Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2024
Forfattere
Theresa Weigl Jorunn Børve Emily Follett Melissa Magerøy Carl Gunnar Fossdal Hanne Larsen Siv Fagertun RembergSammendrag
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Forfattere
Geir-Harald StrandSammendrag
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Sammendrag
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Anne MuolaSammendrag
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Sigridur Dalmannsdottir Marit Jørgensen Helga Amdahl Kristoffer Herland Hellton Odd Arne RognliSammendrag
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Forfattere
Berit Arheimer Christophe Cudennec Attilio Castellarin Salvatore Grimaldi Kate V. Heal Claire Lupton Archana Sarkar Fuqiang Tian Jean-Marie Kileshye Onema Stacey Archfield Günter Blöschl Pedro L. Borges Chaffe Barry F.W. Croke Moctar Dembélé Chris Leong Ana Mijic Giovanny M. Mosquera Bertil Nlend Adeyemi O. Olusola Maria J. Polo Melody Sandells Justin Sheffield Theresa C. van Hateren Mojtaba Shafiei Soham Adla Ankit Agarwal Cristina Aguilar Jafet C.M. Andersson Cynthia Andraos Ana Andreu Francesco Avanzi Ryan R. Bart Alena Bartosova Okke Batelaan James C. Bennett Miriam Bertola Nejc Bezak Judith Boekee Thom Bogaard Martijn J. Booij Pierre Brigode Wouter Buytaert Konstantine Bziava Giulio Castelli Cyndi V. Castro Natalie C. Ceperley Sivarama K. R. Chidepudi Francis H. S. Chiew Kwok P. Chun Addisu G. DagnewSammendrag
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Forfattere
Maria Medina Marino P. Reyes‑Martín Laura Levy Alba Lázaro‑González Enrique Andivia Peter Annighöfer Farhah Assaad Jürgen Bauhus Raquel Benavides Henrik Böhlenius Vito E. Cambria María D. Carbonero Jorge Castro Akaki Chalatashvili Donato Chiatante Claudia Cocozza Sofa Corticeiro Dagnija Lazdina Giovanbattista De Dato Michele De Sanctis Jovana Devetaković Lars Drossler Lenka Ehrenbergerová Peter Ferus Lorena Gómez‑Aparicio Arndt Hampe Kjersti Holt Hanssen Berthold Heinze Marcin Jakubowski María N. Jiménez Branko Kanjevac Jan J. Keizer Ivona Kerkez‑Janković Marcin Klisz Wojciech Kowalkowski Klaus Kremer Johan Kroon Dario La Montagna Jelena Lazarević Emanuele Lingua Manuel E. Lucas‑Borja Adrian Łukowski Magnus Löf Paula Maia Paola Mairota Alberto Maltoni Barbara Mariotti Antonin Martinik Rafaella Marzano Luis MatiasSammendrag
Key message We provide data on seedlot germination potential—a key trait related to regeneration—of 12 oak spe‑ cies. Germination was tested at the University of Granada following international protocols with 8985 acorns from 93 batches and 16 countries across Europe. Data on germination probability, acorn origin, mass, and moisture content measured on another 4544 acorns are available at https://doi.org/10.30827/Digibug.87318. Associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/a742c6d8-bc37-4ca2- 8b81-2447c5a8858d. Keywords Acorn, Germination test, Seedlot germination potential, Seed mass, Seed moisture, Seed viability
Sammendrag
Raman spectroscopy is a powerful and non-invasive analytical method for determining the chemical composition and molecular structure of a wide range of materials, including complex biological tissues. However, the captured signals typically suffer from interferences manifested as noise and baseline, which need to be removed for successful data analysis. Effective baseline correction is critical in quantitative analysis, as it may impact peak signature derivation. Current baseline correction methods can be labor-intensive and may require extensive parameter adjustment depending on the input spectrum characteristics. In contrast, deep learning-based baseline correction models trained across various materials, offer a promising and more versatile alternative. This study reports an approach to manually identify the ground-truth baselines for eight different biological materials through extensively tuning the parameters of three classical baseline correction methods, Modified Multi- Polynomial Fit (Modpoly), Improved Modified Multi-Polynomial Fitting (IModpoly), and Adaptive Iteratively Reweighted Penalized Least Squares (airPLS), and combining the outputs to best fit the training data. We designed a one-dimensional Transformer (1dTrans) tailored to fit Raman spectral data for estimating their baselines, and evaluated its performance against convolutional neural network (CNN), ResUNet, and three aforementioned parametric methods. The 1dTrans model achieved lower mean absolute error (MAE) and spectral angle mapper (SAM) scores when compared to the other methods in both development and evaluation of the manually labeled original raw Raman spectra, highlighting the effectiveness of the method in Raman spectra pre-processing.
Forfattere
Hiva Asadikia Seyed Habibollah Mosavi Tannaz Alizadeh Ashrafi Michael R. Reed Shraddha Hegde Hamed Najafi AlamdarloSammendrag
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Forfattere
Arne HermansenSammendrag
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