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. FAIRsharing as a community approach to standards, repositories and policies. (2019) Nature Biotechnology 37, 358. https://doi.org/10.1038/s41587-019-0080-8
• Föll, M.; Moritz, L.; Wollmann, T. et al. Accessible and reproducible mass spectrometry imaging data analysis in Galaxy. (2019) GigaScience 8, 1. https://doi.org/10.1093/gigascience/giz143.
• Wilkinson, M.; Dumontier, M.; Sansone, S. et al. Evaluating FAIR maturity through a scalable, automated, community-governed framework. (2019) Scientific Data 6, 174. https://doi.org/10.1038/s41597-019-0184-5
• Bray, S.; Lucas, X.; Kumar, A. et al. The ChemicalToolbox: reproducible, user-friendly cheminformatics analysis on the Galaxy platform. (2020) 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. Measuring local-directional resolution and local anisotropy in cryo-EM maps. (2020) Nature Communications 11, 55.  https://doi.org/10.1038/s41467-019-13742-w
• Rocca-Serra, P.; Sansone, S. Experiment design driven FAIRification of omics data matrices, an exemplar. (2020) Scientific Data 6, 271. https://doi.org/10.1038/s41597-019-0286-0
• Abudukelimu, A.; Barberis, M.; Redegeld, F. et al. Complex stability and an irrevertible transition reverted by peptide and fibroblasts in a dynamic model of innate immunity. (2020) Frontiers in Immunology 10. https://doi.org/10.3389/fimmu.2019.03091
• Goble, C.; Cohen-Boulakia, S.; Soiland-Reyes, S. et al. FAIR computational workflows. (2020) Data Intelligence, 2(1-2), 108.  https://doi.org/10.1162/dint_a_00033
• David, R.; Mabile, L.; Specht, A. et al. FAIRness literacy: The Achilles’ heel of applying FAIR principles. (2020) Data Science Journal, 19(32), 1-11. http://doi.org/10.5334/dsj-2020-032
• Střelák, D.; Filipovič, J.; Jiménez-Moreno, A. et al. FlexAlign: An accurate and fast algorithm for movie alignment in cryo-electron microscopy. (2020) Electronics, 9 (6), 1040.  https://doi.org/10.3390/electronics9061040
• Melero, R.; Sorzano, C.; Foster, B. et al. Continuous flexibility analysis of SARS-CoV-2 spike prefusion structures. (2020) International Union of Crystallography Journal, 7(6), 1059. https://doi.org/10.1107/S2052252520012725
• Caracciolo, C.; Aubin, S.; Jonquet, C. et al. 39 Hints to facilitate the use of semantics for data on agriculture and nutrition.  (2020) Data Science Journal 19(47), 1. http://doi.org/10.5334/dsj-2020-047
• Jacob, D.; David, R.; Aubin, S. et al. Making experimental data tables in the life sciences more FAIR: a pragmatic approach. (2020) GigaScience 9, 1. https://doi.org/10.1093/gigascience/giaa144
• Obst, M.; Exter, K.; Allcock, A. et al. A marine biodiversity observation network for genetic monitoring of hard-bottom communities (ARMS-MBON). (2020) Frontiers in Marine Science, Frontiers Media 7. https://doi.org/10.3389/fmars.2020.572680
• Vilas, J.; Oton, J.; Messaoudi, C. et al.  Measurement of local resolution in electron tomography.  (2020) 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. Improvements on marker-free images alignment for electron tomography.  (2020) Journal of Structural Biology 4, 100037. https://doi.org/10.1016/j.yjsbx.2020.100037
• Tanoli, Z.; Seemab, U.; Scherer, A. et al. Exploration of databases and methods supporting drug repurposing: a comprehensive survey. (2020) Briefings in Bioinformatics 22(2), 1656. https://doi.org/10.1093/bib/bbaa003
• Tangaro, M.; Mandreoli, P.; Chiara, M. et al. Laniakea@ReCaS: exploring the potential of customisable Galaxy on-demand instances as a cloud-based service. (2021) BMC Bioinformatics 22, 544. https://doi.org/10.1186/s12859-021-04401-3
• Sanchez-Garcia, R.; Gomez-Blanco, J.; Cuervo, A. et al.: DeepEMhancer: a deep learning solution for cryo-EM volume post-processing. (2021) Communications Biology 4, 874. https://doi.org/10.1038/s42003-021-02399-1
• Pickering B.; Biro, T.; Austin, C. et al. Radical collaboration during a global health emergency: development of the RDA COVID-19 data sharing recommendations and guidelines. (2021) Open Research Europe 1, 69. https://doi.org/10.12688/openreseurope.13369.1
• Holzinger, A.; Müller, H. Toward human–AI interfaces to support explainability and causability in medical AI. (2021) Computer 54(10), 78. https://doi.org/10.1109/MC.2021.3092610
• Fernández-Giménez, E.; Martínez, M.; Sánchez-García, R. et al. Cryo-EM density maps adjustment for subtraction, consensus and sharpening. (2021) Journal of Structural Biology 213 (4), 107780. https://doi.org/10.1016/j.jsb.2021.107780
• Laveglia, V.; Giachetti, A.; Cerofolini, L. et al. Automated determination of nuclear magnetic resonance chemical shift perturbations in ligand screening experiments: The PICASSO web server. (2021) 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. Perspectives on automated composition of workflows in the life sciences. (2021) F1000Research, 10, 897. https://doi.org/10.12688/f1000research.54159.1
• Strelak, D.; Jiménez-Moreno, A.; José, L. et al. Advances in Xmipp for cryo–electron microscopy: from Xmipp to Scipion. (2021) Molecules 26(20), 6224. https://doi.org/10.3390/molecules26206224
• Machwitz, M.; Pieruschka, R.; Berger, K. et al. Bridging the gap between remote sensing and plant phenotyping—challenges and opportunities for the next generation of sustainable agriculture. (2021) Frontiers in Plant Science, 12, 749374. https://doi.org/10.3389/fpls.2021.749374
• Macias, J.; Sanchez-Garcia, R.; Conesa, P. et al. 3DBionotes COVID-19 edition. (2021)  Bioinformatics 37(22), 4258. https://doi.org/10.1093/bioinformatics/btab397
• Hruska, M.; Holub, D. A complete search of combinatorial peptide library greatly benefited from probabilistic incorporation of prior knowledge. (2022) International Journal of Mass Spectrometry 471, 116723. https://doi.org/10.1016/j.ijms.2021.116723
• Machicao, J.; Specht, A.; Vellenich, D. et al. A deep-learning method for the prediction of socio-economic indicators from street-view imagery using a case study from Brazil. (2022) Data Science Journal 21(6), 1. https://doi.org/10.5334/dsj-2022-006
• Zullino, S.; Paglialonga, A.; Dastrù, W. et al. XNAT-PIC: extending XNAT to preclinical imaging centers. (2022) 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. 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. Selection of data sets for FAIRification in drug discovery and development: Which, why, and how? (2022) Drug Discovery Today 27(8), 2080. https://doi.org/10.1016/j.drudis.2022.05.010
• Crusoe, M.; Abeln, S.; Iosup, A. et al. Methods included: Standardizing computational reuse and portability with the common workflow language. (2022) Communications of the ACM 65(6), 54. https://doi.org/10.1145/3486897
• Ohmann, C.; David, R.; Cano Abadia, M. et al. Pilot study on the intercalibration of a categorisation system for FAIRer digital objects related to sensitive data in the life sciences. (2022) 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. Making Canonical Workflow Building Blocks Interoperable across Workflow Languages. (2022) Data Intelligence, 4(2), 342. https://doi.org/10.1162/dint_a_00135
• Zaliani, A.; Vangeel, L.; Reinshagen, J. et al. Cytopathic SARS-CoV-2 screening on VERO-E6 cells in a large-scale repurposing effort. (2022) Scientific Data, 9, 405. https://doi.org/10.1038/s41597-022-01532-x 
• Laveglia, V.; Giachetti, A.; Sala, D. et al. 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. (2022) 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. Mouse mutants of Gpr37 and Gpr37l1 receptor genes: disease modeling applications. (2022) International Journal of Molecular Sciences, 23(8), 4288. https://doi.org/10.3390/ijms23084288
• Birling, M.; Fray, M.; Kasparek, P. et al. Importing genetically altered animals: ensuring quality. (2022) Mammalian Genome, 33, 100. https://doi.org/10.1007/s00335-021-09908-x
• Hruska, M.; Holub, D. Evaluation of an integrative Bayesian peptide detection approach on a combinatorial peptide library. (2022) 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
• Karoune, E. (2022) Assessing Open Science Practices in Phytolith Research. 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

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. MetaboHUB-Bordeaux. Journées Scientifiques INRA-BAP, Hyères, France. (2019) 10.15454/1.5572412770331912E12.
• Zagidullin, B.; Aldahdooh, J.; Zheng, S. et al. DrugComb – an integrative cancer drug combination data portal. (2019) https://doi.org/10.1101/560227
• Jacob, D.; David, R.; Aubin, S. et al. Towards FAIRification of experimental data tables in life sciences: a pragmatic approach. (2020) 10.15454/1.5572412770331912E12
• Fairweather, E.; Wittner, R.; Chapman, M. et al. International Provenance & Annotation Workshop (IPAW). Non-repudiable provenance for clinical decision support systems. (June 2020)  http://arxiv.org/abs/2006.11233
• Bray, S.; Senapathi, T.; Barnett, C. et al. Intuitive, reproducible high-throughput molecular dynamics in Galaxy: a tutorial. (2020) https://doi.org/10.1101/2020.05.08.084780
• Ramirez-Aportela, E.; Maluenda, D.; Fonseca, Y. et al. FSC-Q: A CryoEM map-to-atomic model quality validation based on the local Fourier Shell Correlation.  (2020) https://doi.org/10.1101/2020.05.12.069831
• Sorzano, C.; Crisman, E.; Carazo, J. et al. Multitarget virtual screening for drug repurposing in COVID19. (2020) https://doi.org/10.26434/chemrxiv.12652997.v2
• Goble, C. The WorkflowHub – a registry for workflows. The Bioschemas workflow profile. (2020) https://doi.org/10.5281/zenodo.4012124
• Canham, S. ECRIN metadata schema for clinical research data objects version 6.0. (August 2021). (2021)  https://doi.org/10.5281/zenodo.1312538
• Soiland-Reyes, S.; Eguinoa, I. Packaging workflows with RO-Crate. (2021) https://doi.org/10.5281/zenodo.4011999.
• Zullino, S.; Paglialonga, A.; Dastrù, W. et al. XNAT-PIC: extending XNAT to preclinical imaging centers. (2021) https://arxiv.org/abs/2103.02044
• Rauber, A.; Weise, M.; Landman, M. et al. Open source secure data infrastructure and processes for life sciences (OSSDIP4LIFE). (2021) https://doi.org/10.5281/zenodo.5534008
• Kerfant, C.; Karoune, E.; Lancelotti, C. et al. Increasing the FAIRness of phytolith data. (2022) https://doi.org/10.5281/zenodo.6379803
• Abbes, A.; Machicao, J.; Meneguzzi, L. et al. Checklist strategies to improve the reproducibility of deep learning experiments with an illustration. (2022) https://doi.org/10.5281/zenodo.6587702
• Machicao, J.; Abbes, A.; Meneguzzi, L. et al. Deep learning experiments: common challenges and recommendations for improvement. (2022) https://doi.org/10.5281/zenodo.6587694
• David, R.; Specht, A.; O’Brien, M. et al. Multilingual data challenges in professionalizing data stewardship worldwide (V1.1). (2022) https://doi.org/10.5281/zenodo.6588167
• Epinoux, C.; Castrec, J.; David, R. et al. Good practices and challenges for sharing multidisciplinary scientific data: How the QUAMPO project fits into the FAIR and open science movement? (2022) 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. usegalaxy.be. (2022) https://doi.org/10.5281/zenodo.6607021
• Karoune, E., Plomp, E. Removing barriers to reproducible research in archaeology (Version v2). (2022) https://doi.org/10.5281/zenodo.6618672
• Coppens, F. Building an interoperable genomic data infrastructure. (2022) 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
• Schmidt-Tremmel, F. (30 June 2022) EOSC-Life – Building a Digital Space for the Life Sciences. (Poster presented at ELIXIR All Hands 2022). https://doi.org/10.7490/f1000research.1119018.1
• Serrano-Solano, B. (30 June 2022) Galaxy in EOSC-Life. (Slides presented at ELIXIR All Hands 2022). https://doi.org/10.7490/f1000research.1119100.1
• 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

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 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)

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

• 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.

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.

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.