891 to 900 of 1,003 Results
May 11, 2021 - Spawning behavior of Arctic charr
Egeland, Torvald B.; Folstad, Ivar; Nordeide, Jarle Tryti, 2021, "Video recordings of spawning behavior of Arctic charr; date: 2016-09-20; spawning ground: 3; time: afternoon; camera no: 3", https://doi.org/10.18710/BIVY51, DataverseNO, V1
This dataset contains video recordings of spawning behavior of Arctic charr (Salvelinus alpinus) in Lake Fjellfrøsvatnet (69°08′N 19°34′E), Troms, Northern Norway. The recordings were made with camera no. 3 at spawning ground 3 in the afternoon of 20 September 2016. A description of the data structure and format is gathered in the documentation dat... |
May 6, 2019
Konstantinell, Aelita Gloria Virginia, 2019, "Replication Data for: Comparative analysis of microRNA expression profiles of exosomes derived from polyomavirus-negative and –positive Merkel cell lines by next generation sequencing", https://doi.org/10.18710/BDMYIK, DataverseNO, V1, UNF:6:L5jfP55zcyTozMfCij6ttw== [fileUNF]
This dataset is about the differentially expressed exosomal microRNA between MCPyV-negative and -positive MCC cell lines. Abstract: MicroRNAs (miRNAs) are small non-coding RNAs responsible for post-transcriptional regulation of gene expression through interaction with messenger RNAs (mRNAs). In the past few years, evidence of the presence of cellul... |
Jan 24, 2018 - NMDC Node UiT
UiT The Arctic University of Norway, 2018, "Scenery photos without the frame (2001)", https://doi.org/10.18710/YA2PCC, DataverseNO, V1, UNF:6:9a32GTExInMkejEOTVwzPQ== [fileUNF]
Contains photographs documenting under-water scenery and fauna across Svalbard. The data base contains photographs documenting under-water scenery and fauna across Svalbard and mainland at depths from ca. 5 to 30 m at non-marked, non-revisited locations. These non-scaled photographs were taken by divers. These photographs were taken in support of t... |
Jan 24, 2018 - NMDC Node UiT
UiT The Arctic University of Norway, 2018, "Scenery photos without the frame (2007)", https://doi.org/10.18710/W8RIGH, DataverseNO, V1, UNF:6:N4bjXqvrE3ukPI1SjWHVqg== [fileUNF]
Contains photographs documenting under-water scenery and fauna across Svalbard. The data base contains photographs documenting under-water scenery and fauna across Svalbard and mainland at depths from ca. 5 to 30 m at non-marked, non-revisited locations. These non-scaled photographs were taken by divers. These photographs were taken in support of t... |
Apr 22, 2021
Stamm, Johann, 2021, "Programming code for article "Radar imaging with EISCAT 3D"", https://doi.org/10.18710/QRDET2, DataverseNO, V2, UNF:6:hamTKJhUq9zuYrdRESkUUg== [fileUNF]
Programming code for article "Radar imaging with EISCAT 3D" A new incoherent scatter radar called EISCAT 3D is being constructed in Northern Scandinavia. It will have the capability of producing volumetric images of ionospheric plasma parameters using aperture synthesis radar imaging. This study uses the current design of EISCAT 3D to explore the t... |
May 10, 2021 - Spawning behavior of Arctic charr
Egeland, Torvald B.; Folstad, Ivar; Nordeide, Jarle Tryti, 2021, "Video recordings of spawning behavior of Arctic charr; date: 2016-09-18; spawning ground: 3; time: morning; camera no: 1", https://doi.org/10.18710/WIXGJZ, DataverseNO, V1
This dataset contains video recordings of spawning behavior of Arctic charr (Salvelinus alpinus) in Lake Fjellfrøsvatnet (69°08′N 19°34′E), Troms, Northern Norway. The recordings were made with camera no. 1 at spawning ground 3 in the morning of 18 September 2016. A description of the data structure and format is gathered in the documentation datas... |
Ströhl, Florian; Jadhav, Suyog S.; Ahluwalia, Balpreet Singh; Agarwal, Krishna; Prasad, Dilip K., 2022, "Supplementary data for "Object detection neural network improves Fourier ptychography reconstruction"", https://doi.org/10.18710/BBU6JD, DataverseNO, V1
This dataset holds the trained deep learning models for our paper "Object detection neural network improves Fourier ptychography reconstruction". The results produced in the paper can be replicated through the use of these models in conjunction with the inference scripts provided in our GitHub repository: External Link. Abstract High resolution mic... |
Oct 11, 2023
Belibassakis, Kostas; Perera, Lokukaluge Prasad; Adnan, Muhammad, 2023, "Demonstrating the dynamic wing technology at real sea conditions using 10-12m long, self-propelled ship model", https://doi.org/10.18710/VXGPII, DataverseNO, V1
The Seatech project is devoted to the demonstration of the dynamic using large scale models tested wing at sea and the engine technology testing in relevant environment. The project tasks include the design of full-size wing, the system Life-cycle cost analysis (LCCA) and using the advanced data analytics for the evaluation of retrofitability and m... |
May 28, 2021
Koehl, Jean-Baptiste, 2020, "Replication data for: Devonian–Carboniferous collapse and segmentation of the Billefjorden Trough, and Eurekan inversion–overprint and strain partitioning and decoupling along inherited WNW–ESE-striking faults", https://doi.org/10.18710/EAZDNV, DataverseNO, V2
High-resolution versions of the figures and supplements of the Koehl et al. (2020) manuscript entitled Devonian–Carboniferous collapse and segmentation of the Billefjorden Trough, and Eurekan inversion–overprint and strain partitioning and decoupling along inherited WNW–ESE-striking faults, which could not be attached to the manuscript itself due t... |
Jun 25, 2021
Opstad, Ida S.; Godtliebsen, Gustav; Sekh, Arif Ahmed, 2021, "Replication Data for: Physics based machine learning for sub-cellular segmentation in living cells", https://doi.org/10.18710/IDCUCI, DataverseNO, V1
Abstract: Segmenting sub-cellular structures in living cells from fluorescence microscopy images is a ground truth (GT) hard problem. The microscope’s 3-dimensional blurring function, finite optical resolution due to light diffraction, finite pixel resolution, and complex morphological manifestations of the structures, all contribute to GT-hardness... |
