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Fig. 10 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract

Fig. 10: Temperature (A) and salinity (C) average profiles with the associated STD for model (red) and Argo (blue), calculated from all the associated profiles of the study area (Fig. 1). Profile differences (Argo – model) of the average temperature (green line) and salinity (brown line) (B). T-S diagram of all Argo and model associated profiles for two depth layer zones (Argo: 200-800m light blue, 800-2000 m dark blue) (Model: 200-800 m pink, 800-2000 m red) (D).

Fig. 9 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract

Fig. 9: Temperature (A) and salinity (C) average profiles with the associated STD for model (red) and Argo (blue), calculated from the associated profiles during the "winter" periods (November – April). The associated profiles for the "summer" periods (May – October) are shown in (B) and (D) for the temperature and salinity respectively.

Fig. 7 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract

Fig. 7: A: Argo salinity average profiles in Southern Adriatic (SA) and Otranto Strait (OS) for the years 2010 (green) and 2012 (purple). B: Argo salinity average profiles in the Northern Ionian (NI) for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple). C: Model salinity average profiles in Southern Adriatic (SA) and Otranto Strait (OS) for the years 2010 (green) and 2012 (purple). D: Argo salinity average profiles in the Northern Ionian (NI) for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple).

Fig. 8 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract

Fig. 8: A: Argo salinity average profiles in the south-eastern Ionian for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple). B: Model salinity average profiles in the south-eastern Ionian for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple).

Fig. 6 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract

Fig. 6: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the southern Ionian region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the southern Ionian.

Fig. 5 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract

Fig. 5: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the northern Ionian region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the northern Ionian.

Fig. 3 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract

Fig. 3: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the southern Adriatic region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the south Adriatic.

Fig. 4 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract

Fig. 4: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the Otranto Strait. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the Otranto Strait.

Fig. 1 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract

Fig. 1: SANI model bathymetry (A). The geographical area covered by SANI model (red rectangular) and the divided sub-regions SA (Southern Adiatic - yellow), OS (Otranto Strait - green), NI (Northern Ionian – brown) and SI (Southern Ionian – blue). All the available (966) Argo profiles for the period 2008-2012 from 21 individual floats denoted with different colours according to their WMO number (B).

On the relationship between methane production in anaerobic incubations of peat material and in-situ methane emissions

These files are meant to accompany the publication: On the relationship between methane production in anaerobic incubations of peat material and in-situ methane emissions  Alexandra B. Cory1, Rachel M. Wilson1*, Olivia C. Ogles1, Patrick M. Crill2, Zhen Li3, Kuang-Yu Chang3, Samantha Bosman1, EMERGE Project Coordinators4, Isogenie Field Team5, Virginia I. Rich3, and Jeffrey P. Chanton1 <a name="_Hlk72229759"></a>1Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, <a name="_Hlk72229408"></a>USA 2Dept of Geological Sciences and Bolin Centre for Climate Research, Stockholm University; Stockholm, 106 91 Stockholm, Sweden <a name="_Hlk72229459"></a>3Department of Microbiology, The Ohio State University, Columbus, OH, USA 4Lawrence Berkeley National Laboratory; Berkeley, CA, USA. 5EMERGE Project Coordinators list of authors and affiliations appears in Acknowledgements.   Corresponding author: Rachel M. Wilson (rmwilson@fsu.edu) Key Points: ·         Laboratory incubations predict field methane emissions from a peatland ·         Interannual variation is best represented by the modeled results ·         Daily-scale variation is driven by processes other than temperature and water table depth   This paper is being submitted for consideration for publication and includes the following archived files: The file:   (1)    UPLOAD_chamber_CO2_and_CH4_with_T.xlsx contains 3 tabs of data measured from the field: (1) CH4 daily, (2) CO2 daily, (3) temp.   The first tab, CH4 daily contains the measured methane fluxes from the field auto chambers spanning 2012-2018. Column headers are             Date:  date of year measurement taken             DOY:  day of year measurement taken             seqday: sequential day of measurement since 01/01/2002 year: year of measurement site: indicates the autochamber site from which the data are measured cH4_flx (mg CH4/m2/d): measured methane emission in milligrams CH4 per m2 per day gC/m2/d: fluxes in grams of C per m2 per day gC/m2/y: fluxes in grams of C per m2 per year   The second tab, CO2 daily contains the measured CO2 fluxes from the field auto chambers spanning 2012-2018. Column headers are similar to the CH4 daily tab revised for CO2 when appropriate.   The third tab, temp provides the temperature in the peat below the surface at 50cm, 20cm and 10cm for 2012-2018.   (2)    UPLOAD_Incubation_All_Temp_Timesaeries_Data.xlsx contains the CO2 and CH4 production for the incubation vials at the various temperature treatments. The headers are: Habitat: indicates the habitat type from which the incubated peat was taken Depth: indicates shallow (9-19cm) peat vs. deep (25-35cm) peat Temp_C: indicates the temperature at which the incubation was conducted in °C Sample: gives a laboratory unique sample identification code Day: indicates day of incubation CH4_umoles_gDry: is the accumulated CH4 production in micromoles per g dry weight of peat CO2_umoles_gDry: is the accumulated CO2 production in micromoles per g dry weight of peat.   (3)    Fen Python Code and Bog Python Code contain all the files required to recreate the modeling results for the Fen and bog respectively.

Experimental data related to the publication: "In-situ analysis of the effect of residual fcc phase and special grain boundaries on the deformation dynamics in pure cobalt"

The article figures were produced solely from these data sets employing data processing methods described therein. For experimental conditions and naming conventions please refer to the paper. 1. Deformation data files contained within "deformation_data.zip": The .zip archive contains five files related to five samples of thermally treated cobalt: def_co600.csv def_co800.csv def_co1100.csv def_co1100-10c.csv def_co1100-20c.csv The data were recorded during compression of the above-listed samples at room temperature.  2. Acoustic emission (AE) data files contained within "AE_data.zip": The .zip archive contains four files related to four samples of thermally treated cobalt: AE_co600.wav AE_co800.wav AE_co1100.wav AE_co1100-20c.wav The AE data were recorded in continuous mode ("data streaming" at 2 MHz) during compression of the above-listed samples at room temperature.     3. Electron back-scatter diffraction (EBSD) data files contained within "EBSD_data.zip": The .zip archive contains fifty-three .osc files related to samples of as-drawn and thermally treated cobalt within four folders: 0c - as-drawn and annealed samples (i.e. without thermal cycling) 10c - annealed samples after thermal cycling of 10 cycles 20c - annealed samples after thermal cycling of 20 cycles ex-situ_def - ex-situ EBSD during deformation of selected samples The .osc data files represent EBSD data after clean-up procedures described in detail in the manuscript.

Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable"

Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable"  <a href="../api/records/13133835/draft/files/tmdcm.txt/content" target="_blank" rel="noopener noreferrer">tmdcm.txt</a>: current meter data  <a href="../api/records/13133835/draft/files/tide.txt/content" target="_blank" rel="noopener noreferrer">tide.txt</a>: tidal gauge data  <a href="../api/records/13133835/draft/files/windspeed.txt/content" target="_blank" rel="noopener noreferrer">windspeed.txt</a>: windspeed data  Figure 2: Figure2.npy Figure 3: Figure 3 abc .npy Figure16:  <a href="../api/records/13133835/draft/files/spatial_Vc.npy/content" target="_blank" rel="noopener noreferrer">spatial_Vc.npy</a> & <a href="13133835" target="_blank" rel="noopener noreferrer">spatial_h.npy</a>  Figure 17: <a href="../api/records/13133835/draft/files/streching_ncf.npy/content" target="_blank" rel="noopener noreferrer">streching_ncf.npy</a>

opencc-by-4.0May 2019View details →

In-situ data recorded from thaumatin crystals on Diamond Light Source VMXi

Data collected as part of routine beamline commissioning, from samples of thaumatin grown in 0.1 M Sodium citrate, 0.75M Sodium / Potassium tartrate. Data were collected in unattended mode with positions identified via SynchWeb from photographs taken with a Formulatrix imaging system (picture included.) Each data set consists of 200 images taken with an Eiger 2X 4M detector at a distance of 186mm, with exposure time of 2 ms, wavelength 0.97950 angstroms and 2% transmission with a DMM beam. Automated processing combined data using the xia2 multiplex tool to give a sufficiently complete data set for structure solution and refinement.

An in-situ daily dataset for benchmarking temporal variability of groundwater recharge

A newly developed benchmark dataset of groundwater recharge per unit specific yield (RpSy, n meters) at daily temporal resolution is presented. The data has been obtained through the application of the Water table Fluctuation (WTF) method at groundwater wells within the continental US. To ensure high-fidelity estimates, only wells that meet a set of stringent criteria have been considered. The RpSy dataset may serve as a benchmark for validating the temporal consistency of recharge products and daily simulation results from land surface and integrated hydrologic models. The resulting product is a continuous daily RpSy (n meters) time series data for 485 groundwater wells. The data files are provided in the .csv format and consist of three columns for each observation well. The first column lists the local time, while the second and third columns provide the RpSy and RpSyu (considering a groundwater depth-dependent specific yield) time series in meters per day. Additionally, a file containing site information for all the selected wells is included. It contains four columns that detail the USGS ID of the groundwater well, its latitude (Lat), longitude (Long), and screen depth (depth, in meters). The data file can be accessed in most text editors and spreadsheets.

opencc-by-4.0Sep 2024View details →

In-Situ Aircraft Observations from North China on May 22, 2017 for AAS

dataset for Airborne Investigation of Riming: Cloud and Precipitation Microphysics Within a Weak Convective System in North China

opencc-by-4.0Sep 2024View details →

Recommendations for reporting equivalent black carbon (eBC) mass concentrations based on long-term pan-European in-situ observations

A reliable determination of equivalent black carbon (eBC) mass concentrations derived from filter absorption photometers (FAPs) measurements depends on the appropriate quantification of the mass absorption cross-section (MAC) for converting the absorption coefficient (babs) to eBC. This study investigates the spatial–temporal variability of the MAC obtained from simultaneous elemental carbon (EC) and babs measurements performed at 22 sites. We compared different methodologies for retrieving eBC integrating different options for calculating MAC including: locally derived, median value calculated from 22 sites, and site-specific rolling MAC. The eBC concentrations that underwent correction using these methods were identified as LeBC (local MAC), MeBC (median MAC), and ReBC (Rolling MAC) respectively. Pronounced differences (up to more than 50 %) were observed between eBC as directly provided by FAPs (NeBC; Nominal instrumental MAC) and ReBC due to the differences observed between the experimental and nominal MAC values. The median MAC was 7.8 ± 3.4 m2 g-1 from 12 aethalometers at 880 nm, and 10.6 ± 4.7 m2 g-1 from 10 MAAPs at 637 nm. The experimental MAC showed significant site and seasonal dependencies, with heterogeneous patterns between summer and winter in different regions. In addition, long-term trend analysis revealed statistically significant (s.s.) decreasing trends in EC. Interestingly, we showed that the corresponding corrected eBC trends are not independent of the way eBC is calculated due to the variability of MAC. NeBC and EC decreasing trends were consistent at sites with no significant trend in experimental MAC. Conversely, where MAC showed s.s. trend, the NeBC and EC trends were not consistent while ReBC concentration followed the same pattern as EC. These results underscore the importance of accounting for MAC variations when deriving eBC measurements from FAPs and emphasize the necessity of incorporating EC observations to constrain the uncertainty associated with eBC.

opencc-by-4.0Oct 2024View details →

Processed data used for JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations"

This is the processed dataset used in the JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations" by Xiao Ge. Please contact the author (gexiao@tamu.edu) for all the original/processed outputs of R-CESM, and use the following original papers as citations. The dataset used in this research includes: 1. Loop Current Dynamics 2009-2011: LC_*.nc is the processed (reorganized) data for each in-situ station, * represents their station ID <ul> <li>https://digital.library.unt.edu/ark:/67531/metadc955416/</li> <li>https://www.sciencedirect.com/science/article/pii/S0377026516301348?via%3Dihub</li> <li>https://search.dataone.org/view/%7BBD2513E6-3B34-4B7C-BCB9-3C4ED5E8D0FB%7D</li> </ul> 2. Regional Community Earth System Model, R-CESM: <a href="https://zenodo.org/api/records/13932074/draft/files/h.nc/content" target="_blank" rel="noopener noreferrer">h.nc</a> is the bathymetry data of R-CESM; cmpr_*.nc files are provided as examples of the original R-CESM outputs; pvsf_prho_*.nc are the processed (subsampled at the target region and interpolated on potential density layers, derived stream function, potential vorticity, and relative vorticity) R-CESM outputs used in this research; and <a href="https://zenodo.org/uploads/13932074" target="_blank" rel="noopener noreferrer">LC_pv_40hlp_2013.nc</a> is the example of organized processed R-CESM (pvsf_prho_*.nc files) containing potential vorticity and relative vorticity for figures plotting <ul> <li>https://journals.ametsoc.org/view/journals/bams/102/9/BAMS-D-20-0024.1.xml?tab_body=fulltext-display</li> </ul>

opencc-by-4.0Jun 2021View details →

Characterizing ferromagnetic domains in ring structures using in-situ magnetic Fresnel imaging

This deposit contains supplementary datasets and data processing scripts used in a Specialization Project by Rajith Aravinth at the Norwegian University of Science and Technology (NTNU). Dataset: 2021_03_26_FA721_A6.5_in_situ_stack.hspy Sample FA721, window W1, ring A6.5um, objective lens 0768. Tilting range [-2.0, 2.0] deg. in X and [-2.0, 2.0] deg. in Y, step size 1.0 deg. Python files: processing.py utils.py p001_make_hyperspy.py Running processing.py produces the domains and areas as numpy files, that can be used for visualisation and quantifications. Looping through the whole dataset takes quite some time, hence the results are also to be found in the .npy files. # Numpy files areas.npy domains.npy Notebook: Jupyter_notebook.ipynb More detail overlook of the algorithm, with visualizations and result

opencc-by-4.0Dec 2020View details →

Dataset for deep-learning in-situ classification of HIV-1 virion morphology

This dataset contains TEM micrographs for HIV-1 virion samples intended for classification and detection as follows: <ol> <li> HIV-1_virion_classification_backbone_dataset.zip : Contains 1806 .tif images of isolated HIV-1 virions extracted and augmented from TEM micrographs. The images are divided into training (1443 images) and validation (363 images) sets and each of these is divided into eccentric, mature, immature labeled folders: <ul> <li> HIV-1_virion_classification_backbone_dataset/ <ul> <li> train/ <ul> <li> eccentric/ </li> <li> immature/ </li> <li> mature/ </li> </ul> </li> <li> val/ <ul> <li> eccentric/ </li> <li> immature/ </li> <li> mature/ </li> </ul> </li> </ul> </li> </ul> </li> <li> HIV-1_rcnn_dataset_full.zip : Contains 59 .tif TEM micrographs of HIV-1 samples as well as a matching .csv file recording the attributes of each viral instance and coordinates of the rectangular region that contains it: </li> </ol> <ul> <li> region_data_<image_id>.csv: <ul> <li> filename: Name of the image this csv refers to. (Ex: 0131001.png) </li> <li> file_size: Size (bytes) of the image this csv refers to. (Ex: 11755590) </li> <li> file_attributes: Specific attributes of the micrograph. (Ex: None) </li> <li> region_count: Number of viral instances detected in the micrograph. (Ex: 39) </li> <li> region_id: ID of a specific viral region. (Ex: 1) </li> <li> region_shape_attributes: Coordinates of the bounding box of <region_id> that contains a virion. (Ex: {"name":"rect","x":1022,"y":357,"width":225,"height":228}) </li> <li> region_attributes: Classification of the virion enclosed in this region (eccentric/mature/immature). (Ex: {"particle_class":"mature"}) </li> </ul> </li> </ul> The images are divided into training (46 images) and validation (13 images) sets and each of these contains folder for each micrograph: <ul> <li> HIV-1_rcnn_dataset_full/ <ul> <li> train/ <ul> <li> 0131001/ <ul> <li> 0131001.png </li> <li> region_data_0131001.csv </li> </ul> </li> <li> 0131004/ <ul> <li> 0131004.png </li> <li> region_data_0131004.csv </li> </ul> </li> <li> … </li> </ul> </li> <li> val/ <ul> <li> 0131002/ <ul> <li> 0131002.png </li> <li> region_data_0131002.csv </li> </ul> </li> <li> 0131003/ <ul> <li> 0131003.png </li> <li> region_data_0131003.csv </li> </ul> </li> <li> … </li> </ul> </li> </ul> </li> </ul> The first dataset (HIV-1_virion_classification_backbone_dataset.zip) is intended for viral classification algorithms while the second dataset (HIV-1_rcnn_dataset_full) is intended for detection and segmentation algorithms (for example RCNN). Applications of this dataset as well as source code can be found at <a href="https://github.com/Perilla-lab/TEMNet">https://github.com/Perilla-lab/TEMNet</a> .