Find research datasets worth reusing

opencc-by-4.0Oct 2020View details →

A Synthetic Global Spatiotemporal Sampled River Discharge Database for Different Satellite Altimetry Mission Orbits

Corresponding peer-reviewed publication This dataset corresponds to all the RRR input and output files that were used in the study reported in: <ul> <li> Sikder, Md. S., Bonnema, M., Emery, C. M., David, C. H., Lin, P., Pan, M., et al. (2021). A Synthetic Data Set Inspired by Satellite Altimetry and Impacts of Sampling on Global Spaceborne Discharge Characterization. Water Resources Research, 57(2), e2020WR029035. <a href="https://doi.org/10.1029/2020WR029035">https://doi.org/10.1029/2020WR029035</a> </li> </ul> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.  Note that this dataset makes extensive use of the river network and RAPID simulations that were produced in the following study, and the paper is gratefully acknowledged here: <ul> <li> Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., et al. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. Water Resources Research, 55(8), 6499–6516. <a href="https://doi.org/10.1029/2019WR025287">https://doi.org/10.1029/2019WR025287</a> </li> </ul> Version of record and details of this version The version of record for this dataset (i.e. the one used in the aforementioned paper) is version V1.1 available at <a href="https://doi.org/10.5281/zenodo.4064188">https://doi.org/10.5281/zenodo.4064188</a>. This version V2.1 was produced to facilitate testing of the RRR software (<a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a>). Notable details regarding this version compared to V2.0 are as follows: <ul> <li>The temporal sequence files (seq_TIM*.csv) of observations for regular temporal sampling now all have a sampling mean time of 0 second for every river reach instead of the previous value which corresponded to the cycle of observations (e.g. 259,200 seconds for a three-day regular temporal sampling). This allows to start sampling at the onset of each simulation instead of at the end of the first cycle. This change does impact the findings of the study.</li> <li>The sampled discharge files (Qout*.nc) where produced with an updated version of rrr_anl_spl_mod.py which now selects the time step at which a sample is retained using a slightly different approach. The update only impacts sampling results when the sampling time matches the river model output time step exactly, and is more accurate now. This change does impact the findings of the study.</li> </ul>

opencc-by-4.0Sep 2021View details →

Field data for: Enterovirus sequence data obtained from primate samples in Central Africa suggest a high prevalence of enteroviruses with possible zoonotic potential

Enteroviruses infect humans and animals, can cause disease, and some may be transmitted across species barriers. We collected different types of samples from various species  of Central African wildlife, including data on sampling location and tested the samples for the presence of Enterovirus RNA using a family level PCR. Specimen collection was approved by an Institutional Animal Care and Use Committee (IACUC) of the University of California Davis, and the Governments of Cameroon and the Democratic Republic of the Congo. Enterovirus RNA was detected in samples from 17 primates and 2 rodents. Some sequences were very similar while others were dissimilar to known species, highlighting the unexplored enterovirus diversity in wildlife. The samples and filed data were collected by field ecologists as part of the USAID funded PREDICT project (https://ohi.vetmed.ucdavis.edu/programs-projects/predict-project) and screened for enterovirus RNA using consensus PCR. Maps were generated using basic maps from Paintmaps (http://www.paintmaps.com), a free tool for educational and academic use. The dataset contains the metadata on enterovirus screening among wildlife in Cameroon and the Democratic Republic of the Congo from 2003-2014 as part of the USAID funded PREDICT project. Please refer to the article for more information on methods and references.

opencc-by-4.0Sep 2021View details →

Synthetic Escherichia coli mixture samples with variable coverage

This dataset contains the synthetic mixture samples and reference sequences - as well as the appropriate metadata - that were originally used in the 2021 revision of the mSWEEP manuscript. There are 87 samples in total, each containing 100bp paired-end Illumina sequencing reads from 10 different Escherichia coli strains from 10 different lineages. The number of reads is set so that the sequencing coverage of the individual strains varies between 50x and 0.10x and sums up to 100x.

opencc-by-4.0Dec 2020View details →

Data from: Flattening the curve: approaching complete sampling for diverse beetle communities

DATA FROM: Burner, R., J. Åstrom, T. Birkemoe, A. Sverdrup-Thygeson. 2021. Flattening the curve: approaching complete sampling for diverse beetle communities. Insect Conservation and Diversity <a href="https://doi.org/10.1111/icad.12540">https://doi.org/10.1111/icad.12540</a>    ACKNOWLEDGEMENTS This research was funded by the Norwegian Environment Directorate as part of an ‘Agreement on monitoring hollow oaks and insects in hollow oaks’. The Norwegian University of Life Sciences (NMBU) workshop designed and produced the cross-pane flight intercept traps. Thanks to Sindre Ligaard for identifying the beetle species, and to Lindsay Burner, Ruben Roos, and Ross Wetherbee for assistance in the field. High-performance computing resources were provided by Frederick H. Sheldon and Louisiana State University (LSU HPC). INFORMATION This dataset contains all data necessary to reproduce the analysis in the resulting manuscript. Briefly, 110 insect traps were set for 3 months in a single forest stand in Ås, Norway in 2020. This dataset includes trap locations, number of individuals of each species captured in each trap, trap type, and forest covariates collected around the traps. For more detailed information see manuscript and README file. From abstract of manuscript: <ol> <li>Insects are a hyper diverse and ecologically important group. Their high diversity, however, presents challenges in sampling methodology, because rare species are unreliably detected with low sampling effort. However, the relationship between effort and species detections, critical for effective monitoring and evaluation of population trends, is too seldom quantified.</li> <li>We sampled forest beetles for three months in a 4-ha stand of mixed deciduous forest in southeastern Norway using 110 flight intercept (four types) and Malaise traps, the highest trap density (29 traps/ha) that we have seen reported. We examined species accumulation curves to quantify the benefits of each additional trap, compared capture rates among several trap designs and trap emptying frequencies, and tested for spatial autocorrelation.</li> <li>In total we captured 566 beetle taxa (19,854 individuals) from 52 families, yet our species accumulation curve was only beginning to flatten. Trap types differed considerably in their effectiveness. Nevertheless, twenty of our most effective window traps detected 75% of all taxa in our dataset. We found no evidence of spatial correlation within the scale of the study (100 m radius), nor did trap-level forest covariates (5 m radius) explain much variation.</li> <li>This implies that low to moderate sampling effort dramatically underestimates species richness, but that a limited number of effective traps can nonetheless achieve relatively thorough sampling for some applications. Immediate trap surroundings and spacing appeared unimportant. But, insect ecologists should take particular care in selecting trap types and be cautious comparing studies that employed different trap types.</li> </ol>

opencc-by-4.0Oct 2021View details →

Sample version 2.0 ITS_LIVE Sentinel-1 image pair product of ice velocity in Greenland

21 samples of version 2.0 ITS_LIVE Sentinel-1 image pair product of ice velocity in three test regions of Greenland Ice Sheet Sensor: Sentinel-1A/B Processor: <a href="https://github.com/isce-framework/isce2">ISCE</a> v2.4.1 (topsApp -> <a href="https://github.com/leiyangleon/Geogrid">Geogrid</a> v1.4.0 -> <a href="https://github.com/nasa-jpl/autoRIFT">autoRIFT</a> v1.4.0) Project: NASA MEaSUREs project <a href="https://its-live.jpl.nasa.gov">ITS_LIVE</a> Region 1 (69.13N, 50.88W; Jakobshavn Isbræ Glacier): 7 ascending image pairs Region 2 (77.61N, 42.79W; central north of interior Greenland): 3 ascending image pairs Region 3 (72.48N, 35.87W; central south of interior Greenland): 10 descending image pairs and 1 ascending image pair This serves as a supplementary dataset for the companion journal article submitted to Earth System Science Data (to appear soon).   Acknowledgement: This effort was funded by the NASA MEaSUREs program in contribution to the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project (<a href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov/</a>) and through Alex Gardner’s participation in the NASA NISAR Science Team

opencc-by-4.0Jan 2022View details →

Improving the Efficiency of Variationally Enhanced Sampling with Wavelet-Based Bias Potentials

Archive with data supporting the paper "Improving the Efficiency of Variationally Enhanced Sampling with Wavelet-Based Bias Potentials" and the related PhD thesis by B. Pampel

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-4.0Nov 2022View details →

Sampling metadata for the publication: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem "

Meta data of sampling location, time and depth of eDNA samples used in the study: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem". As well as taxonomic identification of sponges, their microbial abundance and growth form.

opencc-by-4.0Dec 2021View details →

Sampling Effort Metadata for the Central and South Atlantic Offshore and Deep-Sea Benthos

Metadata containing information on the sampling of benthic taxa in => 30 m water depth in the Central and South Atlantic. This data was compiled as part of a baseline review of the science, policy and management of the region (Bridges et al. in press). Metadata was compiled from sources identified through a literature search and information provided by members of the Challenger 150 Central and South Atlantic Regional Scientific Research Working Group. Version 1 (November, 2022): Metadata used in the Bridges et al. (in press) gap analysis with the exclusion of sensitive datasets (Atkinson et al. In prep). These are in the process of being made open access and will be added in due course.    References Atkinson et al in prep. SeaMap FBIP. Bridges. A.E.H., Howell, K.L., Amaro, T., Atkinson, L., Barnes, D.K.A., Bax, N., Bell, J.B., Bernardino, A.F., Beuck, L., Braga-Henriques, A., Brandt, A., Bravo, M.E., Brix, S., Butt, S., Carranza, A., Doti, B.L., Elegbede, I.O., Esquete, P., Freiwald, A., Gaudron, S.M., Guilhon, M., Hebbeln, D., Horton, T., Kainge, P., Kaiser, S., Lauretta, D., Limongi, P., Mcquaid, K.A., Milligan, R.J., Miloslavich, P., Narayanaswamy, B.E., Orejas, C., Paulus, S., Pearman, T.R.R., Perez, J.A., Ross, R.E., Saeedi, H., Shimabukuro, M., Sink, K., Stevenson, A., Taylor, M., Titschack, J., Vieira, R.P., Vinha, B. & Wienberg, C. Review of the Central and South Atlantic Shelf and Deep-Sea Benthos: Science, Policy and Management. Oceanography and Marine Biology: An Annual Review.

Head-to-head comparison of nasal and nasopharyngeal sampling using SARS-CoV-2 rapid antigen testing in Lesotho

These are pseudo-anonymised data from the MISTRAL study: "Head-to-head comparison of nasal and nasopharyngeal sampling using SARS-CoV-2 rapid antigen testing in Lesotho". The data dictionary explains the data available in the dataset. Between December 2020 and September 2021, 2131 individuals with either COVID symptoms or contact with a COVID-positive case were included from two hospitals in Lesotho and had a valid PCR results.

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.

Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV1

Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1, FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

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.

opencc-by-4.0Jan 2022View details →

2707 water samples from Vallon de Nant analyzed for stable water isotopes (dD, d17O, d18O) and electric conductivity

Database gathering 2707 water samples from the Vallon de Nant (Westerm Swiss Alps) analyzed for stable isotopes of water (dD, d17O, d18O, d-excess, LC-excess, 17O-excess) and electric conductivity. The "VDN_database.csv" file includes the following columns: - time [dd/mm/yyyy HH:MM, UTC] - latitude [decimal degrees, WGS84] - longitude [decimal degrees, WGS84] - elevation [masl] - point: point name - dD [&permil;] - d17O [&permil;] - d18O [&permil;] - d-excess [&permil;] - LC-excess [&permil;] - 17O-excess [per meg] - conductivity [µS/cm] Water is sampled from: - 2 points of main stream (Avançon de Nant) at HyS1 and HyS2 - 5 different springs at GRAS, AUBG, ROCK, BRDG and ICEC - 3 piezometers at PZ1, PZ2 and PZ3 - 2 weather stations for rain at Auberge and Chalet - various locations (GPS points) for the Snowpack - various locations (GPS points) for the Glacier The points with regular sampling are shown on the map "VDN_sampling_points.png".

Sample Dataset - HR Subject Areas

Dataset created as part of the Master Thesis "Business Intelligence – Automation of Data Marts modeling and its data processing".   Lucerne University of Applied Sciences and Arts Master of Science in Applied Information and Data Science (MScIDS) Autumn Semester 2022   Change log Version 1.1: The following SQL scripts were added: <table> <tbody> <tr> <td>Index</td> <td>Type</td> <td>Name</td> </tr> <tr> <td>1</td> <td>View</td> <td>pg.dictionary_table</td> </tr> <tr> <td>2</td> <td>View</td> <td>pg.dictionary_column</td> </tr> <tr> <td>3</td> <td>View</td> <td>pg.dictionary_relation</td> </tr> <tr> <td>4</td> <td>View</td> <td>pg.accesslayer_table</td> </tr> <tr> <td>5</td> <td>View</td> <td>pg.accesslayer_column</td> </tr> <tr> <td>6</td> <td>View</td> <td>pg.accesslayer_relation</td> </tr> <tr> <td>7</td> <td>View</td> <td>pg.accesslayer_fact_candidate</td> </tr> <tr> <td>8</td> <td>Stored Procedure</td> <td>pg.get_fact_candidate</td> </tr> <tr> <td>9</td> <td>Stored Procedure</td> <td>pg.get_dimension_candidate</td> </tr> <tr> <td>10</td> <td>Stored Procedure</td> <td>pg.get_columns</td> </tr> </tbody> </table> Scripts are based on Microsoft SQL Server Version 2017 and compatible with a data warehouse built with Datavault Builder. Data warehouse objects scripts of the sample data warehouse are restricted and cannot be shared.

Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 2 FOV2

Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 2 FOV2) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 2 FOV1

Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 2 FOV1) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 3 FOV2

Image-based data set of a post-mortem lung sample from a non-COVID-19-related pneumonia donor (CONTROL CASE 3 FOV2) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.

Multiplexed histology of COVID-19 post-mortem lung samples - ACUTE CASE 2 FOV3

Image-based data set of a post-mortem lung sample from a COVID-19 donor (ACUTE CASE 2 FOV3) Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.