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opencc-by-4.0May 2024View details →

Factual Report on XRF Analysis Conducted on Bulk Sedimentary Rock Samples from the Mesohellenic Trough

Factual Report on XRF Analysis Conducted on Bulk Sedimentary Rock Samples from the Mesohellenic Trough – Project: PilotStratergy   Analysis date: 30.04.2024 Report date: 02.05.2024, Revision date: - Written by: Christos L. Stergiou, Geologist, PhD Reviewed by: Pavlos Tyrologou, Geologist, PhD Advice also: Previous factual report files “Report_SEM_Round1_Bulk samples.docx” and “Report_XRD_PilotStrategy03.04.2024.docx” for additional information on the mineralogy of the samples included in this report.   1. Materials and Methods Three (3) sedimentary rock samples originating from the Tsotyli (sample Tsot-1; marly SANDSTONE), Eptachori (sample Ept-2; fine GREYWACKE) and Pentalofos (sample Pent-3; greywacke) Formations of the Mesohellenic Trough were powdered and analyzed by X-ray fluorescence (XRF) to determine their mineralogical composition. The samples were field collected by hummer and obtained as rock chips. Pulps produced from the rock chip samples were formed into pressed pellets by mixing 2.4 g of the binder CEREOX® with 9.6 g of rock pulp (i.e. sample-to-wax binder ratio of 4:1). The mixed material was homogenized in a mechanical mixer working at 24 rpm for 15 minutes and then pressed at 5 kbr (Fig. 2). The XRF analysis was performed using Bruker S4-PIONEER with a wavelength-dispersive X-ray fluorescence (WDXRF) analytical system at the Department of Mineralogy-Petrology-Economic Geology, School of Geology, Aristotle University of Thessaloniki. The spectrometer uses an Rh lamp and a system of 5 crystals: LIF200, LIF220, LIF420, XS-55, and PET. It also has two detectors: a gas proportional counter and a scintillation counter. The X-ray beam was used at its maximum energy of 50-60 kV. The element lines that were measured were the Ka and La lines, depending on the element. The method includes corrections for overlaps and matrix effects. Analytical results are presented in Table 1, while major conclusions after XRF analysis are presented below by taking into consideration conclusions previously made after XRD and SEM-EDS analysis. Previous published investigations on these samples include geomechanical and petrophysical methods for the evaluation of the parent sedimentary formations to capture and store CO2 (Tyrologou et al. 2023).     Table 1. Bulk geochemical analyses of major and minor elements for the analyzed samples Tsot-1, Ept-2 and Pent-3 from the Mesohellenic Trough.   <div> <table> <tbody> <tr> <td> Element </td> <td> Tsot-1 </td> <td> Ept-2 </td> <td> Pent-3 </td> </tr> <tr> <td> wt.% </td> <td>   </td> <td>   </td> <td>   </td> </tr> <tr> <td> SiO2 </td> <td> 34.85 </td> <td> 36.25 </td> <td> 15.49 </td> </tr> <tr> <td> Al2O3 </td> <td> 7.18 </td> <td> 6.61 </td> <td> 2.9 </td> </tr> <tr> <td> Fe2O3 </td> <td> 2.66 </td> <td> 3.32 </td> <td> 1.22 </td> </tr> <tr> <td> CaO </td> <td> 30.27 </td> <td> 25.61 </td> <td> 42.42 </td> </tr> <tr> <td> MgO </td> <td> 4.23 </td> <td> 7.23 </td> <td> 5.98 </td> </tr> <tr> <td> Na2O </td> <td> 1.04 </td> <td> 0.74 </td> <td> 0.39 </td> </tr> <tr> <td> K2O </td> <td> 2.22 </td> <td> 1.39 </td> <td> 0.94 </td> </tr> <tr> <td> MnO </td> <td> 0.11 </td> <td> 0.11 </td> <td> 0.03 </td> </tr> <tr> <td> TiO2 </td> <td> 0.32 </td> <td> 0.39 </td> <td> 0.13 </td> </tr> <tr> <td> P2O5 </td> <td> 0.08 </td> <td> 0.1 </td> <td> 0.06 </td> </tr> <tr> <td> LOI </td> <td> 16.79 </td> <td> 17.89 </td> <td> 30.3 </td> </tr> <tr> <td> Total </td> <td> 99.75 </td> <td> 99.64 </td> <td> 99.86 </td> </tr> <tr> <td> ppm </td> <td>   </td> <td>   </td> <td>   </td> </tr> <tr> <td> Ba </td> <td> 189 </td> <td> 149 </td> <td> 66 </td> </tr> <tr> <td> Co </td> <td> 7.0 </td> <td> 13 </td> <td> 4.0 </td> </tr> <tr> <td> Cr </td> <td> 749 </td> <td> 1,680 </td> <td> 512 </td> </tr> <tr> <td> Cu </td> <td> 14 </td> <td> 23 </td> <td> 7.0 </td> </tr> <tr> <td> Ni </td> <td> 112 </td> <td> 221 </td> <td> 78 </td> </tr> <tr> <td> Rb </td> <td> 192 </td> <td> 95 </td> <td> 82 </td> </tr> <tr> <td> Sc </td> <td> bdl </td> <td> bdl </td> <td> bdl </td> </tr> <tr> <td> Sr </td> <td> 298 </td> <td> 342 </td> <td> 242 </td> </tr> <tr> <td> V </td> <td> 650 </td> <td> 990 </td> <td> 306 </td> </tr> <tr> <td> Zn </td> <td> 31 </td> <td> 38 </td> <td> 15 </td> </tr> <tr> <td> Zr </td> <td> 100 </td> <td> 113 </td> <td> 54 </td> </tr> </tbody> </table> </div> *LOI = Loss of ignition, bdl = below detection limit.   5. Conclusions The XRF analysis confirms suggestions and conclusions made on previously acquired SEM-EDS and XRD analytical results focusing on bulk samples obtained by hammering from marly SANDSTONE (Tsot-1) and greywacke (Ept-2, Pent-3) originating from the Tsotyli, Eptachori and Pentalofos Formations of the Mesohellenic Trough (Fig. 1). Bulk geochemical analysis reveals that calcium and silica are the most enriched elements (Table 1). Sample Tsot-1 (24.85 wt.% SiO2) is slightly siliceous in composition, sample Epth-2 (36.25 wt.% SiO2) is dominantly siliceous, while sample Pent-3 is dominantly calcareous in composition (42.42 wt.% CaO, Table 1). These results are complementary to the semi-quantitative estimates obtained by XRD analysis (cf. Report_XRD_PilotStrategy03.04.2024.docx). In sample Tsot-1, calcite content is 30 wt.%, while quartz (29 wt.%) and albite (19 wt.%) percentages sum to 50 wt.%, supporting the siliceous profile acquired by XRF. In sample Ept-2, quartz (37 wt.%) is the dominant mineral phase followed by calcite (29 wt.%), while in sample Pent-3, calcite (41 wt.%) is the more enriched mineral phase, supporting the obtained geochemical results where SiO2 is 36.25 wt.% in sample Ept-2 and CaO is 45.42 wt.% for sample Pent-3. In addition, the geochemical results support the suggested level of maturity of the analyzed samples with Etp-2 showing the highest and Pent-3 the lowest maturity. Finally, sample Ept-2 shows the highest enrichment in minor elements, including 1,680 ppm of Cr and 990 ppm of V (Table 1). Relative enrichments in these trace elements, as well as in Co, Cu, Ni and Zn could be related to the highest incorporation of detrital material related to the ophiolitic basement rocks of the Mesohellenic Trough. Variations in trace elements may be associated with minor mineral phases not detected by the XRD analysis and the SEM-EDS examination of bulk samples. The study of thin-polished sections under a plane polarized and an electron scanning microscope shall clarify the mineral composition of the samples and conclude the mineralogical and geochemical investigation.   6. References Tyrologou, Pavlos, et al. (2023). Progress for carbon dioxide geological storage in West Macedonia: A field and laboratory-based survey." Open Research Europe 3. https://doi.org/10.12688/openreseurope.15847.2

opencc-by-4.0Nov 2022View details →

Synchrotron X-ray Diffraction Analysis - Measuring Bulk Crystallographic Texture from Differently-Orientated Ti-6Al-4V Samples

A dataset of synchrotron X-ray diffraction (SXRD) analysis files, recording the refinement of crystallographic texture from six differently orientated Ti-6Al-4V (Ti-64) samples. Two different refinement methods were used to fit a range of diffraction pattern ring intensities, for determining crystallographic texture in both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases. The first procedure was based on an established Rietveld refinement method, using the software package <a href="https://maud.radiographema.eu">MAUD (Materials Analysis Using Diffraction)</a>. The second procedure uses a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package. Both methods were used to calculate texture from each of the six different sample orientations, a combination of the six sample orientations, and in a batch processing method for calculating spatially-resolved texture variation from 387 individual X-Y stage-scan SXRD measurements across one of the samples. Material The Ti-64 material used in this study was pre-rolled to 87.5% reduction at 915ºC and then air-cooled to develop a characteristic texture. Six different rectangular samples were cut from this material and are referenced according to alignment with the original rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction), and alignment with the horizontal (X) and vertical (Y) axes of the synchrotron detector; <table align="center"> <caption>A table recording the SXRD run number and sample orientation analysed.</caption> <thead> <tr> <th scope="col">Run Number</th> <th scope="col">Sample Orientation Reference</th> <th scope="col"> Sample Orientation (Horizontal - Vertical) </th> </tr> </thead> <tbody> <tr> <td>103840</td> <td>Sample 6</td> <td>TD45ºRD - ND</td> </tr> <tr> <td>103841</td> <td>Sample 5</td> <td>RD - TD45ºND</td> </tr> <tr> <td>103842</td> <td>Sample 4</td> <td>TD - RD45ºND</td> </tr> <tr> <td>103843</td> <td>Sample 3</td> <td>RD - TD</td> </tr> <tr> <td>103844</td> <td>Sample 2</td> <td>RD - ND</td> </tr> <tr> <td>103845</td> <td>Sample 1</td> <td>TD - ND</td> </tr> </tbody> </table> Diffraction Pattern Averaging The .cbf images found in the <a href="https://doi.org/10.5281/zenodo.7311306">raw dataset</a> were first converted into .tiff images. The stage-scan images were then averaged together for each of the different sample orientations, using a Python notebook <a href="https://github.com/LightForm-group/sxrd-tiff-summer">sxrd-tiff-summer</a>, to produce six averaged .tiff images. These averaged .tiff image capture average diffraction peak intensities from an area of about 96.75 mm2 (equivalent to a total volume of around 193.5 mm3) from each piece, which is therefore representative of bulk crystallographic texture from six different sample orientations. MAUD Analysis To process data using MAUD the diffraction pattern images must first be caked, which converts the data into .dat files of intensity versus 2θ profiles, using 72 azimuthal cakes, each of 5° azimuthal width. Although MAUD has an in-built function to cake data, using ImageJ, it is not possible to cake data in MAUD with ImageJ in an automated way. Therefore, caking was done using <a href="https://pyfai.readthedocs.io/en/master/">pyFAI</a>, an open-source Python package, with the caking procedure recorded in a separate Python notebook, <a href="https://github.com/LightForm-group/pyFAI-integration-caking">pyFAI-integration-caking</a>. The caking was applied to each of the six averaged tiff images, as well as being applied to 387 individual X-Y stage-scan tiff images from Sample 1 (103845). The caking procedure was also applied to the CeO2 calibrant diffraction pattern, creating a .dat file that could be used for calibration of the instrument parameters within MAUD, before fitting the experimental data from the different samples. A separate package <a href="https://github.com/LightForm-group/MAUD-batch-analysis">MAUD-batch-analysis</a> was used to record the setup of the files and details of the refinement procedure. Details about the refinement procedure are also recorded in an accompanying paper reporting on these results. A number of refinement steps were used to fit the caked data from the six different sample orientations, and calculate texture. Texture was also calculated from a .dat file that combined all six sample orientations together. The crystallographic texture was refined using the E-WIMV algorithm, which was found to best reproduce quantitative texture intensity values with an orientation distribution function (ODF) resolution of 15º. The MAUD-batch-analysis package also contains details about how to setup and run MAUD in an automated batch processing mode. MAUD's batch mode was used to calculate texture from a series of 387 individual stage-scan diffraction patterns from Sample 1 (103845). A MAUD-batch-analysis script was first used to substitute caked data from the 387 diffraction patterns into template .par files, which contained an initial refinement of the volume fraction, crystal sizes and micro-strain, as a starting point. Both the crystal parameters and texture were then iteratively refined, in MAUD, using a .ins batch analysis script launched from the terminal. This was done to refine both α and then β phase texture. The texture data from the MAUD analysis was recorded as an ODF, with 15º resolution over all Euler space, and extracted in text format using a script from MAUD-batch-analysis. These text files can be loaded into <a href="https://mtex-toolbox.github.io">MTEX</a>, for plotting and analysing both the α and β phase crystallographic texture. Continuous-Peak-Fit Analysis A .poni calibration file was created using <a href="https://www.clemensprescher.com/programs/dioptas">Dioptas</a>, through a refinement matching peak intensities from a CeO2 standard diffraction pattern image. Dioptas was then used to determine peak bounds in 2θ for characterising a total of 21 α and 4 β lattice plane rings from the Ti-64 diffraction pattern images, which were recorded in a .py input script. Using these two inputs, Continuous-Peak-Fit automatically converts full diffraction pattern rings into profiles of intensity versus azimuthal angle, for each 2θ section, which can also include multiple overlapping α and β peaks. The Continuous-Peak-Fit refinement can then be launched in a notebook or from the terminal, to automatically calculate a full mathematical description, in the form of Fourier expansion terms, to match the intensity variation of each individual lattice plane ring. The results for peak position, intensity and half-width for all 21 α and 4 β lattice plane peaks were recorded at an azimuthal resolution of 1º and stored in a .fit output file. Details for setting up and running this analysis can be found in the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package. This package also includes a Python script for extracting lattice plane ring intensity distributions from the .fit files, matching the intensity values with spherical polar coordinates to parametrise the intensity distributions from each of the six different sample orientations, in the form of pole figures. The script can also be used to combine intensity distributions from different sample orientations. The final intensity variations are recorded for each of the lattice plane peaks as text files, which can be loaded into MTEX to plot and analyse both the α and β phase crystallographic texture. This method was also used to analyse all 387 individual diffraction patterns recorded across Sample 1 (S1 – 103845), to quantify the texture variation across the piece. Metadata An accompanying YAML text file contains associated processing metadata for both the MAUD and the Continuous-Peak-Fit analyses, recording information about the different packages used to process the data, along with details about the different files contained within this analysis dataset.

opencc-by-4.0Nov 2022View details →

Synchrotron X-ray Diffraction Dataset - Measuring Bulk Crystallographic Texture from Differently-Orientated Ti-6Al-4V Samples

A dataset of raw synchrotron X-ray diffraction (SXRD) images, recording crystallographic texture from two different pre-processed Ti-6Al-4V (Ti-64) materials, analysing six differently orientated samples from each material. The aim of the work was to provide a large dataset for testing and improving crystallographic texture refinement from SXRD patterns, with the use of different computational fitting methods. Prior to the experiment, the Ti-64 materials had been pre-rolled and then air-cooled to develop the microstructure, rolling to 50% and 87.5% reduction at 915ºC using a rolling mill at The University of Manchester. Rectangular samples (2 mm thick) were then machined from these rolled blocks. The samples were cut along different directions, three samples along different orthogonal rolling directions, and three at different angles to the rolling directions. The samples are referenced according to alignment of the rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction) with the long horizontal (X) axis and short vertical (Y) axis of the rectangular specimens.  Data was recorded using a high energy 99.8 keV synchrotron X-ray beam and a 5 second exposure at the detector. The slits were adjusted to give a 0.5 x 0.5 mm beam area, chosen to optimally resolve both the α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phase peaks. The SXRD data was recorded across each of the specimens by stage-scanning the beam in sequential X-Y positions at 0.5 mm increments, forming a rectangular grid of measurement points across each sample. A powder Ti-64 sample was also measured as a random texture standard. As well as the main experiment, 3 samples (sample 1, 2 and 3) were held together in different orders (1, 2, 3 ; 2, 1, 3 ; 2, 3, 1) and analysed through-thickness, to measure how beam attenuation might affect the bulk texture measurement. In addition, different detector exposure times (1 to 0.04 seconds) were also tested to analyse the impact of exposure time on overall intensity, to see how well the α and β peaks could be resolved from background noise at very fast acquisition frequencies. The raw data is in the form of synchrotron diffraction pattern images which has been separated according to experiment type. An accompanying YAML text file contains associated beamline metadata for each measurement. Further details of the experimental setup can be found in a pdf document. The material data folder contains further details about the material and sample orientations, including an electron backscatter diffraction (EBSD) map that can be used to verify the crystallographic texture.

opencc-by-4.0Dec 2022View details →

Measuring Bulk Crystallographic Texture from Ti-6Al-4V Hot-Rolled Sample Matrices using Synchrotron X-ray Diffraction (Analysis Dataset)

A dataset of synchrotron X-ray diffraction (SXRD) analysis files, recording the refinement of crystallographic texture from a number of Ti-6Al-4V (Ti-64) sample matrices, containing a total of 93 hot-rolled samples, from three different orthogonal sample directions. The aim of the work was to accurately quantify bulk macro-texture for both the α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases across a range of different processing conditions. Material Prior to the experiment, the Ti-64 materials had been hot-rolled at a range of different temperatures, and to different reductions, followed by air-cooling, using a rolling mill at The University of Manchester. Rectangular specimens (6 mm x 5 mm x 2 mm) were then machined from the centre of these rolled blocks, and from the starting material. The samples were cut along different orthogonal rolling directions and are referenced according to alignment of the rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction) with the long horizontal (X) axis and short vertical (Y) axis of the rectangular specimens. Samples of the same orientation were glued together to form matrices for the synchrotron analysis. The material, rolling conditions, sample orientations and experiment reference numbers used for the synchrotron diffraction analysis are included in the data as an excel spreadsheet. SXRD Data Collection Data was recorded using a high energy 90 keV synchrotron X-ray beam and a 5 second exposure at the detector for each measurement point. The slits were adjusted to give a 0.5 x 0.5 mm beam area, chosen to optimally resolve both the α and β phase peaks. The SXRD data was recorded by stage-scanning the beam in sequential X-Y positions at 0.5 mm increments across the rectangular sample matrices, containing a number of samples glued together, to analyse a total of 93 samples from the different processing conditions and orientations. Post-processing of the data was then used to sort the data into a rectangular grid of measurement points from each individual sample. Diffraction Pattern Averaging The stage-scan diffraction pattern images from each matrix were sorted into individual samples, and the images averaged together for each specimen, using a Python notebook <a href="https://github.com/LightForm-group/sxrd-tiff-summer">sxrd-tiff-summer</a>. The averaged .tiff images each capture average diffraction peak intensities from an area of about 30 mm2 (equivalent to a total volume of ~ 60 mm3), with three different sample orientations then used to calculate the bulk crystallographic texture from each rolling condition. SXRD Data Analysis A new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package was used to fit full diffraction pattern ring intensities, using a range of different lattice plane peaks for determining crystallographic texture in both the α and β phases. Bulk texture was calculated by combining the ring intensities from three different sample orientations. A .poni calibration file was created using <a href="http://www.clemensprescher.com/programs/dioptas">Dioptas</a>, through a refinement matching peak intensities from a LaB6 or CeO2 standard diffraction pattern image. Two calibrations were needed as some of the data was collected in July 2022 and some of the data was collected in August 2022. Dioptas was then used to determine peak bounds in 2θ for characterising a total of 22 α and 4 β lattice plane rings from the averaged Ti-64 diffraction pattern images, which were recorded in a .py input script. Using these two inputs, Continuous-Peak-Fit automatically converts full diffraction pattern rings into profiles of intensity versus azimuthal angle, for each 2θ section, which can also include multiple overlapping α and β peaks. The Continuous-Peak-Fit refinement can be launched in a notebook or from the terminal, to automatically calculate a full mathematical description, in the form of Fourier expansion terms, to match the intensity variation of each individual lattice plane ring. The results for peak position, intensity and half-width for all 22 α and 4 β lattice plane peaks were recorded at an azimuthal resolution of 1º and stored in a .fit output file. Details for setting up and running this analysis can be found in the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package. This package also includes a Python script for extracting lattice plane ring intensity distributions from the .fit files, matching the intensity values with spherical polar coordinates to parametrise the intensity distributions from each of the three different sample orientations, in the form of pole figures. The script can also be used to combine intensity distributions from different sample orientations. The final intensity variations are recorded for each of the lattice plane peaks as text files, which can be loaded into MTEX to plot and analyse both the α and β phase crystallographic texture. Metadata An accompanying YAML text file contains associated SXRD beamline metadata for each measurement. The raw data is in the form of synchrotron diffraction pattern .tiff images which were too large to upload to Zenodo and are instead stored on The University of Manchester's Research Database Storage (RDS) repository. The raw data can therefore be obtained by emailing the authors. The material data folder documents the machining of the samples and the sample orientations. The associated processing metadata for the Continuous-Peak-Fit analyses records information about the different packages used to process the data, along with details about the different files contained within this analysis dataset.

opencc-by-4.0Dec 2022View details →

Measuring Bulk Crystallographic Texture from Ti-6Al-4V Hot-Rolled Sample Matrices using Synchrotron X-ray Diffraction (Results Dataset)

A dataset of crystallographic texture results for both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases, measured from 31 different hot-rolled Ti-6Al-4V (Ti-64) materials and 3 differently orientated samples using synchrotron X-ray diffraction (SXRD). The aim of the work was to accurately quantify bulk macro-texture for both the α and β phases across a range of different processing conditions, and to compare results with electron backscatter diffraction (EBSD) measurements. The synchrotron intensities were extracted using a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package, and then directly used to calculate the pole figures, orientation distribution functions (ODFs) and numerical values for the texture indices in <a href="https://mtex-toolbox.github.io">MTEX</a> Material The Ti-64 materials had been hot-rolled at a range of different temperatures, and to different reductions, followed by air-cooling. Three samples of different orientation were cut from the centre of these rolled blocks, and from the starting material. The material and hot-rolling conditions are recorded in this <a href="https://doi.org/10.5281/zenodo.7438090">analysis dataset</a> as an excel spreadsheet and summarised in the table below. <table align="center"> <caption>A table recording the sample number and associated hot-rolling condition.</caption> <tbody> <tr> <td> Sample Number </td> <td> Rolling Condition </td> </tr> <tr> <td>1</td> <td>825ºC, 87.5% Reduction</td> </tr> <tr> <td>2</td> <td>865ºC, 87.5% Reduction</td> </tr> <tr> <td>3</td> <td>895ºC, 87.5% Reduction</td> </tr> <tr> <td>4</td> <td>915ºC, 87.5% Reduction</td> </tr> <tr> <td>5</td> <td>935ºC, 87.5% Reduction</td> </tr> <tr> <td>6</td> <td>950ºC, 87.5% Reduction</td> </tr> <tr> <td>7</td> <td>960ºC, 87.5% Reduction</td> </tr> <tr> <td>8</td> <td>975ºC, 87.5% Reduction</td> </tr> <tr> <td>9</td> <td>1020ºC, 87.5% Reduction</td> </tr> <tr> <td>10</td> <td>β-annealed, 825ºC, 87.5% Reduction</td> </tr> <tr> <td>11</td> <td>β-annealed, 915ºC, 87.5% Reduction</td> </tr> <tr> <td>12</td> <td>β-annealed, 975ºC, 87.5% Reduction</td> </tr> <tr> <td>13</td> <td>Reduced heating from 915ºC, 87.5% Reduction</td> </tr> <tr> <td>14</td> <td>Reduced heating from 975ºC, 87.5% Reduction</td> </tr> <tr> <td>15</td> <td>825ºC, 75% Reduction</td> </tr> <tr> <td>16</td> <td>865ºC, 75% Reduction</td> </tr> <tr> <td>17</td> <td>895ºC, 75% Reduction</td> </tr> <tr> <td>18</td> <td>915ºC, 75% Reduction</td> </tr> <tr> <td>19</td> <td>935ºC, 75% Reduction</td> </tr> <tr> <td>20</td> <td>950ºC, 75% Reduction</td> </tr> <tr> <td>21</td> <td>960ºC, 75% Reduction</td> </tr> <tr> <td>22</td> <td>975ºC, 75% Reduction</td> </tr> <tr> <td>23</td> <td>1020ºC, 75% Reduction</td> </tr> <tr> <td>24</td> <td>β-annealed, 825ºC, 75% Reduction</td> </tr> <tr> <td>25</td> <td>β-annealed, 915ºC, 75% Reduction</td> </tr> <tr> <td>26</td> <td>β-annealed, 975ºC, 75% Reduction</td> </tr> <tr> <td>27</td> <td>Reduced heating from 915ºC, 75% Reduction</td> </tr> <tr> <td>28</td> <td>Reduced heating from 975ºC, 75% Reduction</td> </tr> <tr> <td>29</td> <td>As-received</td> </tr> <tr> <td>30</td> <td>As-received, β-annealed</td> </tr> <tr> <td>31</td> <td>975ºC, 50% Reduction</td> </tr> </tbody> </table> MTEX Data Analysis The lattice plane intensities for 22 α and 4 β phase peaks were extracted from the Continuous-Peak-Fit analysis, also included in this <a href="https://doi.org/10.5281/zenodo.7438090">analysis dataset</a>, and saved as text files in the form of pole figures. The lattice intensity text files were analysed in MTEX using scripts from the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package, to plot pole figures and ODF slices, and to calculate pole figure maxima, ODF maxima, texture indices and texture component phase fractions. A kernel half-width of 10° was found to produce optimal data fitting, for highly accurate texture strength intensity values. Metadata An accompanying YAML text file contains associated processing metadata for the SXRD analysis, recording information about the packages used to process the data, along with details about the different files contained within this results dataset.

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Belowground foodweb biomass and soil CN and bulk density from moist acidic tundra nutrient addition plots (since 1989, 2006) sampled July 2011.

Biomass of belowground community groups (bacteria, fungi, protozoa, nematodes, rotifers, tardigrades) determined for organic and mineral soils in moist acidic tundra. Soil carbon and nitrogen content, bulk density, and depth are included.

openOpenDec 2015View details →

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Belowground foodweb biomass and soil CN and bulk density from moist acidic tundra nutrient addition plots (since 2006) sampled August 2012.

Biomass of soil rotifers, tardigrades, enchytraeids, protozoa and nematode groups from organic and mineral soils in moist acidic tundra nutrient addition plots (since 2006) sampled August 2012.

openOpenDec 2015View details →

opencc-by-4.0Jan 2020View details →

Major and trace bulk sample and micro-XRF geochemistry, carbon and oxygen stable isotope compositions of magmatic and sedimentary rocks from Hovedøya Island, Oslo fjord, Norway.

This data set reports on the methodologies and results of geochemical analysis carried out on samples of magmatic rock, calcite and sedimentary rocks of Hovedoya Island, Oslo fjord, Norway, in the framework of the publication by Poppe et al. (2020; Geochemistry, Geophysics, Geosystems; <a href="https://doi.org/10.1029/2019GC008685">https://doi.org/10.1029/2019GC008685</a>). The major and trace element bulk sample geochemical analysis was carried at the Laboratoire G-Time, Université Libre de Bruxelles, Brussels (V. Debaille), the micro-XRF mapping and line scanning, was carried out at the laboratory of the Analytical and Environmental Geo-Chemistry (AMGC) group at the Vrije Universiteit Brussel (VUB), Brussels (N.J. de Winter, S. Poppe) and the stable isotope composition analysis was carried out as well at the AMGC laboratory (S. Poppe, S. Goderis), supervised by P. Claeys and M. Kervyn, in collaboration with P. Boulvias.  Data sheets are provided in .csv or .xlsx format and compressed folders containing .TIF images of µXRF elemental maps are attached. This data set also contains the complete data sets obtained for the construction of calibration curves for µXRF line scan analysis of rock samples of magmatic composition at the AMGC laboratory at VUB.

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Text-fig. 3. Examples of plant macrofossil assemblages from post-evaporitic sections. a: bedding plane from Ciabòt Cagna covered by impressions of plant parts, with dominance of leaves of cf. Oleinites liguricus M.SACHSE, MCEA-P05038. b: waterloggedcompressed seeds of Toddalia latisiliquata (R.LUDW.) H.-J.GREGOR sieved out of a bulk sediment sample from Pollenzo, MGPTPU141033. c: millimeter-sized, waterlogged-compressed seeds of Sambucus pulchella C.REID et E.REID with abundant cracks, probably formed during both diagenesis and extraction of the fossils (bulk sediment sample from Ciabòt Cagna), MGPT- in Late Messinian Flora From The Post-Evaporitic Deposits Of The Piedmont Basin (Northwest Italy)

Text-fig. 3. Examples of plant macrofossil assemblages from post-evaporitic sections. a: bedding plane from Ciabòt Cagna covered by impressions of plant parts, with dominance of leaves of cf. Oleinites liguricus M.SACHSE, MCEA-P05038. b: waterloggedcompressed seeds of Toddalia latisiliquata (R.LUDW.) H.-J.GREGOR sieved out of a bulk sediment sample from Pollenzo, MGPTPU141033. c: millimeter-sized, waterlogged-compressed seeds of Sambucus pulchella C.REID et E.REID with abundant cracks, probably formed during both diagenesis and extraction of the fossils (bulk sediment sample from Ciabòt Cagna), MGPT-

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Text-fig. 7. Stereomicroscope microphotographs of plant remains sieved out of a sediment bulk sample (C3X) from bed GLA10 of Govone. a: Tetraclinis salicornioides (UNGER) KVAČEK, shoot fragment, MGPT-PU141083). b: Toddalia latisiliquata (R.LUDW.) H.-J. GREGOR, seed, MGPT-PU141084. c: Toddalia rhenana H.-J.GREGOR, seed, MGPT-PU141085. d: Eurya stigmosa (R.LUDW.) MAI, small seed with piths filled by organic remains and sediment, MGPT-PU141086. e: Eurya stigmosa (R.LUDW.) MAI, fragmentary seed, MGPT-PU141087. f: Visnea germanica MENZEL, fruit from two opposite sides, MGPT-PU141088. g: Symplocos casparyi R.LUDW., endocarp in lateral view from two opposite sides, MGPT-PU141089. Scale bar 1 mm. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results

Text-fig. 7. Stereomicroscope microphotographs of plant remains sieved out of a sediment bulk sample (C3X) from bed GLA10 of Govone. a: Tetraclinis salicornioides (UNGER) KVAČEK, shoot fragment, MGPT-PU141083). b: Toddalia latisiliquata (R.LUDW.) H.-J. GREGOR, seed, MGPT-PU141084. c: Toddalia rhenana H.-J.GREGOR, seed, MGPT-PU141085. d: Eurya stigmosa (R.LUDW.) MAI, small seed with piths filled by organic remains and sediment, MGPT-PU141086. e: Eurya stigmosa (R.LUDW.) MAI, fragmentary seed, MGPT-PU141087. f: Visnea germanica MENZEL, fruit from two opposite sides, MGPT-PU141088. g: Symplocos casparyi R.LUDW., endocarp in lateral view from two opposite sides, MGPT-PU141089. Scale bar 1 mm.

Fig. 9 in Biodiversity of museum and bulk field samples compared: The Chiampo sponge fauna (Eocene, Lessini Mountains, Italy)

Fig. 9. Comparison between species frequency of museum collections (data from Frisone et al. 2016) and bulk sampling (this study). Error bars denote binomial standard errors of percentages.

Fig. 8 in Biodiversity of museum and bulk field samples compared: The Chiampo sponge fauna (Eocene, Lessini Mountains, Italy)

Fig. 8. Distribution of sponge genera from museum collections (data from Frisone et al. 2016) and bulk sampling (this study). Error bars denote binomial standard errors of percentages.

Fig. 7 in Biodiversity of museum and bulk field samples compared: The Chiampo sponge fauna (Eocene, Lessini Mountains, Italy)

Fig. 7. Families and relative abundances frequencies from museum collections (data from Frisone et al. 2016) and bulk sampling (this study). Error bars denote binomial standard errors of percentages.

Fig. 6 in Biodiversity of museum and bulk field samples compared: The Chiampo sponge fauna (Eocene, Lessini Mountains, Italy)

Fig. 6. Comparison between frequency of museum collections (data from Frisone et al. 2016) and bulk field sampling (this study) at the ordinal level. Error bars denote binomial standard errors of percentages.

Fig. 5 in Biodiversity of museum and bulk field samples compared: The Chiampo sponge fauna (Eocene, Lessini Mountains, Italy)

Fig. 5. Distribution of species richness (number of taxa) and species abundance (number of individuals per taxon) among classes.

Fig. 11 in Biodiversity of museum and bulk field samples compared: The Chiampo sponge fauna (Eocene, Lessini Mountains, Italy)

Fig. 11. Rank-abundance distribution of bulk samples (A) and museum collection (Whittaker plots) (B). Y axis shows species abundance; X axis ranks each species in order from most to least abundant. Black dots represent species.

Fig. 2 in Biodiversity of museum and bulk field samples compared: The Chiampo sponge fauna (Eocene, Lessini Mountains, Italy)

Fig. 2. Location (B) and geological map (A) of the study area (Verona and Vicenza provinces, Northern Italy), modified from http://gisgeologia.regione. veneto.it/, author: Regione Veneto, Sezione Geologia e Georisorse, and released under the Italian Open Data License 2.0 (https://www.dati.gov.it/content/ italian-open-data-license-v20). Outline of the Lessini Shelf during the Eocene modified from Bosellini (1989). The sponge-bearing outcrop is indicated by the white star near Chiampo. Cf, Castelvero fault; SVf, Schio-Vicenza fault; Pf, Pedemontana thrust fault.

Fig. 4 in Biodiversity of museum and bulk field samples compared: The Chiampo sponge fauna (Eocene, Lessini Mountains, Italy)

Fig. 4. Rarefaction curve (line in the middle) of the Chiampo sponge fauna at Lovara. Grey area demarcate 95% confidence intervals of diversity estimates.

Fig. 12 in Biodiversity of museum and bulk field samples compared: The Chiampo sponge fauna (Eocene, Lessini Mountains, Italy)

Fig. 12. In situ Eocene specimen of Laocoetis patula from Lovara Quarry, Italy; showing its characteristic morphology with canal openings in quadrate arrangement, indicated by the white arrow. Coin for scale is 22 mm in diameter.