Minor fixes

_bibliography/papers.bib

@@ -21,6 +21,8 @@ @article{costaHIHISIVDatabaseGene2024
 	month = mar,
 	year = {2024},
 	pages = {125},
+    html = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-024-05740-7},
+    pdf = {https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-024-05740-7.pdf},
 }
 
 @article{zulfiqarImplementationFAIRPractices2024a,
@@ -41,11 +43,12 @@ @article{zulfiqarImplementationFAIRPractices2024a
 	month = feb,
 	year = {2024},
 	pages = {118},
+    html = {https://www.mdpi.com/2218-1989/14/2/118},
+    pdf = {https://www.mdpi.com/2218-1989/14/2/118/pdf?version=1708164641},
 }
 
 @inproceedings{krugerGHGAArchiveSelected2024a,
     abbr = {IWSG},
-    selected = {true},
 	address = {Toulouse, France},
 	title = {The {GHGA} {Archive}: {Selected} {Updates}},
 	copyright = {Creative Commons Attribution 4.0 International},
@@ -60,10 +63,11 @@ @inproceedings{krugerGHGAArchiveSelected2024a
 	year = {2024},
 	note = {Publisher: Zenodo},
 	keywords = {FAIR, human genome data, NFDI, omics, science gateway, sensitive data, FEGA},
+    html = {https://zenodo.org/doi/10.5281/zenodo.13863209},
+    pdf = {https://zenodo.org/records/13863209/files/IWSG2024_paper_5.pdf?download=1},
 }
 @article{zulfiqarMAWReproducibleMetabolome2023a,
     abbr = {J. Cheminf.},
-    selected = {true},
 	title = {{MAW}: the reproducible {Metabolome} {Annotation} {Workflow} for untargeted tandem mass spectrometry},
 	volume = {15},
 	copyright = {All rights reserved},
@@ -94,7 +98,8 @@ @article{zulfiqarMAWReproducibleMetabolome2023a
 	month = mar,
 	year = {2023},
 	pages = {32},
-	file = {Full Text:/Users/lgadelha/Zotero/storage/N4BBPKXX/Zulfiqar et al. - 2023 - MAW the reproducible Metabolome Annotation Workfl.pdf:application/pdf},
+    html = {https://jcheminf.biomedcentral.com/articles/10.1186/s13321-023-00695-y},
+    pdf = {https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-023-00695-y.pdf},
 }
 
 @article{gadelhaGermanHumanGenomePhenome2023,
@@ -112,6 +117,8 @@ @article{gadelhaGermanHumanGenomePhenome2023
 	author = {Gadelha, Luiz and Eufinger, Jan},
 	month = sep,
 	year = {2023},
+    html = {https://www.tib-op.org/ojs/index.php/CoRDI/article/view/394},
+    pdf = {https://www.tib-op.org/ojs/index.php/CoRDI/article/view/394/446},
 }
 
 @inproceedings{gadelhaFrameworkIntegrativeFAIR2022,
@@ -130,6 +137,8 @@ @inproceedings{gadelhaFrameworkIntegrativeFAIR2022
 	month = oct,
 	year = {2022},
 	pages = {443--449},
+    html = {https://ieeexplore.ieee.org/document/9973522/},
+    pdf = {https://arxiv.org/pdf/2207.06890},
 }
 
 @incollection{ocanaParslRNASeqEfficientScalable2022,
@@ -151,10 +160,11 @@ @incollection{ocanaParslRNASeqEfficientScalable2022
 	doi = {10.1007/978-3-031-23821-5_13},
 	note = {Series Title: Communications in Computer and Information Science},
 	pages = {174--189},
+    html = {https://link.springer.com/10.1007/978-3-031-23821-5_13},
 }
 
 @article{cruzParallelPerformanceProfiling2022,
-    abbr = {Computación y Sistemas},
+    abbr = {Comp. y Sis.},
 	title = {Parallel {Performance} and {I}/{O} {Profiling} of {HPC} {RNA}-{Seq} {Applications}},
 	volume = {26},
 	copyright = {All rights reserved},
@@ -167,6 +177,8 @@ @article{cruzParallelPerformanceProfiling2022
 	author = {Cruz, Lucas and Coelho, Micaella and Galheigo, Marcelo and Carneiro, Andre and Carvalho, Diego and Gadelha, Luiz and Boito, Francieli and Navaux, Philippe and Osthoff, Carla and Ocaña, Kary},
 	month = dec,
 	year = {2022},
+    html = {https://cys.cic.ipn.mx/ojs/index.php/CyS/article/view/4437},
+    pdf = {https://cys.cic.ipn.mx/ojs/index.php/CyS/article/view/4437/3471},
 }
 
 
@@ -241,7 +253,6 @@ @article{Siracusa2020
 }
 @article{Ocana2020,
 abbr = {FGCS},
-selected = {true},
 author = {Oca{\~{n}}a, Kary A.C.S. and Galheigo, Marcelo and Osthoff, Carla and Gadelha, Luiz and Porto, Fabio and Gomes, Ant{\^{o}}nio Tadeu A. and de Oliveira, Daniel and Vasconcelos, Ana Tereza},
 doi = {10.1016/j.future.2020.01.030},
 issn = {0167739X},
@@ -255,7 +266,6 @@ @article{Ocana2020
 }
 @incollection{Mondelli2019,
 abbr = {ER Workshops},
-selected = {true},
 author = {Mondelli, Maria Luiza and {Townsend Peterson}, A. and Gadelha, Luiz},
 booktitle = {Advances in Conceptual Modeling. ER 2019. Lecture Notes in Computer Science, vol. 11787},
 doi = {10.1007/978-3-030-34146-6_3},
@@ -359,7 +369,6 @@ @incollection{Sanchez-Tapia2018
 }
 @article{Costa2017,
 abbr = {PeerJ},
-selected = {true},
 abstract = {There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions, transcription factors and gene annotation. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managing the resulting data and its respective metadata are challenging tasks. Additionally, a great amount of effort is equally required to run in-silico experiments to structure and compose the information as needed for analysis. Different programs may need to be applied and different files are produced during the experiment cycle. In this context, the availability of a platform supporting experiment execution is paramount. We present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. It includes GeNNet-Wf, a scientific workflow that pre-loads biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and gene set enrichment analysis. A user-friendly web interface, GeNNet-Web, allows for setting parameters, executing, and visualizing the results of GeNNet-Wf executions. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment in different analysis scenarios. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results are integrated into GeNNet-DB, a database about genes, clusters, experiments and their properties and relationships. The resulting graph database is explored with queries that demonstrate the expressiveness of this data model for reasoning about gene interaction networks. GeNNet is the first platform to integrate the analytical process of transcriptome data with graph databases. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use. Developers can add new functionality to components of GeNNet. The derived data allows for testing previous hypotheses about an experiment and exploring new ones through the interactive graph database environment. It enables the analysis of different data on humans, rhesus, mice and rat coming from Affymetrix platforms. GeNNet is available as an open source platform at https://github.com/raquele/GeNNet and can be retrieved as a software container with the command docker pull quelopes/gennet.},
 author = {Costa, Raquel L. and Gadelha, Luiz and Ribeiro-Alves, Marcelo and Porto, F{\'{a}}bio},
 doi = {10.7717/peerj.3509},
@@ -424,7 +433,6 @@ @article{Meirelles2015a
 @incollection{Gadelha2014,
 author = {Gadelha, Luiz and Mattoso, Marta},
 abbr = {IPAW},
-selected = {true},
 booktitle = {Provenance and Annotation of Data and Processes. IPAW 2014. Lecture Notes in Computer Science, vol. 8628},
 doi = {10.1007/978-3-319-16462-5_11},
 isbn = {978-3-319-16461-8},