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Replication Data for: Metaphor elicitation project
No full textThis dataset contains replication and supplementary data for a project about metaphor elicitation, where informants were specifically prompted to produce a metaphor in written texts about a topic that was meaningful to them.
The main study related to this dataset took place in a Norwegian upper secondary class, and involved an interenvention over the course of a semester, with elicitation being conducted twice (at the start and at the end of the semester). A second elicitation study involving an online survey took place in Finland with college students (also two elicitataion points).
This dataset contains the entire texts produced by the Norwegian and Finnish informants from all four elicitation points (the elicited metaphors and justifications, if any).
The documents here also provide the information and consent form, metadata questionaire, classroom exercises, elicitation tasks, and interview guides created for the Norwegian intervention
Drone-based mapping of surging glaciers in Rindersbukta, Svalbard
No full textThis database contains drone-based mapping data of three surging glaciers in Rindersbukta, Svalbard, Norway. The dataset was generated using a structure-from-motion (SfM) method using drone-based imagery. The data was processed with Agisoft Metashape and the processed data consists of digital elevation models (DEMs) in georeferenced .TIF file format, orthomosaic maps in georeferenced .TIF, .JPG, and .PNG file format, and textured 3D models in .STL and .OBJ/.MTL/.JPG file format. In addition, a process report in archived .PDF file format is included for each dataset. Mapping was conducted with a DJI Mavic 2 Pro Enterprise. The mapping area covers the crevassed glacier fronts. Data collection was conducted during Spring 2022 (19-23.04.2022), Spring 2023 (23-24.03.2023), and Spring 2024 (10.03.2024). The following glaciers are mapped: Scheelebreen (2022, 2023), Vallåkrabreen (2022, 2023, 2024), and Paulabreen (2022). For Vallåkrabreen, two different datasets are available, where the "bulge" datasets only contains the area of the surge buldge, whereas the other dataset contains the entire glacier area
Replication Data for: Goose grubbing and warming suppress summer net ecosystem CO2 uptake differentially across high-Arctic tundra habitats
No full textEnvironmental changes, such as climate warming and higher herbivory pressure, are altering the carbon balance of Arctic ecosystems; yet how these drivers modify the carbon balance among different habitats remains uncertain. This dataset is used to investigate how spring goose grubbing and summer warming – two key environmental-change drivers in the Arctic – alter CO2-fluxes in three tundra habitats varying in soil moisture and plant-community composition.Where: CO2-flux data were gathered from a full-factorial randomized-block experiment simulating spring goose grubbing and summer warming in high-Arctic Svalbard.
When: CO2-flux data were gathered at each of three sampling occasions (early, peak, and late summer) in summer 2016 and summer 2017.Data collection and processing: CO2-fluxes were assessed using a closed-system made of a clear acrylic chamber (25 cm × 25 cm area × 35 cm height), including a fan for air mixing, connected through an air pump (L052C-11, Parker Corp, Cleveland, Ohio, USA; ~1 l min-1 flow rate) to a CO2 infrared gas analyzer (LI-840A, LICOR, Lincoln, Nebraska, USA). We calculated CO2-fluxes for each measurement by fitting linear regression models based on the ideal gas law.How to use this dataset: All analyses of this dataset were run in the R statistical and computing environment v. 4.3.0 (https://www.r-project.org). To use this dataset, download the most recent version of R and R studio and import the dataset in your workspace. Additional information on how to analyze this data is given in the related publication
GNSS Scintillation Data (60 s) at Hopen in 2023
No full textThis data set contains phase and amplitude scintillation data at 60 seconds time resolution at Hopen, 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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Data for Remote Sensing: Biomass Change Estimated by TanDEM-X Interferometry and GEDI in a Tanzanian Forest
No full textMapping and quantification of forest biomass change are key for forest management and for forests’ contribution to the global carbon budget. We explored the potential of covering this with repeated acquisitions with TanDEM-X. We used an eight-year period in a Tanzanian miombo woodland as a test case, having repeated TanDEM-X elevation data for this period and repeated field inventory data. We also investigated the use of GEDI space–LiDAR footprint AGB estimates as an alternative to field inventory. The map of TanDEM-X elevation change appeared to be an accurate representation of the geography of forest biomass change. The relationship between TanDEM-X phase height and above-ground biomass (AGB) could be represented as a straight line passing through the origin, and this relationship was the same at both the beginning and end of the period. We obtained a similar relationship when we replaced field plot data with the GEDI data. In conclusion, temporal change in miombo woodland biomass is closely related to change in InSAR elevation, and this enabled both an accurate mapping and quantification wall to wall within 5–10% error margins. The combination of TanDEM-X and GEDI may have a near-global potential for estimation of temporal change in forest biomass
Aurora Vent Field (82.9N, Fram Strait) surface sediment geochemistry, grain size and mineralogy
No full textSedimentological and geochemical datasets of sediment cores collected from Aurora Vent Field (82.9N) in the Fram Strait during HACON21 (MC-148, BlaC-01, BlaC-03, GC-191, GC-193) and HACON19 (MUC-33, MUC-34, MUC-37, MUC-39) expeditions. The sediment cores were collected via gravity coring (GC), multicoring (MC, MUC), and ROV-guided blade coring (BC) from the R/V Kronsprins Hakon. Gravity coring was conducted with a 6 m long steel barrel deployed which contains a PVC liner with an inner diameter of 10 cm. Here, we report the results for some laminated intervals in GC-191 (core lenght = 215 cm) and GC-193 (core lenght = 255 cm). These sites are located at various distances from the active vents. We used a KC Denmark DK8000 multicorer hosting up to six transparent plastic liners with a diameter of 10 cm and length of 70 cm. We report the results from surface (0-5 cm) sediment of the multicore MC-148 (25 cm) and some deeper samples from MUC-33, MUC-34, MUC-37, MUC-39. ROV-guided coring operations at the vent site were conducted with the ROV Aurora, owned and operated by REV Ocean (Norway). The blade cores are picked up from a compartment on the ROV by its manipulator arm and pushed into the seabed. Blade cores can reach a maximum depth of 32 cm below sea floor, have a thickness of 10 cm and a width of 25 cm. Here we report the data from the upper 5 cm of blade cores BlaC-01 (28 cm) and BlaC-03 (17 cm) and two deeper samples from BlaC-01 (19-20 cm and 25-26 cm). Sampling locations are reported in the separate the Core_location.txt file
Replication Data for: Automatic time in bed detection from hip-worn accelerometers for large epidemiological studies: The Tromsø Study
No full textDataset description
16 participants wore an Actigraph w-GT3X on their right hip together with two consumer-based wearables, a Polar Vantage (Polar Electro Oy, Kempele, Finland) watch and an Oura (Oura Health Oy, Oulo, Finland]) ring. This data, as summarized in this repository, has been used to validate an algorithm to detect time in bed. For more information, see the publication's abstract below.
This dataset consists of 20 files: "00_ReadMe.txt", "calibration_error.txt", "data_protection_impact_assessment.pdf", "wearable_data.csv" and 16 raw acceleration data files ("participant00.csv to participant15.csv").
"00_ReadMe.txt": Contains a detailed information about the dataset
"calibration_error.txt": Contains the estimated calibration errors, correction coefficients, and success information for the 16 raw acceleration data files.
"data_protection_impact_assessment.pdf": sets out the legal and ethical grounds for open publication of the dataset.
"wearable_data.csv": Contains daily sleep duration values from a Polar watch and the Oura ring for the 16 participants
Each of the 16 raw acceleration data files (participantXX.csv") contains the triaxial raw acceleration data at a 100hz sampling frequency.
Article abstract
Accelerometers are frequently used to assess physical activity in large epidemiological studies. They can monitor movement patterns and cycles over several days under free-living conditions and are usually either worn on the wrist or the hip. While wrist-worn accelerometers have been frequently used to additionally assess sleep and time in bed behavior, hip-worn accelerometers have been widely neglected for this task due to their primary focus on physical activity. Here, we present a new method with the objective to identify the time in bed to enable further analysis options for large-scale studies using hip-placement like time in bed or sedentary time analyses. We introduced new and accelerometer specific data augmentation methods, such as mimicking a wrongly worn accelerometer, additional noise, and random croping, to improve training and generalization performance. Subsequently, we trained a neural network model on a sample from the population-based Tromsø Study and evaluated it on two additional datasets. Our algorithm achieved an accuracy of 94% on the training data, 92% on unseen data from the same population and comparable results to consumer-wearable data obtained from a demographically different population. Generalization performance was overall good, however, we found that on a few particular days or participants, the trained model fundamentally over- or underestimated time in bed (e.g., predicted all or nothing as time in bed). Despite these limitations, we anticipate our approach to be a starting point for more sophisticated methods to identify time in bed or at some point even sleep from hip-worn acceleration signals. This can enable the re-use of already collected data, for example, for longitudinal analyses where sleep-related research questions only recently got into focus or sedentary time needs to be estimated in 24h wear protocols. </p
Background data for: Latent-variable modeling of ordinal outcomes in language data analysis
No full textThis dataset contains tabular files with information about the usage preferences of speakers of Maltese English with regard to 63 pairs of lexical expressions. These pairs (e.g. truck-lorry or realization-realisation) are known to differ in usage between BrE and AmE (cf. Algeo 2006). The data were elicited with a questionnaire that asks informants to indicate whether they always use one of the two variants, prefer one over the other, have no preference, or do not use either expression (see Krug and Sell 2013 for methodological details). Usage preferences were therefore measured on a symmetric 5-point ordinal scale. Data were collected between 2008 to 2018, as part of a larger research project on lexical and grammatical variation in settings where English is spoken as a native, second, or foreign language. The current dataset, which we use for our methodological study on ordinal data modeling strategies, consists of a subset of 500 speakers that is roughly balanced on year of birth.
Abstract: Related publication
In empirical work, ordinal variables are typically analyzed using means based on numeric scores assigned to categories. While this strategy has met with justified criticism in the methodological literature, it also generates simple and informative data summaries, a standard often not met by statistically more adequate procedures. Motivated by a survey of how ordered variables are dealt with in language research, we draw attention to an un(der)used latent-variable approach to ordinal data modeling, which constitutes an alternative perspective on the most widely used form of ordered regression, the cumulative model. Since the latent-variable approach does not feature in any of the studies in our survey, we believe it is worthwhile to promote its benefits. To this end, we draw on questionnaire-based preference ratings by speakers of Maltese English, who indicated on a 5-point scale which of two synonymous expressions (e.g. package-parcel) they (tend to) use. We demonstrate that a latent-variable formulation of the cumulative model affords nuanced and interpretable data summaries that can be visualized effectively, while at the same time avoiding limitations inherent in mean response models (e.g. distortions induced by floor and ceiling effects). The online supplementary materials include a tutorial for its implementation in R
Replication Data for: Evaluation of the cytotoxic and antimicrobial potential of epiphytic biomass obtained from Laminaria hyperborea biorefinery side-streams
No full text This dataset describes the calculations and the related methods used to determine the EC50 values-cytotoxicity against 5 cell lines (MOLM13, PC3, MCF7, NRK, H9C2) of the crude extracts and flash fractions of Laminaria's epiphytes.
In order to assess the cytotoxicity, we performed the WST-1 cell proliferation assay, a colorimetric assay, using 96 well plates [1]. By measuring the UV absorbance, we calculated the % cell survival and then, the EC50 (concentration that reduces the viability of cells by 50%) using four-parameter regression analysis (dose response curve). We also performed one-way statistical analysis to determine differences between the extracts and between the fractions.
[1] Diaz, A.E.C.; Herfindal, L.; Andersen, H.L.; Fossen, T. Cytotoxic Natural Products Isolated from Cryptogramma Crispa (L.) R. Br. Molecules 2023, 28, 7723, https://doi.org/10.3390/molecules28237723 </p
Replication data for "Caledonian reactivation and reworking of Timanian thrust systems and implications for latest Mesoproterozoic to mid-Paleozoic tectonics and magmatism in northern Baltica"
No full textReplication data for "Caledonian reactivation and reworking of Timanian thrust systems and implications for latest Mesoproterozoic to mid-Paleozoic tectonics and magmatism in northern Baltica". The dataset includes the following:
-Figure 1a: Figure 1. ( a) Elevation map of the Barents Sea, northern Norway and northwestern Russia showing major structural elements from the Norwegian Offshore Directorate (thin white lines) and major fault trends in the Barents Sea (Timanian; yellow lines). The location of ( a) is shown as a red rectangle in the upper inset map and the location of ( b) as a black rectangle. The basemap and the upper inset are from Jakobsson et al. (2012). A Polar Stereographic projection was used (datum: WGS84). Abbreviations: AFC: Asterias Fault Complex; AKTW: Alta–Kvænangen tectonic window; BaFZ: Baidaratsky Fault Zone; BB: Bjørnøya Basin; BFC: Bjørnøyrenna Fault Complex; BP: Bjarmeland Platform; CTF: Central Timan Fault; ETF: East Timan Fault; FG: Forlandsundet Graben; FP: Finnmark Platform; FSB: Fingerdjupet Sub-Basin; FTB: Fold-and-Thrust Belt; HB: Harstad Basin; HfB: Hammerfest Basin; HFC: Hoop Fault Complex; JFC: Jason Fault Complex; KCFZ: Kongsfjorden–Cowanodden fault zone; KDFZ: Kinnhøgda–Daudbjørnpynten fault zone; LH: Loppa High; MB: Maud Basin; MFC: Måsøy Fault Complex; MFZ: Molloy Fracture Zone; MH: Mercurius High; NB: Nordkapp Basin; ND: Norsel Dome; NH: Norsel High; OB: NP: Nordkinn Peninsula; Olga Basin; PSP: Polhem Sub-Platform; RFC: Ringvassøya Fault Complex; RP: Rybachi Peninsula; SaD: Samson Dome; SeFZ: Senja Fracture Zone; SD: Sredni Peninsula; SFZ: Spitsbergen Fracture Zone; SH: Stappen High; SIP: Seiland Igneous Province; SISZ: Sørøya–Ingøya shear zone; SkB: Sørkapp Basin; SR: Senja Ridge; SRFZ: Sredni–Rybachi Fault Zone; SvB: Sørvestnaget Basin; SvD: Svalis Dome; TFFC: Troms–Finnmark Fault Complex; TKFZ: Trollfjorden–Komagelva Fault Zone; TyB: Tiddlybanken Basin; TøB: Tromsø Basin; VH: Veslemøy High; VKSZ: Vimsodden–Kosibapasset Shear Zone; VP: Varanger Peninsula; WTF: West Timan Fault.
-Figure 1b: ( b) Geological map of northern Norway and the southern Barents Sea showing the main onshore and offshore structures and tectonostratigraphic units. The map is after Koehl et al. (2019) with updates after Siedlecka and Siedlecki (1967), Siedlecki (1980), Townsend et al. (1986), Rice (1994), Kirkland et al. ( 2005; 2006a; 2007a; 2007b; 2008a; 2008b), Indrevær et al. (2013), Corfu et al. (2014), Koehl et al. (2018a), Faber ( 2018, manuscript 3), and Roberts and Siedlecka (2022). Abbreviations: AFC – Asterias Fault Complex; AsW: Altenes tectonic window; AW: Alta–Kvænangen tectonic window; Bf: Båtsfjorden; BFC: Bjørnøyrenna Fault Complex; Bj: Bjørnøya; Bn: Båsnæringsfjellet; Bv: Berlevåg; DP: Digermulen Peninsula; GL: Gjesvær Low; Hj: Hjelmsøya; Ig: Ingøya;Kf: Kongsfjorden; Kv: Kvaløya; LG: Lillefjord Granite; Lk: Laksefjorden; Ln: Langfjorden; LVF: Langfjorden–Vargsundet fault; Ma: Magerøya; MFC: Måsøy Fault Complex; NFC: Nysleppen Fault Complex; NP: Nordkinn Peninsula; Pf: Porsangerfjorden; PP: Porsanger Peninsula; RA: Ragnarokk Anticline; Rf: Rolvsøya fault; Rk: Reinøykalven; RG: Revsneshamn Granite; RLFC: Ringvassøya–Loppa Fault Complex; Rv: Rolvsøya; RW: Repparfjord–Komagfjord tectonic window; Sf: Syltefjorden; Sff: Syltefjordfjellet; SFZ: Senja Fracture Zone; SISZ: Sørøya–Ingøya shear zone; Sk: Stikonjargga Peninsula; sNB – southwesternmost Nordkapp basin; SP: Sværholt Peninsula; Sø: Sørøya; TFFC: Troms–Finnmark Fault Complex; TKFZ: Trollfjorden–Komagelva Fault Zone; Tn: Tanafjorden; TyB: Tiddlybanken Basin; VP: Varanger Peninsula; VVFC: Vestfjorden–Vanna Fault Complex.
-Figure 2: Figure 2: Tectonostratigraphic chart and regional correlations in the study area. Abbreviations: E: Eidvågeid migmatite; L: Lillefjord Granite; R: Revsneshamn Granite; Ø: Øksfjord Gabbro.
-Figure 3a-b: Figure 3. Overview of the (a) 2D and (b) 3D seismic reflection database uses in the present study. The maps also display the depth surfaces (in milliseconds TWT) of the main two Timanian thrusts mapped on the Finnmark Platform.
-Figure 4a: Interpreted (up) and uninterpreted (down) seismic reflection data on the western Finnmark Platform. The location of the data is shown in Figure 7. ( a– c) NE–SW-trending seismic lines illustrating the moderately NNE-dipping geometry of the Sørøya–Ingøya shear zone.
-Figure 4b: Interpreted (up) and uninterpreted (down) seismic reflection data on the western Finnmark Platform. The location of the data is shown in Figure 7. ( a– c) NE–SW-trending seismic lines illustrating the moderately NNE-dipping geometry of the Sørøya–Ingøya shear zone.
-Figure 4c: Interpreted (up) and uninterpreted (down) seismic reflection data on the western Finnmark Platform. The location of the data is shown in Figure 7. ( a– c) NE–SW-trending seismic lines illustrating the moderately NNE-dipping geometry of the Sørøya–Ingøya shear zone.
-Figure 4d: Interpreted (up) and uninterpreted (down) seismic reflection data on the western Finnmark Platform. ( d) NW–SE-trending seismic line showing major thickness variations of the Sørøya–Ingøya shear zone related to Devonian–Carboniferous core complex exhumation ( Koehl et al., 2018a) and to Caledonian reworking into NE–SW-striking folds.
-Figure 5a: Zoom in interpreted (up) and uninterpreted (down) seismic data on the western Finnmark Platform. The location of the data is shown in Figure 6. ( a) Zoom in a NE–SW-trending seismic line showing numerous SSW-verging folds and associated brittle thrusts with top-SSW offsets within the western portion of the Sørøya–Ingøya shear zone. Triangular-shaped packages in the upper left corner are interpreted as deformed foreland basin deposits.
-Figure 5b: Zoom in interpreted (up) and uninterpreted (down) seismic data on the western Finnmark Platform. ( b) Zoom in a NE–SW-trending seismic line showing sigmoidal packages of Z-shaped reflections respectively interpreted as antiformal thrust stacks and duplexes within the Sørøya–Ingøya shear zone.
-Figure 5c: Zoom in interpreted (up) and uninterpreted (down) seismic data on the western Finnmark Platform. ( c) Zoom in a NE–SW-trending seismic line showing the eastern portion of the Sørøya–Ingøya shear zone, which bends back into a NNE-dipping orientation. Most folds within the shear zone display a vergence to the south-southwest indicating top-SSW movements.
-Figure 5d: Zoom in interpreted (up) and uninterpreted (down) seismic data on the western Finnmark Platform. ( d) Zoom in a NW–SE-trending seismic line showing symmetric folds in shallow basement rocks in a major NE–SW-striking syncline possibly consisting of rocks equivalent to the Magerøy Nappe (MN; upper right corner), northwest-verging folds in the northwestern limb of the major syncline (left hand-side), and southeast-verging fold structures in the southeastern limb of the major syncline (lower right corner).
-Figure 6a: Interpreted seismic reflection data on the eastern Finnmark Platform. The location of the data is shown in Figure 7. ( a) Intra-basement N–S- to NE–SW-striking Caledonian folds. The data show dominantly symmetric folds. Note that the URU reflection coincides with the Top-basement reflection here.
-Figure 6b: Interpreted seismic reflection data on the eastern Finnmark Platform. ( b) NNE-dipping ductile shear zone consisting of planar mylonitic surfaces and SSW-verging folds indicating transport direction towards the south-southwest. Notice the mild reactivation of the shallow portion of the thrust by a late Paleozoic listric normal fault. A different color scheme was used to enhance the contrast and better highlight the structures described.
-Figure 7: Map showing the depth surfaces (in milliseconds TWT) of the main two Timanian thrusts on the Finnmark Platform. Notice the correlation of major synforms and antiforms observed in the field onshore and on onshore–offshore magnetic data (plain and dashed pink lines) with folding of the Timanian thrusts on the Finnmark Platform in map view identified via seismic mapping (depth surfaces). The map includes major faults from Figure 1. For abbreviations, see Figure 1b.
-Figure 8: Bathymetric and topographic data onshore Finnmark and nearshore fjords. The map shows major brittle faults, mafic dykes and sills, ductile fabrics (notably folded bedding surfaces), and glacial features. The location of seismic line BSS01-205 ( Figure 4c) is shown as a thick black line. The location of Figure9a and c–e is shown as black frames.
-Figure 9a-c: Zoom in bathymetric data in (a) the Nordkinn Peninsula, (b) west of the Nordkinn Peninsula in 3D with view towards the northeast, (c) the Repparfjord–Komagfjord tectonic window.
-Figure 9d-e: Zoom in bathymetric data in (d) southeast of Rolvsøya, and (e) Sørøya.
-Figure 10a-b: ( a) Interpreted and ( b) uninterpreted magnetic data onshore–nearshore northern Norway. The location of Figure 11a–d is shown as black frames in ( b).
-Figure 11a-d: Zoom in interpreted and uninterpreted tilt derivative data from Nasuti et al. (2015) in (a) Magerøya, (b) Porsangerfjorden and the Sværholt Peninsula, (c) the Sværholt Peninsula and Nordkinn Peninsula, and (d) in Syltefjorden and Syltefjordfjellet.
-Figure 12a-b: ( a) Interpreted and ( b) uninterpreted tilt-derivative data onshore–nearshore northern Norway. The location of Figure 13a–c is shown as black frames in ( b).
-Figure 13a-b: Zoom in interpreted and uninterpreted tilt derivative data from Nasuti et al. (2015) in (a) Sørøya, (b) Porsangerfjorden, the Sværholt Peninsula, and the Nordkinn Peninsula.
-Figure 13c: Zoom in interpreted and uninterpreted tilt derivative data from Nasuti et al. (2015) in (c) the Digermulen Peninsula and the Varanger Peninsula.
-Figure 14a-b: ( a) Interpreted and ( b) uninterpreted tilt-derivative data from Gernigon et al. (2014). The data show a correlation between fold structures in the field and on seismic data with magnetic anomalies both onshore northern Norway and on the Finnmark Platform offshore.
-Figure 15: Interpreted (upper inset) and uninterpreted (lower inset) magnetic data from Gernigon et al. (2014) showing tens of kilometers wide anomalies interpreted as major folds.
-Figure 16a-d: Summary model detailing the tectonic evolution of northern Norway and the southern Barents Sea from the latest Mesoproterozoic to the mid-Paleozoic. ( a) Deposition of sedimentary rocks of the Kalak Nappe Complex, possibly in a foreland basin associated with the Grenvillian–Sveconorwegian Orogeny at 1050–910 Ma and their partial migmatization and intrusion by tonalites at ca. 980 Ma. ( b) Normal faulting at 825–735 Ma and felsic magmatism at 840–828 Ma (granite and pegmatite, e.g., Lillefjord and Revsneshamn granites) in the Kalak Nappe Complex during the breakup of Rodinia. ( c) Top-SSW contraction during the Timanian Orogeny at 650–550 Ma and intrusion of the Seiland Igneous Province in a back-arc basin at 580–520 Ma. ( d) Top-southeast thrusting and reworking (folding) and partial reactivation of NNE-dipping Timanian thrusts during the Caledonian Orogeny in the early–mid Paleozoic