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The dataset includes 30-second video clips from both thermal infrared and RGB cameras, featuring six consecutive hourly recordings captured during the spring and autumn periods of both years at each river site. These video samples were used to derive surface flow velocities using Large-Scale Particle Image Velocimetry (LSPIV) (Muste et al., 2008) and Space-Time Image Velocimetry (STIV) (Fujita et al., 2007) techniques and were validated against subsequent Acoustic Doppler Current Profiler (ADCP) measurements. In addition to the video data, the dataset contains a complete series of hourly river discharge estimates derived using the STIV technique. These discharge time series span the full ice-free channel seasons of 2023 and 2024 for both rivers. Daily discharges were computed from the hourly estimates and compared against national hydrological monitoring data (Ympäristö.fi, 2025). In addition, the following data is provided: Processed results of surface flow velocity and discharge computations, 1-dimensional bathymetry profiles obtained from ADCP depth measurements, Photogrammetric camera models and search lines for processing in Hydro-STIV software, Processed daily water level and discharge data from national hydrological monitoring stations, Flow velocity measurements obtained with ADCP. These datasets support the photogrammetric input required for the image velocimetry techniques (LSPIV and STIV) and facilitate comparative analyses with conventional flow measurement methods. Software used in processing: Hydro-STIV (version v124) – Used for flow velocity and discharge computation. Available at: https://www.hydrosoken.co.jp/en/hydrostiv/. A free 30-day trial is available upon request. PIVLab (version v2.58) – Used for surface flow velocity estimation using the LSPIV technique. Openly available at: https://www.pivlab.de/ RIVeR (version v2.6) – Used for the rectification of image-derived flow velocity fields. Available at: https://doi.org/10.1016/j.cageo.2017.07.009 Acknowledgements: The work was funded by The European Union – NextGenerationEU Recovery instrument (RRF) through Research Council of Finland projects Hydro RI Platform (346167) and Green-Digi-Basin (347703). This research has been conducted with Flagship Programme funding granted by the Research Council of Finland for Digital Waters [DIWA] Flagship (decision no. 359248). Also work was funded by Research Council of Finland via DefrostingRivers project (338480). This work was a part of the Ministry of Education and Culture’s Doctoral Education Pilot under Decision No. VN/3137/2024-OKM-6 (Digital Waters [DIWA] Doctoral Education Pilot related to the DIWA Flagship [decision no. 359248] funded by the Research Council of Finland's Flagship Programme). We thank the following personnel for assisting in the field on 12.4.2023, 6.9.2023, 9.4.2024 and 3.9.2024 at Vantaanjoki Pitkäkoski site: Virpi Pajunen, Reeta Vaahtera, Walter Box and Tuure Takala. Also Erik van Rooijen and Joshua Johnson assisted on 20.5.2023, 22.9.2023, 18.5.2024 and 19.9.2024 for gathering the data at Oulankajoki site. We also thank for Oulanka Research station of the University of Oulu for assisting with the installation and maintenance of the Oulankajoki static camera system. Also we thank HSY (Vantaanjoki Pitkäkoski) and Metsähallitus (Oulankajoki) for allowing the building of the systems at their premises. References: Muste, M., Fujita, I., & Hauet, A. (2008). Large‐scale particle image velocimetry for measurements in riverine environments. Water Resources Research., 46(4). https://doi.org/10.1029/2008WR006950 Fujita, I., Watanabe, H., & Tsubaki, R. (2007) Development of a non-intrusive and efficient flow monitoring technique: The Space Time Image Velocimetry (STIV). Int J River Basin Man, 5(2), 105–114. https://doi.org/10.1080/15715124.2007.9635310 Ympäristö.fi. River discharge observations. https://wwwi3.ymparisto.fi/i3/paasivu/ENG/2023/Virtaama/Virtaama.htm. Accessed 14.1.2025."},"field_of_science":[{"id":"be6042ba-672d-4271-88a9-836126561f67","url":"http://www.yso.fi/onto/okm-tieteenala/ta1171","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Geosciences","fi":"Geotieteet","sv":"Geovetenskaper"}}],"infrastructure":[],"issued":"2025-06-26","keyword":[],"language":[],"metadata_owner":{"id":"829302c3-f5d4-4d20-9ec3-5148bcf385cc","organization":"aalto.fi","admin_organization":"aalto.fi"},"other_identifiers":[],"persistent_identifier":"doi:10.5281/zenodo.15744753","pid_generated_by_fairdata":false,"projects":[],"provenance":[],"relation":[],"remote_resources":[{"title":{"en":"Data in remote location","fi":"Aineisto ulkoisessa palvelussa","sv":"Material i en extern tjänst","und":"Data in remote 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Beyond its reproductive functions, E2 impacts neurotransmitter systems such as dopamine (DA). Vertically aligned carbon nanofibers (VACNFs) have shown good sensitivity, selectivity against ascorbic acid (AA) and uric acid (UA), biocompatibility, and reduced fouling in DA sensing. In this study, we explore the use of Ti-Ni-CNF electrodes with CNFs grown for 5 min and 30 min for the direct electrochemical co-detection of E2 and DA. The longer growth time led to a 142% increase in average CNF length and a 36% larger electroactive surface area. In E2 detection, the electrodes demonstrate a wide linear range of 0.05–10 µM and sensitivity of 0.016 and 0.020 µA/µM for Ti-Ni-CNF-5 min and Ti-Ni-CNF-30 min, respectively. The sensor performance remains largely unaffected even in the presence of other steroid hormones such as progesterone and testosterone. Co-detection of equimolar E2 and DA shows promising peak separation of 0.34 ± 0.01 V and repeatability after 10 measurements. A notable improvement in the E2/DA peak current ratio, from 0.53 ± 0.07 to 0.81 ± 0.16, was achieved with the increased CNF length. Our results demonstrate the influence of adsorption sites in electrochemical detection, especially for analytes such as E2 and DA that both rely on adsorption for oxidation. While detecting small and fluctuating physiological concentrations remains a challenge, these findings can be used in choosing and fabricating electrode materials for more accurate and accessible continuous hormone measurements, including the possibility of multianalyte sensing platforms. 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