Find research datasets worth reusing

Biomass estimation of winter wheat using the Light Use Efficiency model in Bavaria (Atlas) in LandKlif project

This dataset shows the predicted biomass (g/m2) of winter wheat (WW) using the Light Use Efficiency (LUE) model for Bavaria in 2019. The LUE model uses satellite data (Landsat-8, MODIS) and climate data (temperature and solar radiation) to calculate the plant biomass. The crop yield is predicted and validated at district level using LfStat data. The validation results show an R2 of 0.82 with an RMSE of 5.46 dt/ha.  This dataset is conducted under LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html">Bavarian State Ministry of Science and the Arts</a> within the <a href="https://www.bayklif.de/">Bavarian Climate Research Network (bayklif)</a>.  Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.

opencc-by-4.0May 2024View details →

Supporting data for "Granularity of model input data impacts estimates of carbon storage in soils"

The exchange of carbon between the soil and the atmosphere is an important factor in climate change.  Soil organic carbon (SOC) storage is sensitive to land management, soil properties, and climatic conditions, and these data serve as key inputs to computer models projecting SOC change.  Farmland has been identified as a sink for atmospheric carbon, and we have previously estimated the potential for SOC sequestration in agricultural soils in Vermont, USA using the Rothamsted Carbon Model.  However, fine spatial-scale (high granularity) input data are not always available, which can limit the skill of SOC projections.  For example, climate projections are often only available at scales of 10s to 100s of km2.  To overcome this, we use a climate projection dataset downscaled to <1 km2  (~18,000 cells).  We compare SOC from runs forced by high granularity input data to runs forced by aggregated data averaged over the 11,690 km2 study region.  We spin up and run the model individually for each cell in the fine-scale runs and for the region in the aggregated runs factorially over three agricultural land uses and four Global Climate Models.   In this repository are the downscaled climate input data that drive the RothC model, as well as the model outputs for each GCM.

Network-wide Emissions Estimation Using the Macroscopic Fundamental Diagram

This is the dataset for the NCST project of Network-wide Emissions Estimation Using the Macroscopic Fundamental Diagram. Estimation of emissions and energy consumption using MOVES (Motor Vehicle Emission Simulator) model is cumbersome for large-scale network and intractable for real-time analysis and control purposes. This project aims to develop a simplified method for estimation of emissions and energy consumption in large-scale networks using the macroscopic fundamental diagram (MFD). In the proposed method, the network will be divided up into a number of homogeneous regions and the emissions and energy consumption from all links inside each region will be approximated by MFD-based variables in the MOVES model. Later, the developed method will be used to propose real-time traffic control policies optimizing the network-wide emissions and energy consumption.

opencc-by-4.0May 2024View details →

Crary Ice Rise region dh/dt estimates

v2.0 of this dataset includes: <ol> <li>All surface-elevation change (dh/dt) data from ICESat, CryoSat-2, and ICESat-2 altimetry missions necessary to reproduce figures and analysis from Verboncoeur et al. (2024) ('*dhdt_smb');</li> <li>a file containing x,y positions of the ad-hoc reference tracks formed around ICESat ground tracks ('xy_is_masked.csv');</li> <li>a folder containing delineated boundaries used in analysis ('SHAPES.zip');</li> <li>folders containing raw subsetted ICESat data ('IS_data.zip') and a folder containing <a href="https://hub.cryointhecloud.com/hub/login?next=%2Fhub%2F">CryoCloud</a> scripts for downloading ICESat-2 data ('IS2_processing_cryocloud.zip')</li> </ol>

opencc-by-4.0Apr 2024View details →

Composition-based estimates of the thermal properties of New Zealand basement rocks: data used for calculations and figures

This archive contains four files with compositional data (mineralogical and geochemical) used to estimate thermal conductivity and heat production in New Zealand basement terranes (Kirkby et al., 2024). HPR_source_data.csv - contains K, Th, and U concentrations and calculated heat production rates. Longitude - longitude (New Zealand Geodetic Datum) Latitude - latitude(New Zealand Geodetic Datum) K_wtpct - K concentration in weight percent Th_ppm - Th concentration in ppm U_ppm - U concentration in ppm Heat_production_uW/m3 - heat production calculated from K, Th and U concentrations, in microWatts per meter cubed Terrane - name of basement terrane that sample has been assigned to Source - source of data, either PetLAB (Strong et al. 2016), PMAP (Turnbull ref) or separate compilation for this study Reference - Reference citation for data as listed in PetLAB/PMAP or added for this study   TC_from_majors_PMAP.csv - contains thermal conductivity estimated from major element geochemistry (Kirkby et al., 2024) using method of, Jennings et al. (2019). Columns are as follows: Collection - collection that sample is contained in within the PetLAB database (Strong et al., 2016) Collection_Number - sample number within the collection above Sample_ID - unique sample ID in PetLAB Analysis_ID - unique analysis ID in PetLAB Longitude - longitude (New Zealand Geodetic Datum) Latitude - latitude(New Zealand Geodetic Datum) **_wtpct - major oxide concentration in weight percent (normalised to non-volatile component) Terrane - name of basement terrane that sample has been assigned to Analysis_Method - analysis method for major oxide concentrations Thermal_conductivity_pred - calculated estimate of thermal conductivity based on major oxide composition, W/mK Bib_ref - analysis source reference, direct copy of Bib_ref field in PetLAB Reference - analysis source reference, direct copy of Reference field in PetLAB TC_from_modal_mineralogy.csv - contains thermal conductivity estimated from modal mineralogy (Kirkby et al., 2024). Columns are as follows: Longitude - longitude (New Zealand Geodetic Datum) Latitude - latitude(New Zealand Geodetic Datum) Analysis_ID - unique analysis ID in PetLAB Collection - collection that sample is contained in within the PetLAB database (Strong et al., 2016) Collection_ID - sample number within the collection above Subsample - in the case of thermal conductivity, sometimes two samples were measured. This column distinguishes the two measurements Mineralogy_method - method of determining mineralogy, either point count or QEMSCAN Quartz - percentage of quartz in sample Olivine - percentage of olivine in sample Pyroxene - percentage of pyroxene in sample Other - percentage of other minerals in sample Thermal_conductivity_grain_pred - estimated thermal conductivity (W/mK) from mineralogy (Kirkby et al, 2024), W/mK Thermal_conductivity_dry_measured - measured dry thermal conductivity (W/mK) for samples reported by Sanders et al (2024), W/mK Porosity_measured_pct - measured porosity (percent) for samples reported by Sanders et al (2024) Thermal_conductivity_grain_measured - grain thermal conductivity (W/mK) calculated from measured dry thermal conductivity and porosity Terrane - name of basement terrane that sample has been assigned to Bib_ref - analysis source reference, direct copy of Bib_ref field in PetLAB Reference - analysis source reference, direct copy of Reference field in PetLAB TC_from_normative_mineralogy.csv Longitude - longitude (New Zealand Geodetic Datum) Latitude - latitude(New Zealand Geodetic Datum) Analysis_ID - unique analysis ID in PetLAB Collection - collection that sample is contained in within the PetLAB database (Strong et al., 2016) Collection_ID - sample number within the collection above Mineralogy_method - method of determining mineralogy, either Mesonorm or CIPWnormhb (CIPW norm with hornblende) Quartz - percentage of quartz in sample Olivine - percentage of olivine in sample Pyroxene - percentage of pyroxene in sample Other - percentage of other minerals in sample Thermal_conductivity_grain_pred - estimated thermal conductivity (W/mK) from mineralogy (Kirkby et al, 2024) Thermal_conductivity_dry_measured - measured dry thermal conductivity (W/mK) for samples reported by Sagar et al (2022) Porosity_measured_pct - measured porosity (percent) for samples reported by Sagar et al (2024) Thermal_conductivity_grain_measured - grain thermal conductivity (W/mK) calculated from measured dry thermal conductivity and porosity Terrane - name of basement terrane that sample has been assigned to Bib_ref - analysis source reference, direct copy of Bib_ref field in PetLAB Reference - analysis source reference, direct copy of Reference field in PetLAB   tc_by_terrane.csv Contains thermal conductivity, standard deviation, and standard error of thermal conductivity estimates by terrane as shown in Figure 7 of Kirkby et al (2024)   hpr_by_terrane.csv Contains thermal conductivity, standard deviation, and standard error of thermal conductivity estimates by terrane as shown in Figure 7 of Kirkby et al (2024).   References Jennings, S., Hasterok, D., & Payne, J. (2019). A new compositionally based thermal conductivity model for plutonic rocks. Geophysical Journal International, 219(2), 1377-1394. https://doi.org/10.1093/gji/ggz376 Kirkby, A., N. Mortimer, R. Funnell, M. Sagar, A. Seward, K. Faure and F. Sanders (2024). Composition-based estimates of the thermal properties of New Zealand basement rocks, New Zealand Journal of Geology and Geophysics. Sagar, M. W., Funnell, R., Randell, K., Faure, K., Seward, A., Sanders, F., & Stratford, W. R. (2022). Physical properties of Te Riu-a-Māui / Zealandia crustal rocks: Reconnaissance study and future research.  (GNS Science Internal Report 2022/05.  Sanders, F., Seward, A., Sagar, M., & Faure, K. (2024). Thermal Properties of Zealandia Basement Rocks: results of Thermal Conductivity Scanner measurements 2023.  Lower Hutt. (GNS Science Report.  Strong, D. T., Turnbull, R. E., Haubrock, S., & Mortimer, N. (2016). Petlab: New Zealand’s national rock catalogue and geoanalytical database. New Zealand Journal of Geology and Geophysics, 59(3), 475-481. https://doi.org/10.1080/00288306.2016.1157086

opencc-by-4.0Jan 2024View details →

Data & scripts - Estimates on the possible annual seismicity of Venus

Data and scripts to reproduce the work in Van Zelst et al. (2024): 'Estimates on the possible annual seismicity of Venus'. See the file 'description_data&scripts.pdf' for more details.

Fig. 4 in Body mass estimation in Triassic cynodonts from Argentina based on limb variables

Fig. 4. Logarithmic scale representation of the body mass ranges of adult forms of amniotes Chañares and Ischigualasto formations, Triassic of Argentina. Body masses for Cynognathia obtained in this work (black silhouettes) compared with other amniotes known from these formations (white silhouettes). Cynodonts Chiniquodon sanjuanensis Martínez and Forster, 1996, and Probainognathus jenseni Romer, 1970; the dicynodont Dinodontosaurus brevirostris Cox, 1968; and archosauriforms Lagerpeton chanarensis Romer, 1971a, Chanaresuchus sp. (includes C. bonapartei Romer, 1971b, and C. ischigualastensis Trotteyn, Martínez and Alcober, 2012), paracrocodylomorphs and the dinosaur Herrerasaurus ischigualastensis Reig, 1963. The horizontal length of the rectangles represents the body mass range for the genus. The silhouettes are not to scale.

Fig. 2 in Body mass estimation in Triassic cynodonts from Argentina based on limb variables

Fig. 2. Measurements used in this work based on Toledo et al. (2014), as illustrated using the 3D model of the left humerus and femur of Andescynodon mendozensis Bonaparte, 1969 (PVL 3894-1) from the Cerro de las Cabras Formation (upper Anisian), Villa de Potrerillos, Mendoza province, Argentina. A. Humerus in anterior (A1) and distal (A2) views. B. Femur in anterior (B1) and distal (B2) views. Scale bars 10 mm.

Fig. 1. Cynognathia phylogeny plotted onto a in Body mass estimation in Triassic cynodonts from Argentina based on limb variables

Fig. 1. Cynognathia phylogeny plotted onto a stratigraphic scale showing the known observed temporal ranges of taxa. Taxa studied in this contribution are in bold. Modified from Hendrickx et al. (2020). Thick dashed lines indicate separation between periods; thin dotted lines indicate separation between ages.

Fig. 5 in Body mass estimation in Triassic cynodonts from Argentina based on limb variables

Fig. 5. Stacked area chart of animal size (after values of the skull length for Therapsida and skull or limb bone lengths, when skull is not preserved, for Archosauromorpha) from the Argentinean Triassic units: Cerro de La Cabras, Río Seco de la Quebrada, Chañares, and Ischigualasto formations. A. Therapsida (Cynodontia plus Dicynodontia). B. Amniota (Therapsida plus Archosauromorpha). Small, maximum skull length below 150 mm; medium, skull length 150–250 mm; large, maximum skull length greater than 250 mm. Fm., Formation.

Fig. 3 in Body mass estimation in Triassic cynodonts from Argentina based on limb variables

Fig. 3. Cynognathians studied in this work (all in anterior views). A. Left humerus of Exaeretodon argentinus Cabrera, 1943 (PVL 2554) from the Ischigualasto Formation (upper Carnian), Hoyada de Ischigualasto, San Juan, Argentina. B. Right humerus (mirrored) of Cynognathus crateronotus Seeley, 1895 (PVL 3859) from the Río Seco de la Quebrada Formation (lower Carnian), Puesto Viejo, Mendoza province, Argentina. C. Left humerus of Andescynodon mendozensis Bonaparte, 1969 (PVL 3894-1) from the Cerro de las Cabras Formation (upper Anisian), Villa de Potrerillos, Mendoza province, Argentina. D. Left humerus of Massetognathus pascuali Romer, 1967 (PVL 5444) from the Chañares Formation (lower Carnian), Campo de Talampaya, La Rioja province, Argentina. E. Right humerus (mirrored) from Pascualgnathus polanskii Bonaparte, 1966 (MLP 65-VI-18-1) from the Río Seco de la Quebrada Formation (lower Carnian), Puesto Viejo, Mendoza province, Argentina. Scale bars 10 mm.

Dataset for Securing the Sky: Detecting Aircraft Location Drifting through Cross-Checking Receiver-Based Estimated and Received ADS-B Trajectories

Dataset utilized for our tested data in our accepted paper "Securing the Sky: Detecting Aircraft Location Drifting through Cross-Checking Receiver-Based Estimated and Received ADS-B Trajectories"

Supplement to "Discriminating non-stationary flood hazard effects via probabilistic estimation of sparse residuals from rescued and rated stage–discharge data"

This supplement contains the data and script associated with “Sparse hydrometric data rescue for exploratory analyses of conveyance-driven flood hazard trends and controls,” which has been submitted to a journal for consideration. This supplement is deposited on Zenodo.   Code, Data, and Attribute Descriptions   Uploaded are five directories (indicated in bold font, with their contents detailed below) containing input data and various outputs of the analyses performed for our case study on the Pulangi River at Lumayong (Philippines). Please consider this description equivalent to an omnibus README file for the deposited files. Note that we collected and rescued the hydrometric data herein from the archives of the Water Projects Division (WPD) of the Philippine Department of Public Works and Highways (DPWH).   ●      Pln_R_code contains (1) Pln_RC.R, the R script file, and (2) 240601_Pln_RC.RData, which stores objects generated from the script based on the last execution (on 1 June 2024). The script consists of admittedly too many lines of code (e.g., for data wrangling, formal analyses, and producing figures used in the manuscript) that should have been split into multiple .R files. Apologies. Please be guided by the outline and the comments and kindly reach out lest issues with the code arise.   ●      Pln_GH_PDFs contains the scanned gaugekeeper’s reports of gauge heights (“stages”).    ○      The name of each file varies. For example, Pln-GH-1100.pdf contains daily stage readings for all months in the year 2011. But, if the last two digits are not zeroes, as in Pln-GH-1204.pdf, they refer to the month of that year; in this case, for example, the PDF file contains stage data for the month of April in the year 2012. ○      Each scanned sheet contains sub-daily (with readings at “AM,” “NOON,” and “PM”) and mean daily stages for a given month. ○      Also indicated are the gaugekeeper’s remarks on the daily weather (e.g., “fair,” “cloudy”). Under inclement weather, the gaugekeeper would note the duration of rainfall and its intensity and might, at times, record extra stage readings.   ●      Pln_DM_PDFs contains the following PDF files and a sub-directory: ○      Pln-DM-<XXXXXX>.pdf contains the scanned log for each direct stage—discharge measurement, otherwise known as “gaugings.” Each file is named according to the date of gauging (YYMMDD). For example, the log for the gauging performed on 24 February 2011 can be found in the file Pln-DM-110224.pdf. Data from these gaugings are summarized in Pln-DM-filtered.csv in the Pln_In_CSVs directory. ■      The readability of each file varies based on the original quality of the original paper-format data. ■      The first page in each file contains, on the left side, a summary of the gauging data and metadata (e.g., date of measurement, number of gauging verticals or “sections” used, method of crossing or measuring the cross-section), and on the right side, the velocity—area readings at each gauging vertical. ■      The second page in each file contains the plotted cross-section of the channel at the time of measurement. ○      Pln-RC.pdf contains various other paper-format data and some annotations relevant to the historical rating at the station. ■      P. 1: The hydrographic engineer’s comment (dated 19 January 2012) on the evaluation of the discharge data, specifically detailing the periods of validity of the rating curves developed for different sub-periods of monitoring. ■      PP. 2—3: A summary table of all the gaugings performed from 1983 to 2010 whose logs were no longer retrievable (and hence not included as a DM-PDF file in this directory). ■      PP. 4—5: Plots of the official stage—discharge rating curves developed by DPWH hydrographers. ■      PP. 6—8: Rating tables used to convert daily mean stages to deterministic discharge estimates. Two of these rating tables were digitized and can be found in the Pln_RatingTables sub-directory in Pln_In_CSVs. ■      PP. 9—22: Daily stage (m) and its corresponding daily discharge (L/s) for the 2004—2010 sub-period. The stages were digitized and included in Pln-H_arch.csv in the Pln_In_CSVs directory. ○      Pln_DM_unused contains the PDF files of logs (including data and metadata) corresponding to the gaugings that were excluded from our analysis following our filtering step for gauging location consistency.   ●      Pln_In_CSVs contains the following CSV files and two sub-directories; these files were used as inputs to the R script for formal analyses: ○      Pln-H_arch.csv contains the mean daily stage values [“H_bar_arch”] (m) for every day in the 2004—2020 sub-period [“Date”] (YYYY-MM-DD). The stages in this file are already corrected for gross errors. ○      Pln-DM-filtered.csv contains the following information on the gaugings performed on the Pulangi at Lumayong (1983—2020): (i) date [“Date”] (YYYY-MM-DD); (ii) stage [“H”] (m); (iii) discharge in L/s [“Q_lps”] and m3/s [“Q_cms”]; (iv) wetted area [“A_sqm”] (m2); (v) mean flow velocity [“Vel_mps”] (m/s); (vi) channel width [“W_m”] (m); (vii) mean flow depth [“D_ave_m”] (m); (viii) location of the measurement cross-section with respect to the staff gauge, with negative values meaning downstream of the gauge and positive values meaning upstream of the gauge [“XS_loc_wrt_gage”]; (ix) maximum flow depth [“Max_Depth_m”] (m); and (x) minimum streambed elevation [“MINSBE”] (m). Note that this file includes only gaugings that passed our filtering step for measurement location consistency. ○      Pln-DM_oview.csv contains information on the temporal coverage (bounded by “Start_date” and “End_date”) of the available hydrometric data from the station archives. ○      Pln-H_sample_GrossE_corr.csv contains a sample sub-period (2017-06-15 through 2017-11-30) and the corresponding values of stages (m), uncorrected [“H_uncorrected”] and corrected for gross errors [“H_corrected”]. This CSV file was used as an input to the R script to produce one of the figures in the manuscript.  ○      Pln-XS-csv.csv contains data on the gauging transects: gauging ID [“GaugingID”]; date [“Date”] (YYYY-MM-DD); lateral distance from a fixed initial point [“Lat_distance”] (m); width of the gauging vertical [“Width_vert..m.”] (m); depth relative to the water surface [“Depth..m.”] (m); stage at the gauging vertical [“H_m_vert..m.”] (m); and elevation with respect to a fixed arbitrary datum at the station [“Elev..m.”] (m). ○      Pln_Rating_Tables contains two rating tables, Pln-DM - RatingTable_A.csv and Pln-DM - RatingTable_B.csv, prepared and used by DPWH hydrographers for converting stage values to deterministic discharge estimates for the Pulangi River at Lumayong for the 1980s—early 2000s sub-period. Each rating table contains columns for stage [“H”], discharge in L/s [“Q_lps”], and discharge in m3/s [“Q_cms”]. ○      Pln_Q_rated contains two CSV files: Pulangi.csv has the columns “YEAR”, “DAY”, and every month of the year [“JAN” through “DEC”] for the 1983—2003 sub-period, with the values under each month column indicating the deterministic discharge estimates in L/s; Pulangi_trunc.csv contains similarly formatted data, but for the 2009—2010 sub-period.   ●      Pln_Out_CSVs contains two CSV files, the primary outputs of the hydrometric data rescue effort. ○      Pln_Q_recon.csv contains the reconstructed discharge time series (1983—2020) and its associated uncertainties at the 95% credibility interval. It has the following columns: date [“Date”] (YYYY-MM-DD); daily stage [“H”] (m); lower bound of the discharge estimate [“Q_lwr”] (m3/s); median discharge estimate [“Q_med”] (m3/s); and the upper bound of the discharge estimate [“Q_upr”] (m3/s). Pln_H_recon.csv contains the reconstructed and quality-controlled stage time series (1983—2020). It has the following columns: date [“Date”] (YYYY-MM-DD); mean daily stage that has been corrected for gross errors, but not yet filtered through other quality checks [“H_bar_arch”] (m); quality check for low outliers [“Low_Outlier”] (TRUE/FALSE); quality check for flatliners [“Flatliner”] (TRUE/FALSE); difference between the stage values on day i and day i-1 [“Daily_Step”] (m); quality check for large steps [“Large_Step”] (TRUE/FALSE); and the corrected and quality-controlled mean daily stage values [“H”] (m).

opencc-by-4.0Dec 2016View details →

Fig. 1 in Microbial Respiration of Organic Carbon in Freshwater Microcosms: The Potential for Improved Estimation of Microbial CO Emission from Organically Enriched Freshwater Ecosystems

Fig. 1. Densities (ordinate) of ciliates (N × 104 L–1) black bars, and densities of bacteria (N × 109 ml–1) grey bars; for Experiment One and Experiment Two with carbon-enriched (E) and control (C) preparations (abscissa).

opencc-by-4.0Jul 2024View details →

Nanoparticle Size Estimation by Scanning Transmission Electron Microscopy and Generative AI

The "raw" directories contain unaltered simulated and experimental data. The train and val directories contain normalized data used to train the models of the manuscript. The dataframes directory contains all information about the atomic models. Exp info contains info about the raw experimental data (excluding the gas-cell data).

opencc-by-4.0Dec 2018View details →

Fig. 3. Correlation coefficients estimated between water quality indicators and morphological Fig. 4 in Investigation Of Common Reed Regrowth On The Shores Of Recreational Lakes

Fig. 3. Correlation coefficients estimated between water quality indicators and morphological Fig. 4. The number of holidaymakers near parameters of common reeds on the shores of Bridvaisis, Gaustvinis and Gilius lakes (in the Bridvaisis, Gaustvinis and Gilius Lakes. The order from the bottom to the top) and differences boundary of the continuous line side indicates in morphological parameters of plants after the cases where p <0.01; dashed lines, where p <0.05. holidaymakers' visits.

opencc-by-4.0Dec 2021View details →

Рис. 1. Вероятность обнаружения меченых животных (среΑнее ± ошибка) при пяти- и Αесятиметровых интерваΛах межΑу прикормочными станциями в Αвух экспериментах. По второму эксперименту расчеты сΑеΛаны ΑΛя резуΛьтатов отΛова в течение первых трех и поΛных Αесяти Αней. Значение «p» отражает уровень статистической значимости разΛичий межΑу ΑоΛями животных с меткой при Αвух интерваΛах Fig. 1. Probability of finding marked animals (average±standard error) between feeding stations placed at intervals of five and ten meters in the two experiments. In the second experiment, calculations were made for the results of trapping during the first three days and during the whole period of ten days. The p value reflects the statistical significance of differences between the fractions of animals with a mark for two types of intervals in Verification of the bottle-based method for estimating abundance of small mammals using biomarkers

Рис. 1. Вероятность обнаружения меченых животных (среΑнее ± ошибка) при пяти- и Αесятиметровых интерваΛах межΑу прикормочными станциями в Αвух экспериментах. По второму эксперименту расчеты сΑеΛаны ΑΛя резуΛьтатов отΛова в течение первых трех и поΛных Αесяти Αней. Значение «p» отражает уровень статистической значимости разΛичий межΑу ΑоΛями животных с меткой при Αвух интерваΛах Fig. 1. Probability of finding marked animals (average±standard error) between feeding stations placed at intervals of five and ten meters in the two experiments. In the second experiment, calculations were made for the results of trapping during the first three days and during the whole period of ten days. The p value reflects the statistical significance of differences between the fractions of animals with a mark for two types of intervals

opencc-by-4.0Dec 2012View details →

A Preisach method for estimating absolute paleofield intensity under the constraint of using only isothermal measurements: 2. Experimental testing [DATASET]

Raw FORC data published in: Muxworthy, A.R., Heslop, D., Paterson, G.A., Michalk, D., 2011. A Preisach method for estimating absolute paleofield intensity under the constraint of using only isothermal measurements: 2. Experimental testing. J. Geophys. Res. 116, B04103, doi:04110.01029/02010JB007844.

opencc-by-4.0Dec 2020View details →

Figure 2 in First estimate of the fecundity of Ogcocephalus cubifrons (Lophiiformes) with notes on spawning behaviour

Figure 2. – Ova of Ogcocephalus cubifrons, 50x magnification showing the beginning of cleavage indicating the beginning of embryogenesis, and red oil droplet.