals (e.g. [EGA](https://ega-archive.org/), [dbGaP](https://www.ncbi.nlm.nih.gov/gap/), [Health Information Portal](https://www.healthinformationportal.eu/)…). 
+* Register your data as open and accessible as possible through repositories and data portals (e.g. {% tool "ega" %}, {% tool "dbgap" %}, {% tool "health-portal" %}…). 
 * Make your webpage machine accessible and readable, especially for search engines. You can always check the findability of the data you submitted (e.g. using a new session on a web browser), adjust and correct it if needed.
 
 ### Existing approaches
 
-* It is vital that the data you produce gets archived in a permanent archive that allows for controlled distribution, not just for the set of years your project is active. Some examples of human archives are the [EGA](https://ega-archive.org/) and [dbGaP](https://www.ncbi.nlm.nih.gov/gap/), also encompassed by other major frameworks like [BioStudies](https://www.ebi.ac.uk/biostudies/) or the [COVID-19 data portal](https://www.covid19dataportal.org/).
+* It is vital that the data you produce gets archived in a permanent archive that allows for controlled distribution, not just for the set of years your project is active. Some examples of human archives are the {% tool "ega" %} and {% tool "dbgap" %}, also encompassed by other major frameworks like {% tool "biostudies" %} or the {% tool "covid-19-data-portal" %}.
 * Take a look at other approaches at the [Finding metadata](/data-sources/human-clinical-and-health-data) section.
 
 
@@ -77,7 +77,7 @@ Accessibility in infectious diseases research is crucial to ensure quick, secure
 
 ### Existing approaches 
 
-* There are multiple archives with secure procedures already in place for the distribution of sensitive human information through authentication and granted access. For example, the EGA has a [request-and-grant](https://ega-archive.org/access/data-access) method to provide secure ad-hoc access to human datasets.
+* There are multiple archives with secure procedures already in place for the distribution of sensitive human information through authentication and granted access. For example, the {% tool "ega" %} has a request and grant method to provide secure ad-hoc access to human datasets.
 * Check other use-cases and examples at the [Data access]((/human-clinical-and-health-data/data-sources)) section.
 
 
@@ -99,7 +99,7 @@ Without interoperability, data silos may emerge, restricting researchers' abilit
 
 ### Existing approaches
 
-* For controlled vocabularies and ontologies you can use the Ontology Look-up Service ([OLS](https://www.ebi.ac.uk/ols/index)). This handy service compiles multiple ontologies through which you can search at once. 
+* For controlled vocabularies and ontologies you can use the {% tool "ols" %}. This handy service compiles multiple ontologies through which you can search at once. 
 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).
@@ -120,9 +120,9 @@ Infectious disease research heavily relies on the reusability of human clinical
 
 ### Existing approaches 
 
-* 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 ([DUO](https://github.com/EBISPOT/DUO#what-is-duo)) were created (search for yours at [OLS](https://www.ebi.ac.uk/ols/ontologies/duo)). These allow to annotate datasets with usage restrictions, enabling: 
+* 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 [Zenodo](https://zenodo.org/))
+    * 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! 

data-sources/human-biomolecular-data.md

@@ -71,11 +71,11 @@ Please note that these considerations are general in nature and may vary dependi
 ### Existing approaches
 
 - **Public databases:** Various publicly accessible databases serve as repositories for human biomolecular data, such as the {% tool "ncbi" %} databases (e.g., {% tool "genbank" %}, {% tool "geo" %}, {% tool "sra" %}) and European Bioinformatics Institute ({% tool "ebi" %}) databases (e.g., {% tool "european-nucleotide-archive" %}, {% tool "arrayexpress" %}).
-- **Controlled access repositories:** Some data deposition platforms, like dbGaP ({% tool "dbgap" %}) and EGA ({% tool "ega" %}), adopt a controlled access model to protect sensitive human biomolecular data. Researchers interested in accessing the data need to request permission and comply with specific data usage policies.
+- **Controlled access repositories:** Some data deposition platforms, like {% tool "dbgap" %} and {% tool "ega" %}, adopt a controlled access model to protect sensitive human biomolecular data. Researchers interested in accessing the data need to request permission and comply with specific data usage policies.
 - **Data integration platforms:** Platforms like the {% tool "ga4gh" %} provide frameworks and standards for federated data access and integration across multiple repositories. These initiatives aim to facilitate the aggregation and analysis of human biomolecular data from diverse sources while maintaining data privacy and security.
 - **Data citation and DOI assignment:** To acknowledge and promote the contributions of researchers who deposit human biomolecular data, many repositories assign unique digital object identifiers (DOIs) to datasets. This enables proper citation and recognition of the deposited data, enhancing its visibility and impact.
 - **Data submission portals:** Some repositories offer user-friendly web portals or submission systems that guide researchers through the process of depositing human biomolecular data. These portals often provide templates, validation checks, and step-by-step instructions to ensure the completeness and quality of the deposited data.
-- **Consortium-specific databases:** Collaborative research initiatives often establish dedicated databases for sharing and depositing human biomolecular data, such as The Cancer Genome Atlas ({% tool "tcga" %}) for cancer genomics data or the Genotype-Tissue Expression ({% tool "gtex" %}) project for gene expression data across different tissues.
+- **Consortium-specific databases:** Collaborative research initiatives often establish dedicated databases for sharing and depositing human biomolecular data, such as ({% tool "tcga" %}) for cancer genomics data or the ({% tool "gtex" %}) project for gene expression data across different tissues.
 - **Standardized data formats:** Commonly used data formats like FASTQ, BAM, and VCF facilitate data deposition and sharing by ensuring compatibility and interoperability between different analysis tools and databases.
 - **Data publication:** Journals and publishers increasingly require researchers to deposit their human biomolecular data in public repositories as a prerequisite for publication. This promotes data sharing, reproducibility, and transparency in scientific research.
 - **Data sharing platforms:** Online platforms like {% tool "figshare" %}, {% tool "zenodo" %}, and {% tool "dryad" %} provide researchers with the means to deposit and share their human biomolecular data, ensuring its long-term accessibility and enabling collaboration.