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FIGURE 4. A in Testing the impact of two key scan parameters on the quality and repeatability of measurements from CT scan data

FIGURE 4. A video moving through slices of a pteropods shell scanned at 500 ms exposure time, with five xray radiographs averaged per view and an overall scan time of 75 minutes. There are streak artefacts perpendicular to the shell edge that are likely caused by beam hardening or shell movement during the scan. For video file, see https://palaeo-electronica.org/content/2020/ 2923-investigating-ct-scan-quality.

Fig. 3 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures

Fig. 3. Image illustrating under water visibility at the beginning of the snorkeling excursion and the presence of tourists (Lima, 2008).

Fig. 1 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures

Fig. 1. Map showing the location of the study area: Sucuri River (C), município of Bonito area (B), Brazil (A). Adapted from Miranda & Coutinho (2004).

Fig. 8 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures

Fig. 8. Variation of behaviour patterns between before (8h00) and after (9h00) the first disturbance of tourists in the river (mean and SEM) for M. bonita; (a) Tourism and (b) No Tourism. Lighter bars = 8h00; darker bars = 9h00 (Mann-Whitney U-test). N = 70; * p <0.05; ** p <0.01.

Fig. 9 in Impact of ecotourism on the fish fauna of Bonito region (Mato Grosso do Sul State, Brazil): ecological, behavioural and physiological measures

Fig. 9. Variation (mean and SEM) of cortisol responses to restraining stress in Moenkhausia bonita individuals at the No Tourism and Tourism sites (Mann-Whitney U-test, N = 6; Z = -2.95; p <0.005).

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Figure 3 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast

Figure 3. The dependence of the speed wind at a height Figure 4. In-situ MS data breakdown scheme for a

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Figure 5 in Validation of Wind Speed Calculated on Satellite Altimetry Data by Measurements on Weather Stations Located Along the White Sea Coast

Figure 5. The dependence of the correlation coefficient between in-situ wind speed at the WS and remote sensing data on the orientation angle of the main quadrants (a) and their position relative to the White Sea coastline (b).

Dataset for acceleration measurements at the research bridge openLAB in Bautzen, Germany - Change of dynamic behavior in the concrete hardening process

This data set was collected during the construction phase of the openLAB in Bautzen, Germany during the period from 22.01.2024 - 30.04.2024. It includes acceleration measurements and temperature measurements that record the dynamic behavior of the bridge over a period of 49 days (from 22.01.2024 to 11.03.2024; the remaining data cannot be uploaded due to the Zenodo upload restriction, but can be released on request). The detailed documentation of the data set can be found in the file "Bartels, Dunkel, Marx_2024_Documentation.pdf". The documentation describes the structure, the applied monitoring system and the collected data in detail.

Measurements of Methanethiol (MeSH) and dimethyl sulfide (DMS) in surface seawater during 2022 at the monthly sampling observatory of Blanes Bay Microbial Observatory.

Global emissions of methanethiol are highly uncertain and the drivers influencing its seawater concentrations are quasi unexplored. Here we try to address this gap in our understanding by contributing new seasonally resolved seawater measurements. Mediterranean surface seawater sampling at the Blanes Bay Microbial Observatory was carried out monthly during 2022. January, March and April were not sampled due to technical difficulties. Seawater was hand-collected in glass sampling bottles around 11:00 am each day. The samples were kept in the dark and analysed at the ICM-CSIC for MeSH and DMS with the Vocus-PTR coupled to SFCE within 3 h of sample collection. This dataset contains MeSH and DMS surface seawater concentrations and relevant auxiliary data (chlorophyll a, sea surface temperature and sea surface salinity). Sea surface temperature and salinity were determined using a CTD profiler Model SD204 (SAIV A/S). The chlorophyll from the Blanes 2022 timeseries was measured on a fluorometer (10AU Turner fluorometer). Details on the measurement methodology can be found here: Wohl, C.; Güell-Bujons, Q.; Castillo, Y.M.; Calbet, A.; Simó, R. Volatile Organic Compounds Released by Oxyrrhis marina Grazing on Isochrysis galbana. Oceans 2023, 4, 151-169. https://doi.org/10.3390/oceans4020011   Definitions of acronyms, site abbreviations, or other project-specific designations: Lat=latitude (negative indicates south) Lon=longitude (negative indicates west ) Hourlybin = The mean measured concentration during each hour is shown, 30 min either side of the listed time. The timestamp indicates sampling time in UTC, expressed as  DD/MM/YYYY HH:MM MeSH_nM= methanethiol surface seawater concentration in nM, defined as nmol dm^(-3) DMS_nM= dimethyl sulfide surface seawater concentration in nM, defined as nmol dm^(-3) fluo = chlorophyll a concentration in mg m^(-3) SST= sea surface temperature in degrees Celsius SSS= sea surface salinity in practical salinity units (PSU)

Figure 9 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 9. Simulation results for the lift forces acting on the Model A and B tags (top panel) in constant flow (5.6 m/s) as a function of orientation (–20º to 180º). Measured results as a function of orientation (–20º to 90º) in constant 5.6 m/s flow are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).

Figure 8 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 8. Simulation results for the drag forces acting on the Model A and B tags (top panel) in constant flow (5.6 m/s) as a function of orientation (–20º to 180º). Measured results as a function of orientation (–20º to 90º) in constant 5.6 m/s flow are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).

Figure 4. CFD simulation results for Models A in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 4. CFD simulation results for Models A (panels A and C) and B (panels B and D) in steady 5.6 m/s flow. The blue, yellow, and green regions are areas of reduced flow speed that generate forces on the tags. The upper panels show the flow speed over a horizontal cross-section at the tag midline. The lower panels show flow speed over a vertical cross-section at the centerline of the tag. The improved flow around Model B is evident in the smaller magnitude of blue coloration in the wake behind the tag.

Figure 2 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 2. An illustration of the Model A tag in the computational domain used for the simulations of all tag designs. The fluid flow is from left to right and representative orientations of the tag to the flow are shown at the bottom of the figure.

Figure 7 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 7. Simulation results for the lift forces acting on the Model A and B tags (top panel) in variable flow (0.25–10 m/s). Measured results in variable flow speed (1–5.6 m/s) are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).

Figure 10 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 10. Experimental load testing of a Model A tag attached at three test sites on a common dolphin cadaver using four silicone suction cups. Applied force vs. calculated total cup attachment force at the four silicone suction cups are shown for lift (bottom panel) and drag (top panel) loading. The average pressure difference for the four cups is also shown on the right hand axis, where 1 atmosphere is approximately 100 kPa. Forces were applied via a line attached to the tag by pulling either perpendicular to the body (lift) or parallel (drag). The curves end where the cup attachment failed or the cups began to slide. The average of two trials at each site is shown in each panel.

Figure 6 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 6. Simulation results for the drag forces acting on the Model A and B tags (top panel) in variable flow (0.25–10 m/s) with fixed orientation (0º). Measured results in variable flow speed (1–5.6 m/s) are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).

Volatile organic compounds (BVOCs and AVOCs) that were measured at the Eastern Mediterranean coast (Ramat Hanadiv, Israel)

Volatile organic compounds (BVOCs and AVOCs), which were measured at the Eastern Mediterranean coast (Ramat Hanadiv, Israel) in 2015 using a PTR-ToF-MS, and have been published. The publication is available at: <a href="https://acp.copernicus.org/articles/20/12741/2020/acp-20-12741-2020.html" target="_new" rel="noreferrer">https://acp.copernicus.org/articles/20/12741/2020/acp-20-12741-2020.html</a>

Fig. 2 in Measuring and explaining disagreement in bird taxonomy

Fig. 2. Schematic representation of some of the taxon concepts recognized by the different lists within the Tyto alba complex. Full lines represent concepts with species rank; dashed lines concept at subspecies rank. A. General overview. Not all concepts recognized as subspecies are shown. B. Example of concept conflict: the subgroups 'insularis' and 'nigrescens' belong in T. alba according to CLEM and HM; in T. furcata according to IOC (because they split T. furcata from T. alba sensu lato); but in T. glaucops according to BL, a taxon that all lists recognize at species-rank. C. Example of rank conflict. All lists recognize the same taxon concept, but CLEM, HM and IOC recognize it at specieslevel (T. deroepstorffi), while BL recognizes it at subspecies level (T. alba deroepstorffi).

Fig. 5 in Measuring and explaining disagreement in bird taxonomy

Fig. 5. Prevalence of conflict in bird lists. a. Prevalence of taxonomic conflict and agreement across the 12 730 concepts listed by at least one of the four lists. b. Prevalence of classificatory conflict and agreement across the 76 380 list-relations. Agreement relations are in shades of blue and conflict relations are in shades of green.

Fig. 4 in Measuring and explaining disagreement in bird taxonomy

Fig. 4. Directed acyclic graph (DAG) for the effort analysis. This DAG shows the assumed causal relations between disagreement, the proxies for research effort, and the three factors typically assumed to influence disagreement (species concepts, effort and diversification).