Project Deliverables & Publications

The following scientific publications are at least partially funded via EOSC-Life, Grant Agreement No. 824087:

• Sansone, S.; McQuilton, P.; Rocca-Serra, P. et al. (2019). FAIRsharing as a community approach to standards, repositories and policies. Nature Biotechnology 37, 358. https://doi.org/10.1038/s41587-019-0080-8
• Föll, M.; Moritz, L.; Wollmann, T. et al. (2019). Accessible and reproducible mass spectrometry imaging data analysis in Galaxy.  GigaScience 8, 1. https://doi.org/10.1093/gigascience/giz143.
• Wilkinson, M.; Dumontier, M.; Sansone, S. et al. (2019). Evaluating FAIR maturity through a scalable, automated, community-governed framework. Scientific Data 6, 174. https://doi.org/10.1038/s41597-019-0184-5
• Bray, S.; Lucas, X.; Kumar, A. et al. (2020). The ChemicalToolbox: reproducible, user-friendly cheminformatics analysis on the Galaxy platform. Journal of Cheminformatics 12, 40. https://doi.org/10.1186/s13321-020-00442-7. The ChemicalToolbox is used within EOSC-Life’s Demonstrator 1 (Galaxy team).
• Vilas, J.; Tagare, H.; Vargas, J. et al. (2020). Measuring local-directional resolution and local anisotropy in cryo-EM maps. Nature Communications 11, 55.  https://doi.org/10.1038/s41467-019-13742-w
• Rocca-Serra, P.; Sansone, S. (2020). Experiment design driven FAIRification of omics data matrices, an exemplar. Scientific Data 6, 271. https://doi.org/10.1038/s41597-019-0286-0
• Abudukelimu, A.; Barberis, M.; Redegeld, F. et al. (2020). Complex stability and an irrevertible transition reverted by peptide and fibroblasts in a dynamic model of innate immunity. Frontiers in Immunology 10. https://doi.org/10.3389/fimmu.2019.03091
• Goble, C.; Cohen-Boulakia, S.; Soiland-Reyes, S. et al. (2020). FAIR computational workflows. Data Intelligence, 2(1-2), 108.  https://doi.org/10.1162/dint_a_00033
• David, R.; Mabile, L.; Specht, A. et al. (2020). FAIRness literacy: The Achilles’ heel of applying FAIR principles. Data Science Journal, 19(32), 1-11. http://doi.org/10.5334/dsj-2020-032
• Střelák, D.; Filipovič, J.; Jiménez-Moreno, A. et al. (2020). FlexAlign: An accurate and fast algorithm for movie alignment in cryo-electron microscopy. Electronics, 9 (6), 1040.  https://doi.org/10.3390/electronics9061040
• Melero, R.; Sorzano, C.; Foster, B. et al. (2020). Continuous flexibility analysis of SARS-CoV-2 spike prefusion structures. International Union of Crystallography Journal, 7(6), 1059. https://doi.org/10.1107/S2052252520012725
• Caracciolo, C.; Aubin, S.; Jonquet, C. et al. (2020). 39 Hints to facilitate the use of semantics for data on agriculture and nutrition. Data Science Journal 19(47), 1. http://doi.org/10.5334/dsj-2020-047
• Jacob, D.; David, R.; Aubin, S. et al. (2020). Making experimental data tables in the life sciences more FAIR: a pragmatic approach. GigaScience 9, 1. https://doi.org/10.1093/gigascience/giaa144
• Obst, M.; Exter, K.; Allcock, A. et al. (2020). A marine biodiversity observation network for genetic monitoring of hard-bottom communities (ARMS-MBON). Frontiers in Marine Science, Frontiers Media 7. https://doi.org/10.3389/fmars.2020.572680
• Vilas, J.; Oton, J.; Messaoudi, C. et al. (2020). Measurement of local resolution in electron tomography. Journal of Structural Biology 4, 100016. https://doi.org/10.1016/j.yjsbx.2019.100016
• Sorzano, C.; de Isidro-Gómez, F.; Fernández-Giménez, E. et al. (2020). Improvements on marker-free images alignment for electron tomography. Journal of Structural Biology 4, 100037. https://doi.org/10.1016/j.yjsbx.2020.100037
• Tanoli, Z.; Seemab, U.; Scherer, A. et al. (2020). Exploration of databases and methods supporting drug repurposing: a comprehensive survey. Briefings in Bioinformatics 22(2), 1656. https://doi.org/10.1093/bib/bbaa003
• Tangaro, M.; Mandreoli, P.; Chiara, M. et al. (2021). Laniakea@ReCaS: exploring the potential of customisable Galaxy on-demand instances as a cloud-based service. BMC Bioinformatics 22, 544. https://doi.org/10.1186/s12859-01-3
• Sanchez-Garcia, R.; Gomez-Blanco, J.; Cuervo, A. et al. (2021). DeepEMhancer: a deep learning solution for cryo-EM volume post-processing. Communications Biology 4, 874. https://doi.org/10.1038/s42003-021-02399-1
• Pickering B.; Biro, T.; Austin, C. et al. (2021). Radical collaboration during a global health emergency: development of the RDA COVID-19 data sharing recommendations and guidelines. Open Research Europe 1, 69. https://doi.org/10.12688/openreseurope.13369.1
• Holzinger, A.; Müller, H. (2021). Toward human–AI interfaces to support explainability and causability in medical AI. Computer 54(10), 78. https://doi.org/10.1109/MC.2021.3092610
• Fernández-Giménez, E.; Martínez, M.; Sánchez-García, R. et al. (2021). Cryo-EM density maps adjustment for subtraction, consensus and sharpening. Journal of Structural Biology 213 (4), 107780. https://doi.org/10.1016/j.jsb.2021.107780
• Laveglia, V.; Giachetti, A.; Cerofolini, L. et al. (2021). Automated determination of nuclear magnetic resonance chemical shift perturbations in ligand screening experiments: The PICASSO web server. Journal of Chemical Information and Modeling 61(12), 5726. https://doi.org/10.1021/acs.jcim.1c00871
• Lamprecht, A.; Palmblad, M.; Ison, J. et al. (2021). Perspectives on automated composition of workflows in the life sciences. F1000Research, 10, 897. https://doi.org/10.12688/f1000research.54159.1
• Strelak, D.; Jiménez-Moreno, A.; José, L. et al. (2021). Advances in Xmipp for cryo–electron microscopy: from Xmipp to Scipion. Molecules 26(20), 6224. https://doi.org/10.3390/molecules26206224
• Machwitz, M.; Pieruschka, R.; Berger, K. et al. (2021). Bridging the gap between remote sensing and plant phenotyping—challenges and opportunities for the next generation of sustainable agriculture. Frontiers in Plant Science, 12, 749374. https://doi.org/10.3389/fpls.2021.749374
• Macias, J.; Sanchez-Garcia, R.; Conesa, P. et al. (2021). 3DBionotes COVID-19 edition. Bioinformatics 37(22), 4258. https://doi.org/10.1093/bioinformatics/btab397
• Song, F.; Ali Khan,A.; Matteoni, R. et al. (9-12 June 2021). INFRAFRONTIER Supports Targeting Cancer with Animal Models. European Association for Cancer Research (EACR) 2021 Virtual Congress. https://publications.cnr.it/doc/456948
• Rizzo, D.; Cerofolini, L.; Pérez-Ràfols, A. et al. (2021). Evaluation of the Higher Order Structure of Biotherapeutics Embedded in Hydrogels for Bioprinting and Drug Release. Analytical Chemistry, 9(32): 11208–11214.  https://doi.org/10.1021/acs.analchem.1c01850
• Hruska, M.; Holub, D. (2022). A complete search of combinatorial peptide library greatly benefited from probabilistic incorporation of prior knowledge. International Journal of Mass Spectrometry 471, 116723. https://doi.org/10.1016/j.ijms.2021.116723
• Machicao, J.; Specht, A.; Vellenich, D. et al. (2022). A deep-learning method for the prediction of socio-economic indicators from street-view imagery using a case study from Brazil. Data Science Journal 21(6), 1. https://doi.org/10.5334/dsj-2022-006
• Zullino, S.; Paglialonga, A.; Dastrù, W. et al. (2022). XNAT-PIC: extending XNAT to preclinical imaging centers. Journal of Digital Imaging. https://doi.org/10.1007/s10278-022-00612-z
• Eiteneuer, C.; Velasco, D.; Atemia, J. et al. (2022). GXP: analyze and plot plant omics data in web browsers. Plants 11(6), 745. https://doi.org/10.3390/plants11060745
• Brack, P.; Crowther, P.; Soiland-Reyes, S. et al. (2022). Ten simple rules for making a software tool workflow-ready. PLOS Computational Biology 18(3), e1009823. https://doi.org/10.1371/journal.pcbi.1009823
• Alharbi, E.; Gadiya, Y.; Henderson, D. et al. (2022). Selection of data sets for FAIRification in drug discovery and development: Which, why, and how? Drug Discovery Today 27(8), 2080. https://doi.org/10.1016/j.drudis.2022.05.010
• Crusoe, M.; Abeln, S.; Iosup, A. et al. (2022). Methods included: Standardizing computational reuse and portability with the common workflow language. Communications of the ACM 65(6), 54. https://doi.org/10.1145/3486897
• Ohmann, C.; David, R.; Cano Abadia, M. et al. (2022). Pilot study on the intercalibration of a categorisation system for FAIRer digital objects related to sensitive data in the life sciences. Data Intelligence 4(2), 196. https://doi.org/10.1162/dint_a_00126
• Soiland-Reyes, S.; Sefton, P.; Crosas, M. et al. (2022). Packaging research artefacts with RO-Crate. Data Science, 1, 1. https://doi.org/10.3233/DS-210053
• Soiland-Reyes S.; Bayarri G.; Andrio P. et al. (2022). Making Canonical Workflow Building Blocks Interoperable across Workflow Languages. Data Intelligence, 4(2), 342. https://doi.org/10.1162/dint_a_00135
• Zaliani, A.; Vangeel, L.; Reinshagen, J. et al. (2022). Cytopathic SARS-CoV-2 screening on VERO-E6 cells in a large-scale repurposing effort. Scientific Data, 9, 405. https://doi.org/10.1038/s41597-022-01532-x 
• Laveglia, V.; Giachetti, A.; Sala, D. et al. (2022). Learning to identify physiological and adventitious metal-binding sites in the three-dimensional structures of proteins by following the hints of a deep neural network. Journal of Chemical Information and Modeling, 62(12), 2951. https://doi.org/10.1021/acs.jcim.2c00522
• Massimi, M.; Di Pietro, C.; La Sala, G. et al. (2022). Mouse mutants of Gpr37 and Gpr37l1 receptor genes: disease modeling applications. International Journal of Molecular Sciences, 23(8), 4288. https://doi.org/10.3390/ijms23084288
• Birling, M.; Fray, M.; Kasparek, P. et al. (2022). Importing genetically altered animals: ensuring quality. Mammalian Genome, 33, 100. https://doi.org/10.1007/s00335-021-09908-x
• Hruska, M.; Holub, D. (2022). Evaluation of an integrative Bayesian peptide detection approach on a combinatorial peptide library. European Journal of Mass Spectrometry, 27(6), 217.
• Muller, C.; Serrano-Solano, B.; Sun, Y. et al. (2022). The Image Data Explorer: Interactive Exploration of Image-derived Data. PLoS ONE, 17(9): e0273698. https://doi.org/10.1371/journal.pone.0273698
• Boiten, J.-W.; Ohmann, C.; Adeniran, A. et al. (2022). EOSC-LIFE WP4 TOOLBOX: Toolbox for sharing of sensitive data — a concept description. arXiv, July 2022. https://arxiv.org/abs/2207.14296
• Karoune, E. Assessing Open Science Practices in Phytolith Research. (2022). Open Quaternary, 8(1), 3. http://doi.org/10.5334/oq.88
• Wittner, R.; Mascia, C.; Gallo, M. et al. (2022). Lightweight Distributed Provenance Model for Complex Real–world Environments. Scientific Data 9, 503. https://doi.org/10.1038/s41597-022-01537-6
• Laveglia, V.; Giachetti, A.; Sala, D. et al. (2022). Learning to Identify Physiological and Adventitious Metal-Binding Sites in the Three-Dimensional Structures of Proteins by Following the Hints of a Deep Neural Network. Journal of Chemical Information and Modeling, 62 (12), 2951–2960. https://doi.org/10.1021/acs.jcim.2c00522
• Brack, P.; Crowther, P.; Soiland-Reyes, S. et al. (2022). Ten simple rules for making a software tool workflow-ready. PLoS Computational Biology, 18(3), e1009823. https://doi.org/10.1371/journal.pcbi.1009823
• Frexia, F.; Mascia, C.; Wittner, R. et al. (2022). The Common Provenance Model: Capturing Distributed Provenance in Life Sciences Processes. In Challenges of Trustable AI and Added-Value on Health (pp. 415-416). IOS Press.
• Holzinger, A.; Kargl, M.; Kipperer, B. et al. (2022). Personas for Artificial Intelligence (AI) An Open Source Toolbox. IEEE Access, 10, 23732-23747. https://doi.org/10.1109/ACCESS.2022.3154776
• Soiland-Reyes, S.; Sefton, P.; Castro, L. et al. (2022). Creating lightweight FAIR Digital Objects with RO-Crate. Research Ideas and Outcomes, 8, e93937. https://riojournal.com/article/93937/download/pdf/
• Madeira, F.; Pearce, M.; Tivey, A. et al. (2022). Search and sequence analysis tools services from EMBL-EBI in 2022. Nucleic acids research, 50(W1), W276-W279. https://doi.org/10.1093/nar/gkac240
• Serrano-Solano, B.; Fouilloux, A.; Eguinoa, I. et al. (2022). Galaxy: A decade of realising CWFR concepts. Data Intelligence, 4(2), 358-371. https://doi.org/10.1162/dint_a_00136
• Janni, M.; Pieruschka, R. (2022). Plant phenotyping for a sustainable future. Journal of Experimental Botany, 73(15), 5085-5088. https://doi.org/10.1093/jxb/erac286
• Hruska, M. (2022). Identification of variant peptides using mass spectrometry. PhD dissertation (Department of Computer Science, Faculty of Science, Palacky University Olomouc). https://theses.cz/id/1glpu2/Thesis.pdf
• Eva, G.; Liese, G.; Stephanie, B. et al. (2022). Position paper on management of personal data in environment and health research in Europe. Environment International, 165, 107334. https://doi.org/10.1016/j.envint.2022.107334
• Grifagni, D.; Silva, J.; Cantini, F. et al. (2022). Relaxation-Based NMR Assignment: Spotlights on Ligand Binding Sites in Paramagnetic Proteins. Available at SSRN 4206809. http://dx.doi.org/10.2139/ssrn.4206809
• Müller, H.; Holzinger, A.; Plass, M. et al. (2022). Explainability and causability for artificial intelligence-supported medical image analysis in the context of the European in vitro diagnostic regulation. New Biotechnology, 70, 67-72. https://doi.org/10.1016/j.nbt.2022.05.002
• Soiland-Reyes, S.; Castro, L.; Garijo, D. et al. (2022). Updating linked data practices for FAIR digital object principles. Research Ideas and Outcomes, 8, e94501. https://riojournal.com/article/94501/download/pdf/
• Hardisty, A.; Brack, P.; Goble, C. et al. (2022). The specimen data refinery: a canonical workflow framework and FAIR digital object approach to speeding up digital mobilisation of natural history collections. Data Intelligence, 4(2), 320-341. https://doi.org/10.1162/dint_a_00134
• Weise, M.; Kovacevic, F.; Popper, N. et al. (2022). OSSDIP: Open Source Secure Data Infrastructure and Processes Supporting Data Visiting. Data Science Journal, 21(1). OSSDIP: Open Source Secure Data Infrastructure and Processes Supporting Data Visiting (codata.org)
• Batista, D.: Gonzalez-Beltran, A.: Sansone, S. et al. (2022). Machine actionable metadata models. Scientific Data 9, 592. https://doi.org/10.1038/s41597-022-01707-6
• Matentzoglu, N.; Balhoff, J.; Bello, S. et al. (2022, 25 May). A Simple Standard for Sharing Ontological Mappings (SSSOM). Database, 2022. https://doi.org/10.1093/database/baac035
• Tanoli, Z.; Aldahdooh, J.; Alam, F. et al. (2022). Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments. Briefings in bioinformatics, 23(1), bbab350. https://doi.org/10.1093/bib/bbab350
• Zheng, S.; Wang, W.; Aldahdooh, J. et al. (2022). SynergyFinder plus: toward better interpretation and annotation of drug combination screening datasets. Genomics, Proteomics & Bioinformatics. https://doi.org/10.1016/j.gpb.2022.01.004
• Machicao, J.; Ben Abbes, A.; Meneguzzi, L. et al. (2022). Mitigation strategies to improve reproducibility of poverty estimations from remote sensing images using Deep Learning. Earth and Space Science, 9(8), e2022EA002379. https://doi.org/10.1029/2022EA002379
• Müller, H.; Kargl, M.; Plass, M. et al. (2022). Towards a taxonomy for explainable AI in computational pathology. In: Chen, F., Zhou, J. (eds) Humanity Driven AI. Springer, Cham. https://doi.org/10.1007/978-3-030-72188-6_15
• Del Rio, M.; Lianas, L.; Aspegren, O. et al. (2022). AI support for accelerating histopathological slide examinations of prostate cancer in clinical studies. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13373. Springer, Cham. https://doi.org/10.1007/978-3-031-13321-3_48
• Douglass Jr, E.; Allaway, R.; Szalai, B. et al. (2022). A community challenge for a pancancer drug mechanism of action inference from perturbational profile data. Cell Reports Medicine, 3(1), 100492. https://doi.org/10.1016/j.xcrm.2021.100492
• Rizzo, D.; Cerofolini, L.; Giuntini, S. et al. (2022). Epitope mapping and binding assessment by solid-state NMR provide a way for the development of biologics under the quality by design paradigm. Journal of the American Chemical Society, 144(22), 10006–10016. https://doi.org/10.1021/jacs.2c03232
• Ciani, C.; Pérez-Ràfols, A.; Bonomo, I. et al. (2022). Identification and characterization of an RRM-containing, RNA binding protein in Acinetobacter baumannii. Biomolecules, 12(7), 922. https://doi.org/10.3390/biom12070922
• Sanchez-Garcia, R.; Macias, J.; Sorzano, C. et al. (2022). BIPSPI+: Mining Type-Specific Datasets of Protein Complexes to Improve Protein Binding Site Prediction. Journal of Molecular Biology, 434(11), 167556. https://doi.org/10.1016/j.jmb.2022.167556
• Sorzano, C.; Vilas, J.; Ramirez-Aportela, E. et al. (2022). Image processing tools for the validation of CryoEM maps. Faraday Discussions. https://doi.org/10.1039/D2FD00059H
• Bray, S.; Dudgeon, T.; Skyner, R. et al. (2022). Galaxy workflows for fragment-based virtual screening: a case study on the SARS-CoV-2 main protease. Journal of Cheminformatics, 14(22). https://doi.org/10.1186/s13321-022-00588-6
• Aldahdooh, J.; Vähä-Koskela, M.; Tang, J. et al. (2022). Using BERT to identify drug-target interactions from whole PubMed. BMC bioinformatics, 23(1), 1-13. https://doi.org/10.1186/s12859-022-04768-x
• de la Morena, J.; Conesa, P.; Fonseca, Y. et al. (2022). ScipionTomo: Towards cryo-electron tomography software integration, reproducibility, and validation. Journal of Structural Biology, 107872. https://doi.org/10.1016/j.jsb.2022.107872
• Sorzano, C.; Jiménez-Moreno, A.; Maluenda, D. et al. (2022). On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy. Acta Crystallographica Section D: Structural Biology, 78(4), 410-423. https://doi.org/10.1107/S2059798322001978
• Plass, M.; Kargl, M.; Evans, T. et al. (2023). Human-AI interfaces are a central component of trustworthy AI. In: Mehta, M., Palade, V., Chatterjee, I. (eds) Explainable AI: Foundations, Methodologies and Applications. Intelligent Systems Reference Library, vol 232. Springer, Cham. https://doi.org/10.1007/978-3-031-12807-3_11
• De Geest, P.; Coppens, F.; Soiland-Reyes, S. et al. (2022). Enhancing RDM in Galaxy by integrating RO-Crate. Research Ideas and Outcomes, 8, e95164. https://riojournal.com/article/95164/download/pdf/
• Massai, L.; Grifagni, D.; De Santis, A. et al. (2022). Gold-based metal drugs as inhibitors of coronavirus proteins: The inhibition of SARS-CoV-2 main protease by auranofin and its analogs. Biomolecules, 12, 1675. https://doi.org/10.3390/biom12111675
• Petek, M.; Zagorščak, M.; Blejec, A. et al. (2022). pISA-tree – a data management framework for life science research projects using a standardised directory tree. Scientific Data 9, 685. https://doi.org/10.1038/s41597-022-01805-5
• Burgin, J.; Ahamed, A.; Cummins, C. et al. (2022). The European Nucleotide Archive in 2022. Nucleic Acids Research, gkac1051, https://doi.org/10.1093/nar/gkac1051
• David, R.; Ohmann, C.; Boiten, JW. et al. (2022) An iterative and interdisciplinary categorisation process towards FAIRer digital resources for sensitive life-sciences data. Science Reports 12, 20989. https://doi.org/10.1038/s41598-022-25278-z
• Silva, J.; Grifagni, D.; Cantini, F. et al. (2022) ¹H, ¹³C and ¹⁵N assignment of the human mitochondrial paramagnetic iron–sulfur protein CISD3. Biomolecular NMR Assignments, https://doi.org/10.1007/s12104-022-10113-3
• Matentzoglu, N.; Balhoff, J.; Bello, S. et al. (2022). A Simple Standard for Sharing Ontological Mappings (SSSOM). Database 2022. https://doi.org/10.1093/database/baac035 
• Hiltemann, S.; Rasche, H.; Gladman, S. et al. (2023) Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 19(1): e1010752. https://doi.org/10.1371/journal.pcbi.1010752

The following non-peer-reviewed publications are at least partially funded via EOSC-Life, Grant Agreement No. 824087:

• Bernillon, S.; Cabasson, C.; Cassan, C. et al. (2019) MetaboHUB-Bordeaux. Journées Scientifiques INRA-BAP, Hyères, France. 10.15454/1.5572412770331912E12.
• Zagidullin, B.; Aldahdooh, J.; Zheng, S. et al. (2019) DrugComb – an integrative cancer drug combination data portal. https://doi.org/10.1101/560227
• Jacob, D.; David, R.; Aubin, S. et al. (2020) Towards FAIRification of experimental data tables in life sciences: a pragmatic approach. 10.15454/1.5572412770331912E12
• Fairweather, E.; Wittner, R.; Chapman, M. et al. (June 2020) International Provenance & Annotation Workshop (IPAW). Non-repudiable provenance for clinical decision support systems.  http://arxiv.org/abs/2006.11233
• Bray, S.; Senapathi, T.; Barnett, C. et al. (2020) Intuitive, reproducible high-throughput molecular dynamics in Galaxy: a tutorial. https://doi.org/10.1101/2020.05.08.084780
• Ramirez-Aportela, E.; Maluenda, D.; Fonseca, Y. et al. (2020) FSC-Q: A CryoEM map-to-atomic model quality validation based on the local Fourier Shell Correlation.   https://doi.org/10.1101/2020.05.12.069831
• Sorzano, C.; Crisman, E.; Carazo, J. et al. (2020) Multitarget virtual screening for drug repurposing in COVID19. https://doi.org/10.26434/chemrxiv.12652997.v2
• Goble, C. (2020) The WorkflowHub – a registry for workflows. The Bioschemas workflow profile.  https://doi.org/10.5281/zenodo.4012124
• Canham, S. (August 2021) ECRIN metadata schema for clinical research data objects version 6.0. https://doi.org/10.5281/zenodo.1312538
• Soiland-Reyes, S.; Eguinoa, I. (2021) Packaging workflows with RO-Crate.  https://doi.org/10.5281/zenodo.4011999.
• Zullino, S.; Paglialonga, A.; Dastrù, W. et al. (2021) XNAT-PIC: extending XNAT to preclinical imaging centers. https://arxiv.org/abs/2103.02044
• Rauber, A.; Weise, M.; Landman, M. et al. (2021) Open source secure data infrastructure and processes for life sciences (OSSDIP4LIFE). https://doi.org/10.5281/zenodo.5534008
• Goble, C.; Soiland-Reyes, S.; Bacall, F. et al. (2021). Implementing FAIR Digital Objects in the EOSC-Life Workflow Collaboratory. https://doi.org/10.5281/zenodo.4605654
• Kerfant, C.; Karoune, E.; Lancelotti, C. et al. (2022) Increasing the FAIRness of phytolith data. https://doi.org/10.5281/zenodo.6379803
• Abbes, A.; Machicao, J.; Meneguzzi, L. et al. (2022) Checklist strategies to improve the reproducibility of deep learning experiments with an illustration.  https://doi.org/10.5281/zenodo.6587702
• Machicao, J.; Abbes, A.; Meneguzzi, L. et al. (2022) Deep learning experiments: common challenges and recommendations for improvement. https://doi.org/10.5281/zenodo.6587694
• Epinoux, C.; Castrec, J.; David, R. et al. (2022) Good practices and challenges for sharing multidisciplinary scientific data: How the QUAMPO project fits into the FAIR and open science movement? https://doi.org/10.5281/zenodo.6779714
• Karoune, E.; Barboni, D.; Bates, J. et al. (2022) Open phytolith publishing guidelines (Version v1). https://doi.org/10.5281/zenodo.6803489
• Coppens, F. (2022) usegalaxy.be. https://doi.org/10.5281/zenodo.6607021
• Karoune, E., Plomp, E. (2022) Removing barriers to reproducible research in archaeology (Version v2). https://doi.org/10.5281/zenodo.6618672
• Coppens, F. (2022) Building an interoperable genomic data infrastructure.  https://doi.org/10.5281/zenodo.6604464
• Portier, M.; Decruw, C.; Exter, K. et al. (2022) Contemporary data management for biodiversity observation networks leading to linked open data publishing through distributed techniques applying RO-Crate and GitHub actions. Biodiversity Information Science and Standards 6: e94630. https://doi.org/10.3897/biss.6.94630
• Niehues, A.; de Visser, C.; Hagenbeek, F. et al. (2022) A multi-omics data analysis workflow packaged as a FAIR digital object. Research Ideas and Outcomes 8: e94042. https://doi.org/10.3897/rio.8.e94042
• Rocca-Serra, P.; Gu, W.; Ioannidis, V. et al. (2022) The FAIR Cookbook – the essential resource for and by FAIR doers (Preprint, Version 1.0). https://doi.org/10.5281/zenodo.7156792
• Musaubach, M. (September 20, 2022) Ubicación y traducción. Evento de lanzamiento de la Comunidad de Fitolitos Abiertos.  https://doi.org/10.5281/zenodo.7098061
• Grannel, R.; Dauga, D.; Lister, A. et al. (2022) Semi-automatic mapping of external scientific databases into the FAIRsharing platform (Presentation, Version 1.0, RSECon2022). https://doi.org/10.5281/zenodo.7064637
• Karoune, E.; Plomp, E. (2022) Removing Barriers to Reproducible Research in Archaeology (Preprint, Version v3). https://doi.org/10.5281/zenodo.7239193
• Holub, P.; Müller, H.; Bil, T. et al. (2022) Privacy Risks of Whole-Slide Image Sharing in Digital Pathology (preprint). medRxiv. https://doi.org/10.1101/2022.04.06.22273523
• Wang, Y.; Aldahdooh, J.; Yang, H. et al. (2022) DrugRepo: A novel approach to repurpose a huge collection of compounds based on chemical and genomic features. Scientific Reports, 12 (1) https://doi.org/10.1101/2022.04.21.488995
• Xu, F.; Juty, N.; Goble, C. et al. (2022) Features of a FAIR vocabulary (Preprint). Research Square. https://doi.org/10.21203/rs.3.rs-1465079/v1
• Kerfant, C.; Ruiz-Pérez, J.; García-Granero, J. et al. (2022) A dataset for assessing phytolith data for implementation of the FAIR data principles (Preprint). https://doi.org/10.5281/zenodo.7215804
• Karoune, E.; García-Granero, J.; Lancelotti, C. et al.  (September 27, 2022) Open Phytoliths Community Launch Event Presentations. https://doi.org/10.5281/zenodo.7090455
• Goble, C.; Soiland-Reyes, S. (October 5, 2022). RO-Crate: packaging metadata love notes into FAIR Digital Objects (Presentation, HMC/IAPS Conference 2022).  https://doi.org/10.5281/zenodo.7147703
• Kerfant, C.; Karoune, E.; Lancelotti, C. et al. (October 6, 2022). Rethinking data sharing in phytolith research: from FAIR phytoliths to key insights for archaeology (Presentation, EEA Annual Meeting 2022). https://doi.org/10.5281/zenodo.7152322
• Karoune, E. García-Granero, J., Lancelotti, C. et al. (September 27, 2022) Open Phytoliths Community Launch Event Presentations. https://doi.org/10.5281/zenodo.7090455
• Guevara, M.; Cam, D.; Badagbon, J. et al. (October 16, 2022) QSM4SENIOR: Quantitative susceptibility mapping in the aging of the healthy brain. (QMR a joint workshop on MR phase, magnetic susceptibility and electrical properties mapping. Lucca, Italy.) https://doi.org/10.5281/zenodo.7303615
• Clark-Casey, J., Soiland-Reyes, S. (November 16, 2022) Making EOSC Research Objects FAIR with RO-Crate: A common metadata overlay for EOSC repositories. (Talk presented at EOSC Symposium 2022, Prague, CZ.) https://doi.org/10.5281/zenodo.7323480
• Wang, W.; Bao, J.; Zheng, S. et al. (November 8, 2022) A gene essentiality signature for studying the mechanism of action of drugs. (bioRxiv preprint.) https://doi.org/10.1101/2022.11.07.514541 
• Holub, P.; Müller, H.; Bíl, T. et al. (January 12, 2023) Risks of Whole-Slide Image Sharing in Digital Pathology. (medRxiv preprint) https://doi.org/10.1101/2022.04.06.22273523
• David, R.; Richard, A.; Connellan, C. et al. (2023) Umbrella Data Management Plans to Integrate FAIR Data: Lessons from the ISIDORe and BY-COVID Consortia for Pandemic Preparedness. (Data Science Journal preprint) https://doi.org/10.5281/zenodo.7520086

EOSC-Life encompasses 34 deliverables, all but 1 of which are public. Deliverables will be uploaded here as soon as published:

• Deliverable D5.1 – Access and User Management System for Life Science – The Blueprint (Sep 2019)
• Deliverable D9.1 — Final report of analysis of the training needs of different EOSC-Life users (Feb 2020)
• Deliverable D6.1 – FAIR Requirements Document  (March 2020)
• Deliverable D4.2 — Report on requirements for regulatory compliance of sensitive health data and biological and medical research data (May 2020)
• Deliverable D1.1 — EOSC Cloud feasibility assessment for Demonstrators (Aug 2020)
• Deliverable D7.1 — Initial EOSC-LS Cloud Observatory Report (Aug 2020)
• Deliverable D12.1 (Confidential) (Aug 2020)
• Deliverable D2.1 — Cloud implementation of exemplary workflows (Aug 2020)
• Deliverable D11.1 — Data Management Plan (Aug 2020)
• Deliverable D3.1 — Publication of generic guidelines for the organization of topic-specific Open Calls (Sep 2020)
• Deliverable D14.1 — Strategic plan for the development of a COVID-19 repository including specification of technical requirements, policies and procedures (Oct 2020)
• Deliverable D9.2 — Review of EOSC-Life training activities, their impact and potential adaptations (Feb 2021)
• Deliverable D10.1 — Strategic Marketing and Stakeholder Mapping Report (Feb 2021)
• Deliverable D5.2 — Access and User Management System for Life Science – the implementation and usage report (Feb 2021)
• Deliverable D4.3 — Guidance and policy on standards and tools to facilitate sharing and reuse of multimodal data (including imaging), cohort integration, and biosamples (March 2021)
• Deliverable D5.3 — Access and User Management System for Life Science – the blueprint: 1st update (March 2021)
• Deliverable D6.2 — Common Provenance Model (April 2021)
• Deliverable D8.1 — Methodology framework to enhance reproducibility within EOSC-Life (April 2021)
• Deliverable D3.2 — Report on the work of the initial demonstrators (May 2021)
• Deliverable D11.2 — Life Science in EOSC: Summary of EOSC-Life impact and key results (August 2021)
• Deliverable D13.1 — Enhanced COVID-19 Portal and data mobilisation reporting (Nov 2021)
• Deliverable D4.4: Report on data standards for observational and interventional studies, and interoperability between healthcare and research data (December 2021)
• Deliverable D1.3: FAIR services deployment for open calls (February 2022)
• Deliverable D14.2 — Report about technical implementation and validation of the COVID-19 portal (May 2022)
• Deliverable D7.2 — EOSC-Life final EOSC-LS cloud observatory report (June 2022)
• Deliverable D5.4 — EOSC-Life Access and User Management System for Life Science – the blueprint update (June 2022)
• Deliverable D4.1: Requirements for Hosting/Distributing and Access Control for Sensitive Data (August 2022)
• Deliverable D6.3: FAIRsharing Webpage (August 2022)
• Deliverable D13.2: Data Mobilisation Summary (September 2022)
• Deliverable D8.1: EOSC-Life Methodology framework to enhance reproducibility within EOSC-Life – revised version. (September 2022)
• Deliverable D4.5: EOSC-Life Public database inventorying the national health databases and registries and describing their access procedures for reuse for research purposes. (October 2022)
• Deliverable D2.2: EOSC-Life A common environment that can run cross-RIs workflows. (October 2022)

Newsletter 1 – 14 Nov 2020

Newsletter 2 – 10 Feb 2020

Newsletter 3 . AGM – 27 Feb 2020

Newsletter 4 – 27 April 2020

Newsletter 5- 29 June 2020

Newsflash – 2nd Training Open Call – 10 Sept 2020

Newsflash – Digital Life Sciences Open Call – 15 Sept 2020

Newsletter 6 – 22 Oct 2020

Newsletter 7 – 16 Dec 2020

Newsletter 8 – 11 Feb 2021

Newsletter 9 – 19 April 2021

Newsletter 10 – 17 June 2021

Newsletter 11 – 21 October 2021

Newsletter 12 – 16 December 2021

Newsletter 13 – 2 March 2022

Newsletter 14 – 2 May 2022

Newsletter 15 – 30 June 2022

Newsletter 16 – 22 September 2022

Newsletter 17 – 17 November 2022

Subscribe to our newsletter

• Multilingual Data Challenges in Professionalizing Data Stewardship worldwide (V1.1)

David, R.; Specht, A.; O’Brien, M.; Wyborn, L.; Drummond, C.; Edmunds, R.; Filippone, C.; Machicao, J.; Miyairi, N.; Parton, G.; Pignatari Drucker, D.; Stall, S.; Zimmer, N. (2022). RDA 19th Plenary Meeting, Part Of International Data Week, 20–23 June 2022 (RDA Plenary 19th), Seoul, South Korea. Zenodo. https://doi.org/10.5281/zenodo.6588167

The use of common FAIR vocabularies, that are both human and machine readable, is a key criterion in the FAIR principles (e.g. Principle I2 of Wilkinson et al 2016 specifies ‘(meta)data use vocabularies that follow FAIR principles’). Using common FAIR vocabularies will enable data interoperability and the necessary meta-analyses even when data have different origins and are based on multiple vocabularies. This poster offers an overview of the many multi-language challenges for effective Data Stewardship. For instance, some bottlenecks are highly dependent on community approval processes that are linked to data dictionary understandability, and/or related training challenges.

• Incrementally building FAIR Digital Objects with Specimen Data Refinery workflows

Woolland, O.; Brack, P.; Soiland-Reyes, S.; Scott, B.; Livermore, L. (2022). 1st International Conference on FAIR Digital Objects (FDO2022), Leiden, The Netherlands. Zenodo. https://doi.org/10.5281/zenodo.7233688

The Specimen Data Refinery (SDR) is a developing platform for automating transcription of specimens from natural history collections (Hardisty et al. 2022). SDR is based on computational workflows and digital twins using FAIR Digital Objects. In this poster, the authors show their recent experiences with building SDR using the Galaxy workflow system and combining two FDO methodologies with open digital specimens (openDS) and RO-Crate data packaging. They suggest FDO improvements for incremental building of digital objects in computational workflows.

• Creating lightweight FAIR Digital Objects with RO-Crate and FAIR Signposting

Soiland-Reyes, S.; Sefton, P.; Jael Castro, L.; Coppens, F.; Garijo, D.; Leo, S.; Portier, M.; Groth, P.; Goble, C. (2022). 1st International Conference on FAIR Digital Objects (FDO2022), Leiden, The Netherlands. Zenodo. https://doi.org/10.5281/zenodo.7245315

RO-Crate is a lightweight method used to package research outputs and their metadata, based on Linked Data principles and W3C standards. This method enables researchers to flexibly archive and publish rich data packages (or any other research outcome) by capturing their dependencies and context. Additional measures need to be taken, however, to ensure that FAIR principles are applied in a crate (e.g. consistent use of persistent identifiers, provenance, community standards, clear machine/human-readable licensing for metadata and data, and Web publication of RO-Crates). The FAIR Digital Object (FDO) approach provides recommendations that can help to improve findability, accessibility, interoperability and reproducibility for any digital object, allowing implementation through different protocols or standards. This poster presents how the authors followed the FDO recommendations and turned research outcomes into FDOs by publishing RO-Crates on the Web using HTTP, following best practices for Linked Data. They highlight challenges and advantages of the FDO approach, and reflect on what is required for an FDO profile to achieve FAIR RO-Crates.

• Galaxy in EOSC-Life

Serrano-Solano, B. (30 June 2022) ELIXIR All Hands 2022. F1000Research https://doi.org/10.7490/f1000research.1119100.1

These slides provided an overview of the EOSC-Life consortium, work packages, integration with the Galaxy ecosystem, and highlighted specific use cases and user projects, including the COVID-19 Data Portal and training and outreach programs.

• EOSC-Life – Building a Digital Space for the Life Sciences

Schmidt-Tremmel, F. (2022) ELIXIR All Hands 2022. F1000Research https://doi.org/10.7490/f1000research.1119018.1

This poster presented information about EOSC-Life, a platform that brings together the 13 Life Science ‘ESFRI’ research infrastructures (LS RIs) to create an open, digital and collaborative space for biological and medical research. The project is starting to publish ‘FAIR’ data and a catalogue of services provided by participating RIs for the management, storage and reuse of data in the European Open Science Cloud (EOSC). This space will be accessible to European research communities.

• BBMRI-ERIC ELSI Knowledge Base

Cano Abadía, M.; Adeniran, A.; Ahern, C.; Akyüz, K.; Casati, S.; Colussi, I.; Mayrhofer, M.; Schlünder, I. (2022). Europe Biobank Week 2021. Zenodo. https://zenodo.org/record/7016638

This poster presented information about the BBMRI-ERIC ELSI Knowledge Base, an open-access resource platform containing information on ethical, legal, and societal issues relevant to the life sciences and, most notably, biobanking.

• RDA Sensitive Data IG: Towards a common understanding of sensitive data across regions and disciplines

David, R, Burton, N, Davidson, S C, McEachern, S, Kang, K, Michalewicz, A, Pillai, P, Spreadborough, K, & Stevens, F. (2021). RDA 18 Meeting. Zenodo. 10.5281/zenodo.5663084

This poster, supported by some EOSC-Life members, is presented in tandem with the Sensitive Data IG session at RDA18 (which shares the same name). In previous meetings, the Sensitive Data IG has scoped the interest and needs of the RDA community. At this meeting, we focus on specific challenges and opportunities associated with working with Sensitive Data. This poster explores one of these – how do we understand sensitive data definitions in different regions and disciplines, and how might we develop a shared language around sensitive data. For example, developing an understanding of different community agreed vocabularies and how these relate to each other. This poster aims to promote wider discussion on how sensitive data is currently defined. Further, it aims to draw out how context can impact whether data is considered sensitive. In doing so, it takes steps towards developing a common language for discussing sensitive data classifications and levels across contexts/disciplines/localities.

• EOSC-Life pilot study on the intercalibration of a categorisation system for FAIRer digital objects related to sensitive data in the life sciences

David, Romain, Ohmann, Christian, Boiten, Jan-Willem, Canham, Steve, Panagiotopoulou, Maria, Mayrhofer, Michaela Theresia, Longo, Dario, Bietrix, Florence, Chiusano, Maria Luisa, Laroquette, Arnaud, Richard, Audrey, Cano Abadía, Mónica, Dastru, Walter, & Verde, Pablo Emilio. (2021). EOSC-Life pilot study on the intercalibration of a categorisation system for FAIRer digital objects related to sensitive data in the life sciences. RDA 18 Meeting. Zenodo. https://doi.org/10.5281/zenodo.5729437

• Provenance Week 2020 Poster: ISO 23494: Biotechnology – Provenance Information Model for Biological Specimen and Data

Wittner, Rudolf; Holub, Petr; Geiger, Jörg; Müller, Heimo; Goble, Carole; Soiland-Reyes, Stian; Fairweather, Elliot; Pirredu, Luca; Frexia, Francesca; Mascia, Cecilia; Zanetti, Gianluigi; Nakae, Hiroki; Strambio, Caterina; Moore, Josh; Grunwald, David; Swedlow, Jason 4th Provenance Week 2020 10.5281/zenodo.3901011

EOSC-Life version here.

• Issues and obstacles to the sharing of sensitive data in Europe, who are the stakeholders and what are their responsibilities?

David, Romain, Richard, Audrey, Stepanyann, Diana, Versteeg, Krista, Mineau-Cesari, Jonathan, Mistou, Michel-Yves, … Raoul, Hervé. (2020). Issues and obstacles to the sharing of sensitive data in Europe, who are the stakeholders and what are their responsibilities ? (Version 1.0). Presented at the CODATA and GO FAIR International FAIR Convergence Symposium (FAIR Convergence Symposium), France, 10.5281/zenodo.4293611

Presented at:

• RDA’s 16th Plenary – remotely, “Knowledge Ecology”, (RDA P16), Costa Rica, 09 – 12 November 2020 (Session Poster)
• CODATA and GO FAIR International FAIR Convergence Symposium (FAIR Convergence Symposium), France, 27 November – 4 December 2020 (Session Poster, Poster 63)

• The main European initiatives for sharing data and other research results in support of the scientific community. ELIXIR (Covid-19 data portal), OpenAIRE, RDA. Second edition

Allegra Via. Presentation given during the second edition of the webinar “Covid-19 e condivisione dei dati: perché in Italia si fa troppo poco?” belonging to the event series “Open Science e Covid-19. Collaborare per contrastare la pandemia”, on November 16th, 2020.

• Enabling & stimulating submission to the COVID data portal & re-use of the data

Frederik Coppens. Presentation on the status and progress in Belgium at the monthly meeting on the COVID-19 Data Portal organised by EMBL-EBI and the European Commission. This represents the status in November 2020, focussing on the development of tools to submit data to ENA. A command-line application was developed which was made available in Galaxy. Additionally containers have been generated to easily run the application locally.

• How in silico methods and data reuse can help reduce the number of animal experiments

Maddalena Fratelli. Presentation at the conference  “Towards replacement of animals for scientific purposes” ,  2-3 February 2021. Talking about machine learning to develop models of drug sensitivity prediction, (Q)SAR models of toxicity prediction and the role of data re-use in enhancing external validity of the studies.

• Data dictionary cookbook for research data and software interoperability at global scale

Romain David, Laurent Bouveret, Lorraine Coché, Pedro Pizzigatti Corrêa, Rorie Edmunds, Ana Heredia, … Lesley Wyborn. (2021). Data dictionary cookbook for research data and software interoperability at global scale (Version 1.0). Presented at the Research Data Alliance Plenary 17 (RDA P17), Edinburg http://doi.org/10.5281/zenodo.4683066

• RDA Sensitive Data Interest Group: Goals and Roadmap

David, Romain, Akmon, Dharma, Burton, Nichola, McEachern, Steven, Michalewicz, Aleksandra, Pillai, Priyanka, … Stevens, Frankie. (2021). RDA Sensitive Data Interest Group: Goals and Roadmap. http://doi.org/10.5281/zenodo.4690571

• FAIRness in Cryo-Electron Microscopy data analysis

Irene Sánchez, Laura del Caño, Carlos Oscar Sorzano:  FAIRness in Cryo-Electron Microscopy data analysis. FAIR Metrics and data quality session at the EOSC Symposium 2021 (https://www.eoscsecretariat.eu/eosc-symposium-2021). This session pointed out a method developed within the EOSC-Life project for helping Cryo-EM facility users the deposition of data and processing workflows in a standardized way in order to increase its re-use by the scientific community.

• Multilingual Data Challenges in Professionalizing Data Stewardship worldwide (V1.1)

David, Romain, Specht, Alison, O’Brien, Margaret, Wyborn, Lesley, Drummond, Christina, Edmunds, Rorie, Filippone, Claudia, Machicao, Jeaneth, Miyairi, Nobuko, Parton, Graham, Pignatari Drucker, Debora, Stall, Shelley &  Zimmer, Niklas. (2022). RDA 19th Plenary Meeting, Part Of International Data Week, 20–23 June 2022, Seoul, South Korea. This poster offers an overview of the many multi-language challenges for effective Data Stewardship.

• Deep Learning experiments: common challenges and recommendations for improvement.

Machicao, Jeaneth, Abbes, Ali Ben, Meneguzzi, Leonardo, Corrêa, Pedro Pizzigatti, Specht, Alison, David, Romain, Subsol, Gérard, Vellenich, Danton Ferreira, Stall, Shelley, Mouquet, Nicolas, Chaumont, Marc, Berti-Equille, Laure, & Mouillot, David. (2022). RDA 19th Plenary Meeting, Part Of International Data Week, Seoul, South Korea. Based on the reproduction of three Deep Learning case studies on real-world tasks, such as poverty estimation from remote sensing imagery, we identified common problems in the reproduction, then proposed a set of recommendations (‘fixes’) to overcome these problems.

• Checklist Strategies to Improve the Reproducibility of Deep Learning Experiments with an Illustration.

Abbes, Ali Ben, Machicao, Jeaneth, Meneguzzi, Leonardo, Corrêa, Pedro Pizzigatti, Specht, Alison, David, Romain, Subsol, Gérard, Vellenich, Danton Ferreira, Stall, Shelley, Mouquet, Nicolas, Chaumont, Marc, Berti-Equille, Laure, & Mouillot, David. (2022). Checklist Strategies to Improve the Reproducibility of Deep Learning Experiments with an Illustration. RDA 19th Plenary Meeting, Part Of International Data Week, Seoul, South Korea. We compiled 3 different aspects to help researchers to improve Reproducibility and Replicability (R&R), referring to machine learning checklists, guidelines, and principles from FAIR. Then, we illustrated the compilation of 3 recent DL experiments for socio-economic estimation using remotely sensed data.

• Good practices and challenges for sharing multidisciplinary scientific data: how the QUAMPO project fits into the FAIR and open science movement?

Epinoux, Clémence, Castrec, Justine, David, Romain, Fontaine, Quentin, Fullgrabe, Lovina, Gobert, Sylvie, Le Floch, Stephane, Le Jeune, Pierre, Madon, Bénédicte, Marengo, Michel, Pignon-Mussaud, Cécilia, Pillet, Marion, & Thomas, Helen. RDA 19th Plenary Meeting, Part Of International Data Week, Seoul, South Korea. In this poster, we show how the QUAMPO project sought to be part of the open science and FAIR principles (Findable, Accessible, Interoperable and Reusable) movement. Thus, QUAMPO is aiming to become a pioneer project in opening multidisciplinary data in ecotoxicological monitoring of biomarkers in Corsican ports.

• Recent achievements of the EOSC-Life project.

Romano, P., Rosato, P., EOSC-Life project Consortium. 18th Annual Meeting of the Bioinformatics Italian Society, BITS 2022, Verona, Italy. EOSC-LIFE has been funded by the EU in the context of initiatives for the empowerment of Research Infrastructures (RIs). It brings together the 13 Life Science RIs to create an open, digital and collaborative space for biological and medical research. It aims at enabling interoperability of the RI data and workflows, in line with the FAIR principles, by leveraging appropriate technologies such as APIs, ontologies and workflow management systems. In this poster, we present some of its most recent achievements.

In December 2021, EOSC-Life was asked to submit a project brief to the European Commission highlighting milestones toward integration with the European Open Science Cloud and including recommendations for future priorities within the EOSC.

Read the project brief

The EOSC Science Cluster projects sent a joint letter to the EOSC Association and the European Commission in September 2021 arguing for the continuation of the Science Cluster activities in the HEU Work Programme for Research Infrastructures 2023-2024.

View here

View here

Through its cluster projects, ESCAPE, ENVRI-FAIR, SSHOC, PaNOSC and EOSC-Life, the European Strategy Forum on Research Infrastructures (ESFRI) steers the integration and consolidation of thematic e-infrastructure platforms in preparation for connecting them to EOSC. The need to leverage domain expertise and better drive the innovation which research communities require can be found in these position papers reflecting current views and expectations about EOSC and the contributions of ESFRI to the European data infrastructure. An executive summary and overview table provide insights into the views of some of the most important research infrastructures in Europe on the future EOSC.

You can read the collection of the five position papers on Zenodo at this link: https://zenodo.org/record/3675081

The European Open Science Cloud (EOSC) will “enable a trusted, virtual, federated environment in Europe to store, share and reuse digital outputs from research (including publications, data, metadata and software) across borders and scientific disciplines”1. Today, at the end of 2020, many building blocks are in place and on-going EOSC projects endeavour to enrich the EOSC ecosystem. To ensure strong engagement by research communities, we believe that this is the time to re-orient the EOSC activities towards higher relevance for these communities.

The Science Clusters ENVRI-FAIR , EOSC-LIFE , ESCAPE , PaNOSC , SSHOC bring together 72 world-class research infrastructures from the ESFRI roadmap and beyond to work on FAIR data management and connecting their user communities to the EOSC. The European e-infrastructures EGI , EUDAT , GÉANT , and OpenAIRE provide inclusive services across Europe and beyond to the benefit of science.

Link to article