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1. Effects of Altered Precipitation on Biological Soil Crusts, Fungi, and Soil Nitrogen Availability at the Monsoon Rainfall Manipulation Experiment (MRME) in the Sevilleta National Wildlife Refuge, New Mexico (2016)

   https://doi.org/10.6073/pasta/573848e6e65f686686884670de506465  

   Kwiecinski, Jarek V (January 2019, Environmental Data Initiative)

   Microbial activity in drylands is mediated by the magnitude and frequency of growing season rain events that will shift as climate change progresses. Nitrogen is often co-limiting with water availability to dryland plants, and thus we investigated how microbes important to the nitrogen (N) cycle and soil N availability varied temporally and spatially in the context of a long-term rainfall variability experiment in the northern Chihuahuan Desert. Specifically, we assessed biological soil crust (biocrust) chlorophyll content, fungal abundance, and inorganic N in soils adjacent to individuals of the grassland foundation species, Bouteloua eriopoda, and in the unvegetated interspace at multiple time points associated with an experimental monsoon rain treatment. Treatments included small weekly (5 mm) or large monthly (20 mm) rain events, which had been applied during the summer monsoon for nine years prior to our sampling. Additionally, we evaluated target plant C:N ratios and added 15 N-glutamate to biocrusts to determine potential for nutrient transport to B. eriopoda. Biocrust chlorophyll was up to 67% higher in the small weekly or large monthly rainfall regimes compared to ambient controls. Fungal biomass was 57% lower in soil interspaces than adjacent to plants but did not respond to rainfall regime treatments. Ammonium and nitrate concentrations near plants declined through the sampling period but varied little in soil interspaces. There was limited movement of 15 N from interspace biocrusts to leaves but high 15 N retention in the soils even after additional ambient and experimental rain events. Plant C:N ratio was unaffected by rainfall treatments. The long-term alteration in rainfall regime in this experiment did not change how short-term microbial abundance or N availability responded to the magnitude or frequency of events, suggesting a limited response of N availability to future climate change. 

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2. Biogeography of root fungi in grasslands

   https://doi.org/10.6073/pasta/3f63cf15ed0851494a79ba13761cd236  

   Rudgers, Jennifer A; Fox, Sam; Porras-Alfaro, Andrea; Herrera, Jose; Kent, Dylan; Souza, Lara; Chung, Y. Anny; Jumpponen, Ari (January 2021, Environmental Data Initiative)

   Aim: Roots and rhizospheres host diverse microbial communities that can influence the fitness, phenotypes, and environmental tolerances of host plants. Documenting the biogeography of microbiomes can detect the potential for a changing environment to disrupt host-microbe interactions, particularly in cases where microbes, such as root-associated Ascomycota, buffer hosts against abiotic stressors. We evaluated whether root-associated fungi had poleward declines in diversity as occur for many animals and plants, tested whether microbial communities shifted near host plant range edges, and determined the relative importance of latitude, climate, edaphic factors, and host plant traits as predictors of fungal community structure. Location: North American plains grasslands Taxon: Foundation North American grass species ⎯ Andropogon gerardii, Bouteloua eriopoda, B. gracilis, B. dactyloides, and Schizachyrium scoparium and their root-associated fungi Methods: At each of 24 sites representing three replicate latitudinal gradients spanning 17° latitude, we collected roots from 12 individual plants per species along five transects spaced 10 m apart (40 m × 40 m grid). We used next-generation sequencing of the fungal ITS2 region, direct fungal culturing from roots, and microscopy to survey fungi associated with grass roots. Results: Root-associated fungi did not follow the poleward declines in diversity documented for many animals and plants. Instead, host plant identity had the largest influence on fungal community structure. Edaphic factors outranked climate or host plant traits as correlates of fungal community structure; however, the relative importance of these environmental predictors differed among plant species. As sampling approached host species range edges, fungal composition converged among individual plants of each grass species. Main conclusions: Environmental predictors of root-associated fungi depended strongly on host plant species identity. Biogeographic patterns in fungal composition suggested a homogenizing influence of stressors at host plant range limits. Results predict that communities of non-mycorrhizal, root-associated fungi in the North American plains will be more sensitive to future changes in host plant ranges and edaphic factors than to the direct effects of climate. 

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3. Enhancing understanding of the hydrological cycle via pairing of process‐oriented and isotope ratio tracers

   https://doi.org/10.5061/dryad.8w9ghx3mr  

   Fiorella, Richard; Siler, Nicholas; Nusbaumer, Jesse; Noone, David (January 2021, Dryad)

   {"Abstract":["This dataset contains monthly average output files from the iCAM6\n simulations used in the manuscript "Enhancing understanding of the\n hydrological cycle via pairing of process-oriented and isotope ratio\n tracers," in review at the Journal of Advances in Modeling Earth\n Systems. A file corresponding to each of the tagged and isotopic variables\n used in this manuscript is included. Files are at 0.9° latitude x 1.25°\n longitude, and are in NetCDF format. Data from two simulations are\n included: 1) a simulation where the atmospheric model was\n "nudged" to ERA5 wind and surface pressure fields, by adding an\n additional tendency (see section 3.1 of associated manuscript), and 2) a\n simulation where the atmospheric state was allowed to freely evolve, using\n only boundary conditions imposed at the surface and top of atmosphere.\n Specific information about each of the variables provided is located in\n the "usage notes" section below. Associated article abstract:\n The hydrologic cycle couples the Earth's energy and carbon budgets\n through evaporation, moisture transport, and precipitation. Despite a\n wealth of observations and models, fundamental limitations remain in our\n capacity to deduce even the most basic properties of the hydrological\n cycle, including the spatial pattern of the residence time (RT) of water\n in the atmosphere and the mean distance traveled from evaporation sources\n to precipitation sinks. Meanwhile, geochemical tracers such as stable\n water isotope ratios provide a tool to probe hydrological processes, yet\n their interpretation remains equivocal despite several decades of use. As\n a result, there is a need for new mechanistic tools that link variations\n in water isotope ratios to underlying hydrological processes. Here we\n present a new suite of \u201cprocess-oriented tags,\u201d which we use to explicitly\n trace hydrological processes within the isotopically enabled Community\n Atmosphere Model, version 6 (iCAM6). Using these tags, we test the\n hypotheses that precipitation isotope ratios respond to parcel rainout,\n variations in atmospheric RT, and preserve information regarding\n meteorological conditions during evaporation. We present results for a\n historical simulation from 1980 to 2004, forced with winds from the ERA5\n reanalysis. We find strong evidence that precipitation isotope ratios\n record information about atmospheric rainout and meteorological conditions\n during evaporation, but little evidence that precipitation isotope ratios\n vary with water vapor RT. These new tracer methods will enable more robust\n linkages between observations of isotope ratios in the modern hydrologic\n cycle or proxies of past terrestrial environments and the environmental\n processes underlying these observations.  "],"Methods":["Details about the simulation setup can be found in section 3 of the\n associated open-source manuscript, "Enhancing understanding of the\n hydrological cycle via pairing of process\u2010oriented and isotope ratio\n tracers." In brief, we conducted two simulations of the atmosphere\n from 1980-2004 using the isotope-enabled version of the Community\n Atmosphere Model 6 (iCAM6) at 0.9x1.25° horizontal resolution, and with 30\n vertical hybrid layers spanning from the surface to ~3 hPa. In the first\n simulation, wind and surface pressure fields were "nudged"\n toward the ERA5 reanalysis dataset by adding a nudging tendency,\n preventing the model from diverging from observed/reanalysis wind fields.\n In the second simulation, no additional nudging tendency was included, and\n the model was allowed to evolve 'freely' with only boundary\n conditions provided at the top (e.g., incoming solar radiation) and bottom\n (e.g., observed sea surface temperatures) of the model. In addition to the\n isotopic variables, our simulation included a suite of\n 'process-oriented tracers,' which we describe in section 2 of\n the manuscript. These variables are meant to track a property of water\n associated with evaporation, condensation, or atmospheric transport."],"Other":["Metadata are provided about each of the files below; moreover, since the\n attached files are NetCDF data - this information is also provided with\n the data files. NetCDF metadata can be accessed using standard tools\n (e.g., ncdump). Each file has 4 variables: the tagged quantity, and the\n associated coordinate variables (time, latitude, longitude). The latter\n three are identical across all files, only the tagged quantity changes.\n Twelve files are provided for the nudged simulation, and an additional\n three are provided for the free simulations: Nudged simulation files\n iCAM6_nudged_1980-2004_mon_RHevap: Mass-weighted mean evaporation source\n property: RH (%) with respect to surface temperature.\n iCAM6_nudged_1980-2004_mon_Tevap: Mass-weighted mean evaporation source\n property: surface temperature in Kelvin\n iCAM6_nudged_1980-2004_mon_Tcond: Mass-weighted mean condensation\n property: temperature (K) iCAM6_nudged_1980-2004_mon_columnQ: Total\n (vertically integrated) precipitable water (kg/m2).  Not a tagged\n quantity, but necessary to calculate depletion times in section 4.3 (e.g.,\n Fig. 11 and 12). iCAM6_nudged_1980-2004_mon_d18O: Precipitation d18O (\u2030\n VSMOW) iCAM6_nudged_1980-2004_mon_d18Oevap_0: Mass-weighted mean\n evaporation source property - d18O of the evaporative flux (e.g., the\n 'initial' isotope ratio prior to condensation), (\u2030 VSMOW)\n iCAM6_nudged_1980-2004_mon_dxs: Precipitation deuterium excess (\u2030 VSMOW) -\n note that precipitation d2H can be calculated from this file and the\n precipitation d18O as d2H = d-excess - 8*d18O.\n iCAM6_nudged_1980-2004_mon_dexevap_0: Mass-weighted mean evaporation\n source property - deuterium excess of the evaporative flux\n iCAM6_nudged_1980-2004_mon_lnf: Integrated property - ln(f) calculated\n from the constant-fractionation d18O tracer (see section 3.2).\n iCAM6_nudged_1980-2004_mon_precip: Total precipitation rate in m/s. Note\n there is an error in the metadata in this file - it is total\n precipitation, not just convective precipitation.\n iCAM6_nudged_1980-2004_mon_residencetime: Mean atmospheric water residence\n time (in days). iCAM6_nudged_1980-2004_mon_transportdistance: Mean\n atmospheric water transport distance (in km). Free simulation files\n iCAM6_free_1980-2004_mon_d18O: Precipitation d18O (\u2030 VSMOW)\n iCAM6_free_1980-2004_mon_dxs: Precipitation deuterium excess (\u2030 VSMOW) -\n note that precipitation d2H can be calculated from this file and the\n precipitation d18O as d2H = d-excess - 8*d18O.\n iCAM6_free_1980-2004_mon_precip: Total precipitation rate in m/s. Note\n there is an error in the metadata in this file - it is total\n precipitation, not just convective precipitation."]} 

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4. Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx): Net Primary Production Quadrat Data at the Sevilleta National Wildlife Refuge, New Mexico

   https://doi.org/10.6073/pasta/b4c81c7073460ad91a5f0a77f6009ddc  

   Baur, Lauren; Collins, Scott; Muldavin, Esteban; Rudgers, Jennifer A; Pockman, William T. (January 2021, Environmental Data Initiative)

   {"Abstract":["Humans are creating significant global environmental change,\n including shifts in climate, increased nitrogen (N) deposition, and\n the facilitation of species invasions. A multi-factorial field\n experiment is being performed in an arid grassland within the\n Sevilleta National Wildlife Refuge (NWR) to simulate increased\n nighttime temperature, higher N deposition, and heightened El Niño\n frequency (which increases winter precipitation by an average of\n 50%). The purpose of the experiment is to better understand the\n potential effects of environmental change on grassland community\n composition and the growth of introduced creosote seeds and\n seedlings. The focus is on the response of three dominant species,\n all of which are near their range margins and thus may be\n particularly susceptible to environmental change. It is hypothesized\n that warmer summer temperatures and increased evaporation will favor\n growth of black grama (Bouteloua eriopoda), a desert grass, but that\n increased winter precipitation and/or available nitrogen will favor\n the growth of blue grama (Bouteloua gracilis), a shortgrass prairie\n species. Furthermore, it is thought that the growth and survival of\n introduced creosote (Larrea tridentata) seeds and seedlings will be\n promoted by heightened winter precipitation, N addition, and warmer\n nighttime temperatures. Treatment effects on limiting resources\n (soil moisture, nitrogen mineralization), species growth\n (photosynthetic rates, creosote shoot elongation), species\n abundance, and net primary production (NPP) are all being measured\n to determine the interactive effects of key global change drivers on\n arid grassland plant community dynamics. To measure above-ground NPP\n (i.e., the change in plant biomass, represented by stems, flowers,\n fruit and foliage, over time), the vegetation variables in this\n dataset, including species composition and the cover and height of\n individuals, are sampled twice yearly (spring and fall) at permanent\n 1m x 1m plots. The data from these plots is used to build\n regressions correlating biomass and volume via weights of select\n harvested species obtained in SEV157, "Net Primary Productivity\n (NPP) Weight Data." This biomass data is included in SEV205,\n "Warming-El Nino-Nitrogen Deposition Experiment (WENNDEx):\n Seasonal Biomass and Seasonal and Annual NPP.""]} 

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5. Kikuchi pattern dataset from wrought and as-built additively manufactured superalloys

   https://doi.org/10.5061/dryad.zcrjdfnr9  

   Calvat, M; Anjaria, D; Wang, H; Vecchio, K; Stinville, Jean-Charles (January 2025, Dryad)

   {"Abstract":["This dataset provides high-resolution Kikuchi diffraction patterns and\n associated orientation mapping data collected from both wrought and\n as-built additively manufactured (AM) Inconel 718 superalloys. The dataset\n includes raw electron backscatter diffraction (EBSD) patterns stored as\n .tif images and organized through .up2 metadata files, along with\n processed orientation data in .ang format. These measurements were\n acquired using a high-sensitivity EBSD detector over large scan areas,\n enabling detailed spatial resolution of microstructural features such as\n grain orientations, subgrain boundaries, and processing-induced texture.\n The dataset supports a range of applications, including machine learning\n for pattern recognition and the development of robust indexing algorithms.\n By including both wrought and AM material states, this dataset offers\n valuable insight into the influence of manufacturing route on\n crystallographic texture and cellular dislocation structure in Inconel\n 718, a critical alloy for high-temperature structural applications."],"Methods":["Materials and\n Sample Preparation: Three different nickel-based\n superalloys were used in this study: a wrought recrystallized Inconel 718\n (30 minutes at 1050°C followed by 8 hours at 720°C) with chemical\n composition of (wt.%) Ni – 0.56% Al – 17.31% Fe – 0.14% Co – 17.97% Cr –\n 5.4% Nb + Ta – 1.00% Ti – 0.023% C – 0.0062% N; a 3D-printed Inconel 718\n by DED (as-built) and a dynamically recrystallized Waspalloy\n (heat-treated) characterized by a necklace microstructure. The 3D-printed\n material was produced using a Formalloy L2 Directed Energy Deposition\n (DED) unit utilizing a 650 W Nuburu 450 nm blue laser capable of achieving\n a 400 μm laser spot size. Argon was used as the shielding and carrier gas,\n and the specimen remained in its as-built condition. The chemical\n composition is in wt.%: Ni – 0.45% Al – 18.77% Fe – 0.07% Co – 18.88% Cr –\n 5.08% Nb – 0.96% Ti – 0.036% C – 0.02% Cu - 0.04% Mn - 0.08% Si - 3.04%\n Mo. All samples were machined by EDM as flat dogbone samples of gauge\n section 1 × 3 mm². All samples were mechanically\n polished using abrasive papers, followed by diamond suspension down to 3\n μm, and were finished using a 50 nm colloidal silica suspension.\n Electron\n BackScatter Diffraction: EBSD measurements were\n performed on a Thermo Fisher Scios 2 Dual Beam FIB-SEM with an EDAX\n OIM-Hikari detector at an accelerating voltage of 20 kV, current of 6.4\n nA, an exposure time of 8.5 ms per diffraction pattern, 12 mm of working\n distance, and a 70° tilt. In total, 3 maps of 1000 × 900 μm were collected\n with a 1 μm step size, and 4 additional maps were collected at 0.1 μm step\n size. These EBSD maps were saved to .ang files and processed using the\n MTEX toolbox1. For each of these maps, SEM signal, confidence index (CI),\n and image quality (IQ) are provided as .tif files. The orientation maps\n are transformed using the inverse pole figure MTEX coloring [2] (given as\n IPF_mtex.jpg) and provided for the X (horizontal), Y (vertical), and Z\n (normal) directions. Additionally, all Kikuchi patterns were saved with no\n binning to 16-bit images under the .up2 format. Based on the diffraction\n patterns, sharpness maps, indicating the diffuseness of Kikuchi bands [3],\n have been constructed using EMSPHINX software [4] and are provided as .tif\n files. The details on the pattern center are provided in the .ang\n file. Kikuchi\n Patterns preprocessing: The Kikuchi patterns were\n originally acquired using 1 × 1 binning at a resolution of 480 × 480\n pixels. For the purpose of data processing, two versions are provided with\n the initial 1 × 1 binning and with a 4 × 4 binning (resulting in a reduced\n resolution of 120 × 120 pixels). Additional .up2 files, referred to as\n "preprocessed", are provided in which the background was\n subtracted and pattern gray values have been rescaled to fill the complete\n 16-bit range (between 0 and 65535). Due to the large size of the raw,\n unbinned data, they are not hosted on Dryad but can be made available upon\n request to the authors. Files\n Provided: The nomenclature of the provided files is\n described below, and a detailed explanation is available in the\n accompanying ReadMe.txt file, formatted according to DRYAD\n recommendations. The labels 718RX, AM718, and Waspalloy correspond to the\n wrought recrystallized Inconel 718, the as-built additively manufactured\n Inconel 718 (produced by DED), and a partially recrystallized Waspalloy,\n respectively. The term 1um refers to maps collected with a spatial\n resolution of 1 um, while 0.1um_1 and 0.1um_2 denote two separate maps\n acquired at 0.1 um resolution. The file labeled sharpness contains\n sharpness maps, as defined in [3], and computed using the EMSPHINX\n software [4]. Files labeled CI, IQ, and SEM represent the Confidence\n Index, Image Quality, and associated SEM maps obtained using MTEX1 and are\n provided as .tif files. Similarly, IPF_X, IPF_Y, and IPF_Z refer to\n inverse pole figure maps along the X (horizontal), Y (vertical), and Z\n (normal) directions and are provided as .jpg files. The file IPF_mtex\n gives the associated inverse pole figure MTEX coloring [1, 2]. 480x480 and\n 120x120 indicate the diffraction pattern resolutions, with the initial\n binning and with the 4 x 4 binning operation, respectively. All the images\n are stored as .up2 files. Files denoted as 120×120_preprocessed include\n the corresponding preprocessed patterns at the 120 x 120 resolution. The\n preprocessing procedure is detailed in the section "Kikuchi Patterns\n preprocessing." File\n format: The .up2 file is a proprietary data format\n used by EDAX/TSL systems to store Kikuchi pattern images and associated\n metadata from electron backscatter diffraction (EBSD) experiments. Each\n .up2 file contains high-resolution diffraction patterns acquired at each\n scan point, typically stored in a compressed or indexed form for efficient\n access. These files are commonly used when raw Kikuchi patterns are\n required for post-processing, including pattern remapping, machine\n learning applications, or simulation-based indexing. In addition to image\n data, .up2 files also include key acquisition parameters such as beam\n voltage, working distance, detector settings, image resolution, and stage\n coordinates, enabling full traceability of each pattern to its spatial\n location in the sample. The .ang file is a widely used text-based format\n for storing processed electron backscatter diffraction (EBSD) data.\n Generated by EDAX/TSL OIM software, it contains orientation mapping\n results after successful indexing of Kikuchi patterns. Each row in an .ang\n file corresponds to a single scan point and includes key information such\n as spatial coordinates (X, Y), Euler angles (Phi1, PHI, Phi2) defining\n crystallographic orientation, image quality (IQ), confidence index (CI),\n phase ID, and other optional metrics (e.g., grain ID or local\n misorientation). The file begins with a header that describes metadata,\n including step size, scan grid type (square or hexagonal), phase\n information, and scanning parameters. .ang files are commonly used for\n downstream analyses such as grain reconstruction, texture analysis, and\n misorientation mapping, and are often imported into visualization tools\n like MTEX toolbox1 or Dream.3D for further processing. The .tif (Tagged\n Image File Format) is a high-fidelity raster image format widely used in\n scientific imaging due to its ability to store uncompressed or losslessly\n compressed image data. In the context of EBSD datasets, .tif files\n typically store individual Kikuchi diffraction patterns collected during a\n scan. When used within a .up2 dataset, each pattern is saved as a separate\n .tif file, preserving the original grayscale intensity distribution\n necessary for accurate post-processing tasks such as reindexing, pattern\n matching, or machine learning-based classification. These images often\n have high bit-depth (e.g., 12-bit or 16-bit grayscale) to retain subtle\n contrast variations in the diffraction bands, which are critical for\n crystallographic orientation determination. The file naming and\n organization are indexed and referenced by the accompanying .up2 metadata\n file to maintain spatial correlation with the scan grid. The .jpg (or\n .jpeg), standing for Joint Photographic Experts Group, file format is a\n commonly used compressed image format designed to store photographic and\n continuous-tone images efficiently. .jpg uses lossy compression, meaning\n some image detail is discarded to significantly reduce file size. This\n makes it suitable for visual display and documentation purposes, but less\n ideal for quantitative image analysis, where preserving original pixel\n intensity values is critical. References:\n Bachmann, F., Hielscher, R. & Schaeben, H.\n Texture analysis with mtex–free and open source software toolbox.\n Solid state phenomena 160, 63–68 (2010).\n Nolze, G. & Hielscher, R. Orientations–perfectly\n colored. Appl. Crystallogr. 49, 1786–1802\n (2016). Wang, F. et al. Dislocation cells in\n additively manufactured metallic alloys characterized by electron\n backscatter diffraction pattern sharpness. Mater.\n Charact. 197, 112673 (2023). EMsoft-org.\n EMSphInx: Spherical indexing software for diffraction patterns. Public\n beta release; GPL-2.0 license. \n Acknowledgments: M.C., H.W., K.V., and J.C.S.\n are grateful for financial support from the Defense Advanced Research\n Projects Agency (DARPA - HR001124C0394). C.B., D.A., and J.C.S.\n acknowledge the NSF (award #2338346) for financial support. This work was\n carried out in the Materials Research Laboratory Central Research\n Facilities, University of Illinois. Carpenter Technology is acknowledged\n for providing the 718 and Waspalloy material. Tresa Pollock, McLean\n Echlin, and James Lamb are acknowledged for their support on the EBSD\n sharpness calculations."],"TechnicalInfo":["# Kikuchi pattern dataset from wrought and as-built additively\n manufactured superalloys Dataset DOI:\n [10.5061/dryad.zcrjdfnr9](10.5061/dryad.zcrjdfnr9) ## Description of the\n data and file structure #### Files Provided: See the Methods section for a\n description of file naming patterns and meaning. #### Folder architecture:\n 718RX: * 1um * 718RX_1um.ang * 718RX_1um_sharpness.tif * 718RX_1um_CI.tif\n * 718RX_1um_IQ.tif * 718RX_1um_SEM.tif * IPF_mtex.jpg *\n 718RX_1um_IPF_X.jpg * 718RX_1um_IPF_Y.jpg * 718RX_1um_IPF_Z.jpg *\n 718RX_1um_480x480.up2 * 718RX_1um_120x120.up2 *\n 718RX_1um_120x120_preprocessed.up2 * 0.1um_1 * 718RX_0.1um_1.ang *\n 718RX_0.1um_1_sharpness.tif * 718RX_0.1um_1_CI.tif * 718RX_0.1um_1_IQ.tif\n * 718RX_0.1um_1_SEM.tif * IPF_mtex.jpg * 718RX_0.1um_1_IPF_X.jpg *\n 718RX_0.1um_1_IPF_Y.jpg * 718RX_0.1um_1_IPF_Z.jpg *\n 718RX_0.1um_1_480x480.up2 * 718RX_0.1um_1_120x120.up2 *\n 718RX_0.1um_1_120x120_preprocessed.up2 * 0.1um_2 * 718RX_0.1um_2.ang *\n 718RX_0.1um_2_sharpness.tif * 718RX_0.1um_2_CI.tif * 718RX_0.1um_2_IQ.tif\n * 718RX_0.1um_2_SEM.tif * IPF_mtex.jpg * 718RX_0.1um_2_IPF_X.jpg *\n 718RX_0.1um_2_IPF_Y.jpg * 718RX_0.1um_2_IPF_Z.jpg *\n 718RX_0.1um_2_480x480.up2 * 718RX_0.1um_2_120x120.up2 *\n 718RX_0.1um_2_120x120_preprocessed.up2 AM718: * 1um * AM718_1um.ang *\n AM718_1um_sharpness.tif * AM718_1um_CI.tif * AM718_1um_IQ.tif *\n AM718_1um_SEM.tif * IPF_mtex.jpg * AM718_1um_IPF_X.jpg *\n AM718_1um_IPF_Y.jpg * AM718_1um_IPF_Z.jpg * AM718_1um_480x480.up2 *\n AM718_1um_120x120.up2 * AM718_1um_120x120_preprocessed.up2 * 0.1um_1 *\n AM718_0.1um_1.ang * AM718_0.1um_1_sharpness.tif * AM718_0.1um_1_CI.tif *\n AM718_0.1um_1_IQ.tif * AM718_0.1um_1_SEM.tif * IPF_mtex.jpg *\n AM718_0.1um_1_IPF_X.jpg * AM718_0.1um_1_IPF_Y.jpg *\n AM718_0.1um_1_IPF_Z.jpg * AM718_0.1um_1_480x480.up2 *\n AM718_0.1um_1_120x120.up2 * AM718_0.1um_1_120x120_preprocessed.up2 *\n 0.1um_2 * AM718_0.1um_2.ang * AM718_0.1um_2_sharpness.tif *\n AM718_0.1um_2_CI.tif * AM718_0.1um_2_IQ.tif * AM718_0.1um_2_SEM.tif *\n IPF_mtex.jpg * AM718_0.1um_2_IPF_X.jpg * AM718_0.1um_2_IPF_Y.jpg *\n AM718_0.1um_2_IPF_Z.jpg * AM718_0.1um_2_480x480.up2 *\n AM718_0.1um_2_120x120.up2 * AM718_0.1um_2_120x120_preprocessed.up2\n Waspalloy: * 1um * Waspalloy_1um.ang * Waspalloy_1um_sharpness.tif *\n Waspalloy_1um_CI.tif * Waspalloy_1um_IQ.tif * Waspalloy_1um_SEM.tif *\n IPF_mtex.jpg * Waspalloy_1um_IPF_X.jpg * Waspalloy_1um_IPF_Y.jpg *\n Waspalloy_1um_IPF_Z.jpg * Waspalloy_1um_480x480.up2 *\n Waspalloy_1um_120x120.up2 * Waspalloy_1um_120x120_preprocessed.up2 ##\n Code/software See Methods for recommendations on how to open files."]} 

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