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Dataset of Drone Distance Estimation
No full textDATASET MIGRATED FROM FIGSHARE: Drone Datasets for Distance Estimation using YOLOv11 and Regression ModelsThis datasets consists of:YOLOtrainedKongsbergDataset: images of DJI Air 2S drone taken outside of USN Kongsberg campus for pretrained YOLO model.DistanceEstimationKongsbergDataset: images of DJI Air 2S drone taken outside of USN Kongsberg campus arranged in distance folder for regression model.YOLOtrainedVestfoldDataset: images of DJI Mavic 3 Enterprise drone taken outside of USN Vestfold campus for pretrained YOLO model.DistanceEstimationKongsbergDataset: images of DJI Mavic 3 Enterprise drone taken outside of USN Vestfold campus arranged in distance folder for regression model.The images of datasets number 1 and 2 are the same, but the dataset number 2 is arranged in the folders based on the distance when the camera took the picture. Similarly with images is datasets number 3 and 4. The datasets with name started "YOLOtrained..." are grouped into three categories train 60%, valid 20%, and test 20% for the purpose of YOLO training. Also, there are annotation files that represent the location of the drone and its bounding box in the image using YOLO format.The datasets are used for distance estimation research that is submitted to the Remote Sensing journal under title: "Drone Distance Estimation using YOLOv11 and Regression Models for Drone Detection Service in U-Space System".</p
Large-scale literature-derived compound-level dataset of organic chemicals in pyrolysis oils from diverse feedstocks
No full textDATASET MIGRATED FROM FIGSHARE: The dataset provides a curated, study-linked compilation of organic compounds reported in pyrolysis oils from biomass and other waste-derived feedstocks. It is derived from a systematic literature search and screening process and currently includes approximately 8,700 compound-level records and over 3,000 unique compounds extracted from about 100 peer-reviewed studies published between 2008 and 2025.Each compound record is linked to its originating study and annotated with feedstock class, pyrolysis technology, catalyst (where applicable), separation/analytical technique, and reported yield or abundance, enabling users to explore how product slates vary across feedstocks, process conditions, and upgrading strategies. A complementary study-level file documents the PRISMA-based selection process and provides the bibliographic metadata for all included publications.</p
Net annual increment (of AGB) (NAI) map for 2020
No full textThis dataset provides a high-resolution (10 m) pan-European map of forest Net annual increment (of AGB) (NAI) for the year 2020, along with an accompanying standard deviation layer. It is part of the PathFinder collection of forest structure maps, which integrates Sentinel-2 satellite imagery, auxiliary geospatial layers, and National Forest Inventory (NFI) data to deliver detailed forest attribute predictions across Europe. The map supports applications in forest management, biomass estimation, carbon accounting, and ecological modeling. For methodology and data integration details, see the documentation dataset of the PathFinder collection (https://doi.org/10.18710/OEYKEG) and the following publication: Miettinen, J., Breidenbach, J. et al. (2025). PathFinder's High-Resolution Pan-European Forest Structure Maps: An Integration of Earth Observation and National Forest Inventory Data. Zenodo. https://doi.org/10.5281/zenodo.17107267
Replication Data for: Strain-induced manipulation of non-collinear antiferromagnets
No full textReplication Data for the article "Strain-induced manipulation of non-collinear antiferromagnets" published in Physical Review B (2025). The files ‘Fig4a_S1’, ‘Fig4a_S2’ and ‘Fig4a_S3’ contain the x, y and z coordinate of the macrospins S1, S2 and S3, respectively, in Figure 4a. The files ‘Fig4b_Phitrajectory’, ‘Fig4b_Psitrajectory’and ‘Fig4b_Thetatrajectory’ contain the evolution of the nautical angles Phi, Psi and Theta, respectively, in Figure 4b. The files ‘Fig5_Output10’, ‘Fig5_Output15’ and ‘Fig5_Output20’ contain the switching times for strains of 1.0%, 1.5% and 2.0% respectively, in Figure 5. In each file from left to right, the columns represent the Temperature, Switching Time, Standard Deviation, and the Standard Error
Search strategies for a review article on task shifting in eye care in high-income countries
No full textDATASET MIGRATED FROM FIGSHARE: Search strategies for a review article on task shifting in eye care in high-income countries are described.</p
Replication Data for: Predicting Stress in Russian using Modern Machine-Learning Tools
No full textThis dataset consists of a TSV file with five columns of data originating in Zaliznyak's Grammar and Dictionary (1977). The data was programmatically scraped from Giella project data (Moshagen et al., 2013) by Spektor (2021). From Spektor (2021), the data was one of four sources in their RusLex application. Once scraped from there, only symbols were removed.
The Russian word data is preserved from the original in Cyrillic. The last column contains abbreviated morphological features in English (e.g. "V" for verb, "N" for noun, "Fem" for feminine, "Cmpr" for comparative, "Impf" for imperfect). The often many features are separated by semicolons.
Stress codes were derived for each word that represented stress placement: If the stressed vowel was at the end of the word a stress code of 0 signifying oxytone stress was assigned. Next, counting from the end of the word, the penultimate stress was given a 1, meaning a stress on the paroxytone.
Next, if the antepenultimate syllable contained the stress, the word was assigned a 2, meaning a stress on the proparoxytone. The script continued until a stress code was assigned with the following exceptions: a -1 is assigned for those words without explicit stress markers.
The columns in the resultant TSV are: the word without stress markers, the word with stress markers, the derived stress code, the lemma, and all morphological features.
The dataset contains over 300,000 words from Zaliznyak (1977) with many repeated words that have unique morphological features. Please see the README or the paper for a full description of the dataset: https://academicworks.cuny.edu/gc_etds/4974.
References:
Moshagen, Sjur N., Tommi Pirinen, and Trond Trosterud. (2013). Building an open-source development infrastructure for language technology projects. In Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013), (pp. 343–352).
Spektor, Y. (2021). Detection and morphological analysis of novel Russian loanwords (Master’s thesis, CUNY Graduate Center, New York, NY). Retrieved from
https://academicworks.cuny.edu/gc_etds/4572/
Zaliznyak, A.A. (1977). Grammatičeskij slovar’ russkogo jazyka. Slovoizmenenie
[A grammatical dictionary of Russian: Inflection]. Moscow: Russkij jazyk</p
Background data for: "The instantaneous structure of a turbulent wall-bounded flow influenced by freestream turbulence: streamwise evolution"
No full textThis data set contains planar Particle Image Velocimetry measurement fields for the experiments described in the article titled "The instantaneous structure of a turbulent wall-bounded flow influenced by freestream turbulence: streamwise evolution" (doi:10.1017/jfm.2024.1008).
The experiments were conducted in a water channel at the Norwegian University of Science and Technology. The setup includes an active grid to control freestream conditions. To analyze the evolution of the flow, the boundary layer was tested at four different streamwise locations for three grid sequences with freestream turbulence intensities up to 10.9%. Careful preprocessing was implemented to ensure high accuracy and minimal uncertainties.
This work was funded by the Research Council of Norway (see funding information): Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the Research Council of Norway. The granting authority cannot be held responsible for them.</p
GNSS Scintillation Data (60 s) at Ny-Ålesund in 2024
No full textThis data set contains phase and amplitude scintillation data at 60 seconds time resolution at Ny-Ålesund, Svalbard.
The measurements were collected by the University of Bergen using a NovAtel GPStation-6 global navigation satellite system receiver. The measurements include signals from GPS, GLONASS, and GALILEO at different frequencies. These data are used for research on space weather disturbances in the polar ionosphere.
A detailed description of the data structure and format is gathered in the documentation data set: Oksavik, Kjellmar, 2020, "Documentation of GNSS Total Electron Content and Scintillation Data (60 s) at Svalbard", DataverseNO, https://doi.org/10.18710/EA5BYX
This data set is part of a larger collection: Oksavik, Kjellmar, 2020. "The University of Bergen Global Navigation Satellite System Data Collection". DataverseNO. https://doi.org/10.18710/AJ4S-X394.
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Raw data for "Migration of endocrine and metabolism disrupting chemicals from plastic food packaging."
No full textThis dataset contains the raw data of the chemical analysis described in the associated publication, namely the mass spectral data of the plastic extracts and migrates analyzed using UPLC-QTOF-DDA. For experimental details on the nontarget analysis, see the Supporting Information of the publication. The data uploaded here are the Waters .raw files converted to the open mzML format which can be assessed using text editors. Each file contains the individual mass spectral data of a sample. They are labelled with the sample name corresponding to the samples described in Table 1 of the publication. The sample names denote the polymer type of the plastic product. PB refers to the procedural blanks, MeOH refers to the methanol used as a solvent, EtOH10 (10% ethanol), EtOH50 (50% ethanol) and H2O refer to the food simulants used for the migration. POS and NEG indicate the ionization mode. BeforeSPE refers to samples analyzed prior to the solid phase extraction
Replication Data for: Hydroclimate intensification likely aided glacier survival on Svalbard in the Early Holocene
No full textThis dataset includes the results of analyses presented in the study "Hydroclimate intensification likely aided glacier survival on Svalbard in the Early Holocene" by Auer et al. 2025. The study presents evidence of glacier survival on Svalbard during the warmer-than-present Holocene Thermal Maximum (HTM). The analyses were performed on sedimentary records from two lakes (Berglivatnet, Lakssjøen) which receive meltwater from the Åsgardfonna ice cap.The cores were extracted in the summer of 2021 using a Nesje corer (piston cores) and a UWITEC gravity coring system. Chronological data shows they cover ~14 ka.
The data are organized per paper figure per .txt file and include magnetic susceptibility, chronology, X-ray Fluorescence, Computed Tommography and grain size analysis. Additional details may be found in the appended readme file.</p