Ressources bibliographiques
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Articles scientifiques
Aubin A. et al., 2020. Managing data locally to answer questions globally: The role of collaborative science in ecology. Journal of Vegetation Science. https://doi.org/10.1111/jvs.12864
Buschke F. T. et al., 2023. Make global biodiversity information useful to national decision-makers. Nature Ecology and Evolution. https://doi.org/10.1038/s41559-023-02226-2
Fegraus E. H. et al., 2005. Maximizing the Value of Ecological Data with Structured Metadata: An Introduction to Ecological Metadata Language (EML) and Principles for Metadata Creation. Bulletin of the Ecological Society of America. https://doi.org/10.1890/0012-9623(2005)86[158:MTVOED]2.0.CO;2
Fer I. et al., 2020. Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data-model integration. Global Change Biology. https://doi.org/10.1111/gcb.15409
Grüning B. et al., 2018. Practical Computational Reproducibility in the Life Sciences. Cells Systems. https://doi.org/10.1016/j.cels.2018.03.014
Costello M.J. et al., 2013. Biodiversity data should be published, cited, and peer reviewed. Trends in Ecology and Evolution. https://doi.org/10.1016/j.tree.2013.05.002
Gadelha Jr. L.M.R. et al., 2022. A survey of biodiversity informatics: Concepts, practices, and challenges. WIREs Data Mining and Knowledge Discovery. https://doi.org/10.1002/widm.1394
Gomes D.G.E et al., 2022. Why don't we share data and code? Perceived barriers and benefits to public archiving practices. Proc. R. Soc. B.2892022111320221113 http://doi.org/10.1098/rspb.2022.1113
Gonzalez A. et al., 2023. A global biodiversity observing system to unite monitoring and guide action. Nature in Ecology and Evolution. https://doi.org/10.1038/s41559-023-02171-0
Gries C. et al., 2023. The Environmental Data Initiative: Connecting the past to the future through data reuse. Ecology and Evolution. https://doi.org/10.1002/ece3.9592
Hardisty A. and Roberts D. 2013 A decadal view of biodiversity informatics: challenges and priorities. BMC Ecology https://doi.org/10.1186/1472-6785-13-16
Hardisty A. et al., 2019 The Bari Manifesto: An interoperability framework for essential biodiversity variables. Ecological Informatics https://doi.org/10.1016/j.ecoinf.2018.11.003
Hiltemann S. et al.. 2023. Galaxy Training: A powerful framework for teaching! PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1010752
Hughes A.C., et al. 2021. Sampling biases shape our view of the natural world. Ecography. https://doi.org/10.1111/ecog.05926
Hughes A.C. and Grumbine R. E. The Kunming-Montreal Global Biodiversity Framework: what it does and does not do, and how to improve it. Frontiers in Environmental Science. https://doi.org/10.3389/fenvs.2023.1281536
Jenkins G. B. et al., 2023. Reproducibility in ecology and evolution: Minimum standards for data and code. Ecology and Evolution.https://doi.org/10.1002/ece3.9961
Jones M.B. et al., 2006 The New Bioinformatics: Integrating Ecological Data from the Gene to the Biosphere. Annual review in Ecology, Evolution and Systematics. https://doi.org/10.1146/annurev.ecolsys.37.091305.110031
Keller A. et al., 2023. Ten (mostly) simple rules to future-proof trait data in ecological and evolutionary sciences. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.14033
Kissling W.D. et al., 2018. Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale Biological Reviews https://doi.org/10.1111/brv.12359
König C. et al., 2019. Biodiversity data integration—the significance of data resolution and domain. PLOS Biology. https://doi.org/10.1371/journal.pbio.3000183
LaDeau, S.L., et al., 2017. The Next Decade of Big Data in Ecosystem Science. Ecosystems https://doi.org/10.1007/s10021-016-0075-y
Leung B. & Gonzalez A. 2024. Global monitoring for biodiversity: Uncertainty, risk, and power analyses to support trend change detection. Science Advances. https://doi.org/10.1126/sciadv.adj1448
McCleery R. et al., 2023. Uniting Experiments and Big Data to advance ecology and conservation. Trends in Ecology and Evolution. https://doi.org/10.1016/j.tree.2023.05.010
Mcintire E.J.B. et al., 2022. PERFICT: A Re-imagined foundation for predictive ecology. Ecology Letters. https://doi.org/10.1111/ele.13994
Magagna, B, et al. 2020. Reusable FAIR Implementation Profiles as Accelerators of FAIR Convergence. Advances in conceptual modeling. https://link.springer.com/chapter/10.1007/978-3-030-65847-2_13
Michener W.K. et al., 1997 Non geospatial metadata for ecological sciences. Ecological Applications. https://doi.org/10.1890/1051-0761(1997)007[0330:NMFTES]2.0.CO;2
Michener W.K. and Jones M.B. 2012 Ecoinformatics: supporting ecology as a data-intensive science. Trends in Ecology and Evolution https://doi.org/10.1016/j.tree.2011.11.016
Michener W.K. 2014. Ecological Data Sharing. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2015.06.010
Michener W.K. et al., 2015 Ten Simple Rules for Creating a Good Data Management Plan. PLOS Computational Biology https://doi.org/10.1371/journal.pcbi.1004525
Page R. 2016 Towards a biodiversity knowledge graph. Research Ideas and Outcomes https://doi.org/10.3897/rio.2.e8767
Penev L. et al., 2023. Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes. https://doi.org/10.3897/rio.3.e12431
Peterson A.T. et al., 2015 A global perspective on decadal challenges and priorities in biodiversity informatics BMC Ecology https://doi.org/10.1186/s12898-015-0046-8
Poisot T. et al., 2019 Ecological Data Should Not Be So Hard to Find and Reuse. Trends in Ecology and Evolution. https://doi.org/10.1016/j.tree.2019.04.005
Powers S.M. & Hampton S.E., 2018. Open science, reproducibility, and transparency in ecology. https://doi.org/10.1002/eap.1822
Recknagel F. 2023. Cyberinfrastructure for sourcing and processing ecological data. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2023.102039
Rilling M.C. et al., 2015 Biodiversity Research: data without theories - theories without data. Frontiers in Ecology and Evolution https://doi.org/10.3389/fevo.2015.00020
Sterner B. & Elliot S. 2023. How data governance principles influence participation in biodiversity science. Science as culture. https://doi.org/10.1080/09505431.2023.2214155
Todman L.C. et al., 2023 ‘Small Data’ for big insights in ecology. Trends in Ecology and Evolution https://doi.org/10.1016/j.tree.2023.01.015
Urban M.C. et al., 2021 Designing a Platform for Projecting and Protecting Global Biodiversity. BioScience https://doi.org/10.1093/biosci/biab127
Wilkinson M. D. et al., 2017. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data. https://doi.org/10.1038/sdata.2016.18
Livres
Borgman C.L. (Traduction de Charlotte Matoussowsky), 2020. Qu'est-ce que le travail scientifique des données ? Big data, little data, no data. Encyclopédie numérique. DOI : 10.4000/books.oep.14692
Recknagel F., Michener W.K. et al. 2018. Ecological Informatics : Data management and knowledge discovery. Springer Cham. 3ième édition. 482 pages. https://doi.org/10.1007/978-3-319-59928-1
Rapports - Synthèses
Delavaud A. et al. 2014. État des lieux et analyse des paysages des observatoires français de recherche sur la biodiversité. Expertise et synthèse de la Fondation pour la Recherche sur la Biodiversité. Disponible en ligne sur www.fondationbiodiversite.fr/etat-des-lieux-et-analyse-des-paysages-des-observatoires-francais-de-recherche-sur-la-biodiversite/
Mahé S., Marlin C. et al. 2020 Livre Blanc sur les infrastructures françaises de recherche du domaine des sciences du système Terre et de l’environnement - Vision stratégique d’AllEnvi 2020-2030. Paris, France : AllEnvi, 116 p. Disponible en ligne sur https://www.allenvi.fr/wp-content/uploads/2022/06/Livre_blanc_Infrastructures_2020-2030.pdf
Ministère de l'Ensignement Supérieur et de la Recherche. 2022. Feuille de route nationale des infrastructures de recherche. disponible en ligne sur https://www.enseignementsup-recherche.gouv.fr/sites/default/files/2022-03/feuille-de-route-nationale-des-infrastructures-de-recherche---2021-v2--17318.pdf
Norvez O., Milon T., Pamerlon S., Archambeau A.S., Bouix T., Cheminée O., Le Bras Y., Robert S., Vinet C., 2022. Comprendre, partager et ré-utiliser les données de biodiversité : Note explicative sur la complémentarité des systèmes d’information SIB - SINP - GBIF - PNDB. PatriNat (OFB-MNHN-CNRS-IRD) - Centre d’expertise et de données sur le patrimoine naturel. 23 pages. Etalab- 2.0.https://mnhn.hal.science/mnhn-04296424/document