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Vibrating Sample Magnetometry (VSM) measurements of samples from two meteorites.

Contains .dat files, .seq files, .gph files and a .xlsx file. The .dat files are data files (which also contain machine-generated metadata), the .gph files are machine-generated graph files created directly from the .dat files and the .seq files are machine-generated sequence files, detailing the sequence of measurements.

For sample 2 from GRA 95205 and sample 3 from Mundrabilla, the filenames describe the measurements: ZFC and FC refer to zero-field cooled and field cooled. The number appended with “G” refers to the applied magnetic field in Gauss. Sample 3 from Mundrabilla also contains a magnetic hysteresis loop from -20000 G to 20000 G performed at 3 K.

Sample 3 contains labeled ZFC and FC files (though frequently the “G” is not appended to the magnetic field, it is still labeled in Gauss). In addition, there are several sequences of magnetic hysteresis loops (Files called “ETMUND2_MH_#####.dat”) at varying temperatures.

Finally, there is a file named “5and10OeCombined.xlsx”. The zero-field cooled and field cooled measurements at 5 Oe and 10 Oe had 5 measurements at each temperature point. This file averages those points to give a single point at each temperature. This average was used in figure 2c in the associated paper.

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Images of samples and subsamples from Mundrabilla and GRA 95205.

Contains .jpg and .tif images. For Mundrabilla, the images included are those that appear in Fig. S3 from the SI appendix of the associated paper. For Mundrabilla, the images are those of the 5 subsamples from the divide and conquer process (GRA 95205 subsamples 1-5 of subsample c of sample 1 – these are referred to as subsamples 1-5 in the paper). The .tif version of each image zooms in on the part of the image with the sample. The .jpg version of each image includes a scale bar.

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Images from an optical microscope and images generated from those using ImageJ software for the purpose of estimating sample volume.

Contains .png and .jpg image files, .csv files generated by ImageJ analyzing images and .xlsx files containing related calculations

4 samples were measured using this technique which is explained in detail in the SI appendix of the associated paper. Subsamples a, b and c refer to the subsamples of the same name from figure 3 of the paper. Sample MUND-1 refers to the sample of the same name from figure 2 of the paper.

Each sample was analyzed using a 5 step process (the subfolders of each sample sort the files into the relevant steps. 1) We took the raw image file, obtained from the Optical Microscope Images. 2) Meteorite grains were manually outlined using ImageJ. 3) A mask was generated from these outlines. 4) Local Thickness and its error was calculated using the mask. 5) Using Local thickness as an estimate of in plane thickness and the area calculated from the mask, a volume was estimated.

In order to deal with distortions from the quartz tubes that some samples were in when measured, the remaining folder involves the above analysis performed on 5 images of the same sample (subsample a) in a quartz tube. This tube was rotated in between each measurement and the results of this were used to determine a contribution to the error from the quartz tube

The combined analysis .xlsx file combines the final volume and error calculations from all the samples into one document.

ImageJ Software
Version: 2.0.0-rc-69/1.52i
Build: 269a0ad53f
Date: 2018-12-04T11:30:09+0000

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Scanning Electron Micrsoscopy (SEM) images and Energy-Dispersive X-ray Spectroscopy (EDX) data of subsamples from Mundrabilla and GRA 95205.

Contains a .xlsx summary of the EDX element data in all of the subsamples imaged as well as .jpg, .docx, .tnp, .emsa, .sitif, .bmp, .csi, .si, .siref, .txt, .xml, .msa and .fss files.

.jpg files are SEM images of a sample.
.docx files contain summary files of EDX information showing images, SEM spectra and highlighted images that show where in each image and element was record.

.emsa files contain machine parameters. When associated with an EDX measurement, they also include element atomic percentages and weight percentages within an image. These files were used to populate the data in the .xlsx summary file.

Other files are output by the SEM software and are associated with the measurements taken here, but were not used in this research.

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Transmission Electron Microscopy (TEM) images and Energy-Dispersive X-ray Spectroscopy (EDX) data of a grain from subsample a from Mundrabilla.

Contains .pptx summaries of the EDX data and .png raw TEM image files.

Refer to the SI Appendix method section in the associated paper for a description of how the sample was prepared.

The .pptx files contain TEM images, images highlighted with observed elements and elemental breakdowns corresponding to the previous image. Except for the first image in each file, subsequent images are smaller areas of the overall image. At the end of “EDS results.pptx”, two EDX spectra are provided, corresponding to two of the images.

The .png files are the raw images from the .pptx summaries.

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Python code used to compare Energy-Dispersive X-ray Spectroscopy (EDX) data to the entries in the SUPERCON database of superconductors.

Contains the .py python code, a folder with data which was read in by the code and a folder with results generated by the code.

In the data folder, the files are:
DB_raw.txt was obtained by scraping the entire SUPERCON database (which was done by performing an empty search and then adjusting the URL so the entire database would be printed in one page).
DB_format.txt was processed using the routine initialize_raw(). This routine simply reformats DR_raw.txt and discards extraneous data.
GRA_EDX_T.txt and Mund_EDX_T.txt are weight percentages of EDX data of subsamples from GRA 95205 and Mundrabilla meteorites. This data is obtained from the EDX_Summary.xlsx file in the “SEM images and EDX data” component.

These datafiles are read and reformatted into nested lists using the routine “initialize” and then the SUPERCON database data is reformatted into a list of dictionaries breaking down the element frequency in entries using the routine “elbreakdown”.

All this is used by the routine“calcmisc”, which determines the maximum amount (in volume percentage) of any given superconductor that could exist in the samples, based on the EDX data. This is then sorted and filtered so that only those that superconduct in a given Tc range are returned by the routine “sortneeded”. These are then returned so that for any given EDX dataset, there is a corresponding list of every plausible entry in the SUPERCON database, sorted by the maximum amount of that superconductor that could exist in that sample These lists are output as GRA#.txt and MUND#.txt in the results folder.

The routine “calcmisc” calls “materialneeded”, which determines the limiting element for any given superconductor and how much of that element is available. This is unitless and 0 for most superconductors (that is, most superconductors contain an element not observed with EDX).

Oxygen and Carbon were not used in these calculations due to their high concentration in the measurements (which for carbon in particular was a result of the carbon tape the sample was mounted on).

Python version: 3.6.9.
Anaconda Python/R distribution: for module versions, refer to "condalistversions.txt", included in the "SUPERCON code and data" component download file.