Find research datasets worth reusing

opencc-by-4.0Nov 2021View details →

Data to reproduce the results presented in Lake et al. 2021. Journal of Soils and Sediments, https://doi.org/10.1007/s11368-021-03107-6 ("High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting")

This repository contains data on (1) the absorbance data and (2) the measured concentrations, to reproduce computational results as presented in: "High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting".    1. Absorbance data (200-730 nm wavelengths):     * Average absorbance compensated for measured concentrations (average absorbance value per concentration)     * Average absorbance compensated for theoretical concentrations (average absorbance value per concentration)     * Average raw absorbance measured (average absorbance value per concentration)     * Raw absorbance measured (all absorbance values for all concentrations)     Data in all 3 files is indicated per soil sample / mixture, with corresponding fraction(s) of soil sample(s) and corresponding (theoretical) input concentration.      2. Measured concentration data:     * Measured concentration (average concentrations, tested for all experiments and for all theoretical input concentrations)

Reference Measurements of Pavement Samples

Data for MDPI Sensors article Performance Assessment of Reference Modelling Methods for Defect Evaluation in Asphalt Concrete (doi: <a href="https://doi.org/10.3390/s21248190">10.3390/s21248190</a>). Contains data related to Sections 2.3 and 3.1 of the paper. Data describe reference measurements of pavement samples. Data are registered point clouds in ASCII text XYZ format in an arbitrary coordinate system.

OSSE dataset for assessing the sensitivity of pCO2 reconstructions to sampling scales across a Southern Ocean sub-domain

The data stored in this repository are part of the manuscript entitled "The sensitivity of pCO2 reconstructions to sampling scales across a Southern Ocean sub-domain: a semi-idealized ocean sampling simulation approach" submitted in consideration for publication for in European Geosciences Union: Biogeosciences.   The netcdf file includes oceanographic (physical + biogeochemical) data, namely the partial pressure of carbon dioxide (pCO2) data at the surface ocean from the high-resolution (±10km) forced NEMO-PISCES coupled ocean model (BIOPERIANT12) and from the semi-idealized observing system simulation experiments (OSSEs) we performed.

Sample Multi-Modal BIDS dataset (v2.1)

This a sample BIDS dataset created for continous integration of the Connectome Mapper 3. This dataset was acquired at the Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland, using a 3T Siemens Prisma MRI scanner. It adopts the sub-/ses- structure and contains one T1w anatomical MRI (MPRAGE), one diffusion MRI (DSI) , and one resting-state functional MRI as well as additional Freesurfer derivatives. It is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. (See https://creativecommons.org/licenses/by/4.0/ for more details) Changes Version 2.1 <ul> <li>Fix issues with the resampling of the DWI and rfMRI scans with Slicer. They were regenerated in version 2.1 with `mri_convert` to better handle the 4th dimension.</li> <li>For the sake of the size of the dataset, only 100 frames in the fMRI recording has been kept and the sourcedata/ folder has been dropped but can be easily be retrieved in the previous 2.0 version (https://zenodo.org/record/5788803#.Yb2-giYo8bV).</li> </ul> Version 2.0 <ul> <li>For testing purposes, scans found in the root sub-01 directory have been downsampled to 2x2x2 mm3 (MPRAGE), and to 3x3x3 mm3 (DSI and rfMRI) with the ResampleScalarVolume module of Slicer 4.6.2. A copy of the output produced in the terminal by Slicer has been created in the `code/` directory.</li> <li>Original data have been placed in sourcedata/ in concordance to BIDS.</li> </ul>

Data from: Reproducibility of the Quantification of Reversible Wall Interactions in VOC Sampling Lines

Dataset from the following publication (<a href="https://doi.org/10.3390/atmos12020280">https://doi.org/10.3390/atmos12020280</a>). In the paper, a method to quantify the amount of substance segregated by reversible interactions on sampling lines is proposed. The areic amount of a VOC (Acetone) interacting with the pipe is measured for a commercial test pipe (Sulfinert®) as the amount of substance per unit area of the internal surface of the test pipe segregated from the flowing gas mixture. The areic amount is function of numerical integrals estimated under different conditions and reproducibility is evaluated. The data used to estimate the integrals described in this work is organised in folders. Each folder correspond to a sample. Sample information is available on Table 3 of the paper.

Metabolic alterations in Strongyloidiasis stool samples unveil potential biomarkers of infection_dataset

Strongyloidiasis, a parasitosis caused by Strongyloides stercoralis in humans, is a very prevalent infection in tropical or subtropical areas. Gaps on public health strategies corroborates to the high global incidence of strongyloidiasis especially due to challenges involved on its diagnosis. Based on the lack of a gold-standard diagnostic tool, we aimed to present a metabolomic study for the assessment of stool metabolic alterations. Stool samples were collected from 25 patients segregated into positive for strongyloidiasis (n = 10) and negative control (n = 15) and prepared for direct injection high-resolution mass spectrometry analysis. Using metabolomics workflow, 18 metabolites were annotated increased or decreased in strongyloidiasis condition, from which a group of 5 biomarkers comprising caprylic acid, mannitol, glucose, lysophosphatidylinositol and hydroxy-dodecanoic acid demonstrated accuracy over 89% to be explored as potential markers. The observed metabolic alteration in stool samples indicates involvement of microbiota remodeling, parasite constitution, and host response during S. stercoralis infection.

opencc-by-4.0Dec 2019View details →

Antarctic Circumnavigation Expedition sample log: samples collected in the Southern Ocean during the austral summer of 2016/17.

Dataset abstract The Antarctic Circumnavigation Expedition (ACE) spent 90 days circumnavigating Antarctica on the R/V Akademik Tryoshnikov during the austral summer of 2016/17. This dataset provides a record of the samples that were collected during the expedition. Dataset contents <ul> <li>ace_sample_log.csv, data file, comma-separated values</li> <li>README.txt, metadata, text</li> <li>data_file_header.txt, metadata, text</li> </ul> Dataset license This sample log is made available under a Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

Supplementary data for publication Global distribution of mcr gene variants in 214K metagenomic samples

# Supplementary data for the manuscript "Global distribution of mcr gene variants in 214,095 metagenomic samples" SD1_mapped_runids.csv : tab-separated file with columns of run_accessions downloaded from ENA and whether the metagenome were positive for at least one of the mcr genes. SD2_mcr_df.csv : compositional table of mcr-positive metagenomes with associated metadata (collection_year, country, and host) for each run_accession, as well as mapping results. SD3_mcr_contigs.fa : FASTA file with contigs carrying mcr genes. The header contains the run_accession ID. SD4_aldex2_results.csv: CSV file containing ALDEx2 results. The columns are as follows: * group: metadata category (year, country or host). If the column contains more than one label, e.g., "Denmark - 2020 - Pigs", significance is tested within Danish pig samples from 2020. * rab.all:  median clr value for all samples in the feature * rab.win.conditionA:  median clr value for the condition A of samples * rab.win.conditionB: median clr value for the condition B of samples * diff.btw: median difference in clr values between A and B conditions * diff.win: median of the largest difference in clr values within A and B conditions * effect : median effect size: diff.btw / max(diff.win) for all instances * overlap : proportion of effect size that overlaps 0 (i.e. no effect) * we.ep: Expected P value of Welch’s t test * we.eBH: Expected Benjamini-Hochberg corrected P value of Welch’s t test * wi.ep: Expected P value of Wilcoxon rank test * wi.eBH: Expected Benjamini-Hochberg corrected P value of Wilcoxon test * parts: gene name * conditionA: label of condition A that is compared against condition B * conditionB: label of condition B that is compared against condition A * conditions.A.vs.B: label to explain condition A compared against condition B NOTE: see for more explanation of the output of ALDEx2 https://www.bioconductor.org/packages/release/bioc/vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html#5_ALDEx2_outputs SD5: Multi-VCF file containing SNP information on mcr alleles. Can be used to construct consensus sequences. SD6: FASTA file containing all unique consensus sequences reported in the manuscript. SD7: CSV file with an overview of which metagenome contains which unique consensus sequence.

Low energy MeV SIMS yield measurements of various inorganic samples

The low energy range (a few 100 keV to a few MeV) primary ion mode in MeV Secondary Ion Mass Spectrometry (MeV SIMS) and its potential in exploiting the capabilities of conventional (keV) SIMS and MeV SIMS simultaneously was investigated. The aim is to see if in this energy range both types of materials, inorganic and organic, can be simultaneously analyzed. A feasibility study was conducted, first by analyzing the dependence of secondary ion yields in Indium Tin Oxide (ITO – In2O5Sn) and leucine (C6H13NO2) on various primary ion energies and charge states of Cu beam, within the scope of equal influence of electronic and nuclear stopping. Expected behavior was observed for both targets (mainly nuclear sputtering for ITO and electronic sputtering for leucine). MeV SIMS images of samples containing separate regions of Cr and leucine were obtained using both keV and MeV primary ions. Based on the image contrast and measured data, the benefit of a low energy beam is demonstrated by Cr+ intensity leveling with leucine [M+H]+ intensity, as opposed to a significant contrast at higher energy. It is estimated that by lowering the energy, leucine [M+H]+ yield efficiency lowers roughly 20 times as a price for gaining about 10 times larger efficiency of Cr+ yield, while leucine [M+H]+ yield still remains sufficiently pronounced.

CA-TIMS U-Pb analysis of IGSN samples IEMM10001, IEMM10002, IEMM10003, IEM10004

Single zircon U-Pb analyses were performed at the Berkeley Geochronology Center. The samples were crushed and mineral concentrates were then purified using standard mineral separation techniques, including sieves, magnetic separation and density separation. If present, euhedral, clear grains devoid of optically recognizable cores were chosen for analysis. All zircons were pretreated using thermal annealing at 850°C for 48 hrs, followed by chemical abrasion with concentrated HF in pressurized dissolution capsules at 220°C for 6-8 hrs. Prior to dissolution the crystals were cleaned in ultrasonically agitated aqua regia followed by multiple steps of rinsing in clean HNO3. Zircon crystals were then spiked with 205Pb-233U- 235U tracer solution mixed and calibrated at BGC, and dissolved by vapor transfer in HF using miniature PTFE capsules at 220°C for 6 days. After dissolution, large crystals were loaded in 2 to 3 aliquots (see table) to optimize beam stability (which typically is significantly decreased for large, unextracted zircon samples) and test reproducibility. Isotope ratios were determined on a Micromass Sector 54 mass spectrometer using a Daly-type ion counter positioned behind a WARP filter. Pb (as Pb+) and U (as UO2+) were run sequentially on the same filament. The accuracy of the used mixed tracer was tested repeatedly against solutions derived from certified standards of isotopically pure 206Pb and natural U (NIST SRM-991 and CRM-145, respectively), as well as age solutions distributed by the Earthtime initiative (agreement was found to be <0.1%, additional details regarding tracer calibration are in Irmis et al., 2011; Mundil et al., 2004; Black et al., 2004). An additional subset of 5 zircons of the KHB01 sample was analyzed using the Earthtime 535 tracer solution. The resulting mean age of 97.80 ± 0.19 Ma (MSWD 2.0) from this batch is slightly younger, but within uncertainty, compared to our reported age of 98.00 ± 0.14 Ma, even without taking into account an augmented uncertainty resulting from U/Pb in the tracer solutions. The elevated MSWD may be a result of excess scatter from true age dispersion within the zircon population of KHB01. Repeat measurements of the total procedural blank averaged 0.82 ± 0.36 pg Pb (U blanks were indistinguishable from zero), with 206Pb/204Pb = 18.40 ± 0.46, 207Pb/204Pb = 15.64 ± 0.25, 208Pb/204Pb = 38.04 ± 0.75 (all 2σ of population), and a 206Pb/204Pb-207Pb/204 Pb correlation of +0.47 (ratios and uncertainties were propagated into the age and age-error calculations). The total procedural blank including ion exchange chemistry is also as low as 1 pg but scatter in total common Pb concentration and possibly common Pb composition suggests that results from unextracted analyses of zircon yield better reproducibility. Deficient radiogenic 206Pb in zircon due to initial deficit of 230Th is accounted for by assuming a partition coefficient ratio DTh/DU of 0.2 (as applied in Wotzlaw et al., 2014), or by assuming Th/U in the parent magma to be 3.5 where the ET535 tracer solution was used (the resulting difference in age from either approach is negligible). Mass fractionation of U during analysis was controlled by the U double spike, whereas Pb mass fractionation was corrected by 0.15 ± 0.6 %/AMU (based on multiple analyses of NBS 981). The photo-micrograph shows crystals extracted from KHB03 and illustrates that the zircon population is homogeneous.

Dataset for evaluation of unrealistic optimism in time of pandemic COVID-19 on a Polish sample

This dataset contains the data used in a project called "Unrealistic optimism in the eye of the storm. Positive bias towards the consequences of COVID-19 during the second and third waves of the pandemic. ". The project concerns the occurrence of a cognitive bias - unrealistic optimism - with regard to contracting the coronavirus. The following information are attached to the dataset: codebooks with variables' names; analysis codes to replicate our results; supplementary materials with the description of procedures.

Human papillomavirus (HPV) detection in vaginal self-samples: evaluation of eNat® as an alternative suspension medium to ThinPrep®PreservCyt® for vaginal swabs

Dataset 1: HPV positivity on cervical and vaginal self-samples with different HPV real-time PCR assays Dataset 2: hrHPV viral load in cervical and vaginal self-samples Datakey 1: HPV positivity on cervical and vaginal self-samples with different HPV real-time PCR assays Datakey 2: hrHPV viral load in cervical and vaginal self-samples

Dataset: Infrared-radiofluorescence: dose saturation and long-term signal stability of a K-feldspar sample

Original measurement and processed data of the study Infrared-radiofluorescence: dose saturation and long-term signal stability of a K-feldspar sample submitted for review to Radiation Measurements. The data are structured as follows: <ol> <li>Measurement data </li> <li>Processed data</li> </ol> Experiments were carried out at the Archéosciences Bordeaux (UMR 6034, CNRS - Université Bordeaux Montaigne; former IRAMAT-CRP2A) in Bordeaux (France) and at the Département des sciences de la Terre of the Université du Québec à Montréal (Canada). The subfolders are organised by the laboratory where the experiments were carried out: spectrometer measurements in Montréal (00_Montreal_Spectrometer) and spatially resolved measurements (camera) in Bordeaux (10_Bordeaux_Camera).  Measurement data contains sequence files used to run the experiments (so-called *.lseq files) as well as the raw, unaltered measurement output in the form of files with the ending *.xsyg and *.tiff. For the camera measurements in Bordeaux, the system returned a couple of single TIFF files. We merged those files in two files, one for RFnat and RFreg, for convenience reasons. The data are, however, unprocessed.   Processed data is organized like the measurement data folder containing all kinds of semi-automated processed data (PDF files, images). All data were processed with the R (R Core Team, 2021) package 'Luminescence' (Kreutzer et al., 2012; 2021) and an ImageJ macro detailed in Mittelstraß and Kreutzer (2021)   References Kreutzer, S., Schmidt, C., Fuchs, M.C., Dietze, M., Fischer, M., Fuchs, M., 2012. Introducing an R package for luminescence dating analysis. Ancient TL 30, 1–8. Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Smedley, R.K., Christophe, C., Zink, A., Durcan, J., King, G.E., Philippe, A., Guérin, G., Riedesel, S., Autzen, M., Guibert, P., Mittelstrass, D., Gray, H.J., 2021. Luminescence: Comprehensive luminescence dating data analysis. CRAN. https://doi.org/10.5281/zenodo.4729933 Mittelstraß, D., Kreutzer, S., 2021. Spatially resolved infrared radiofluorescence: single-grain K-feldspar dating using CCD imaging. Geochronology 3, 299–319. https://doi.org/10.5194/gchron-3-299-2021 R Core Team, 2021. R: A language and environment for statistical computing. https://www.r-project.org

opencc-by-4.0Nov 2021View details →

Sample accession list for "Malaria protection due to sickle haemoglobin depends on parasite genotype"

This dataset contains a list of sample accessions and associated metadata for P.falciparum DNA samples sequenced for the analysis presented in the paper: Malaria protection due to sickle haemoglobin depends on parasite genotype Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy Nguyen, Sónia Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor10, David J. Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> bioRxiv link: <a href="http://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a>. The data contains: i. a single tab-delimited text file containing accessions and sequence read quality control-related information related to the processing described in [1], and ii. a README file describing the contents of the data in markdown and HTML format.  Please see the enclosed README file for full details. A full list of datasets which have been released with this manuscript can be found on the <a href="https://www.malariagen.net/resource/32">MalariaGEN website</a>.

Single-crystal X-ray diffractometry data for a sample of [Cu(HF₂)(pyrazine)₂]PF₆ collected on beamline I19-2 at Diamond Light Source

Single-crystal X-ray diffractometry data for a sample of [Cu(HF₂)(pyrazine)₂]PF₆. These data were collected at Diamond Light Source, on beamline I19 (experiments hutch 2), on 2022-01-30, and are particularly useful for testing data reduction routines. They are known to produce good merging statistics and final structure refinement. The sample was prepared as follows: Ammonium hexafluorophosphate (NH₄PF₆) (0.310 g, 1.9 mmol), ammonium hydrogen difluoride ((NH₄)HF₂) (0.109 g, 1.9 mmol) and pyrazine (C₄H₄N₂) (0.300 g, 3.7 mmol) were dissolved in 5 mL of deionised water. The obtained colourless solution was slowly added to a blue solution of copper(II) nitrate prepared by dissolving copper(II) nitrate hemipentahydrate (Cu(NO₃)₂ · 2.5(H₂O)) (0.425 g, 1.8 mmol) in 5 mL of deionised water. The solutions were mixed in a plastic beaker at room temperature. The formation of blue crystals of [Cu(HF₂)(pyrazine)₂]PF₆ on the side of the beaker started after few seconds and continued for about 24 hours during which the sealed beaker was not moved. The sample was measured at room temperature and the illuminating beam had a wavelength of 0.4859 Å (25.52 keV). Beamline I19-2 at Diamond Light Source, a four-circle κ-geometry diffractometer (see <a href="https://onlinelibrary.wiley.com/doi/10.1107/97809553602060000936">[Kern 2019]</a>) with an undulator source, is described in <a href="https://doi.org/10.1107/S0909049512008801">[Nowell 2012]</a> but has since been upgraded to use a Dectris Eiger2 X 4M CdTe hybrid photon counting detector. The data are written in the <a href="https://manual.nexusformat.org/classes/applications/NXmx.html">NXmx variant</a> of the <a href="https://www.nexusformat.org/">NeXus format</a>, and so include metadata with a functionally complete description of the diffractometer. Inventory of data: <ul> <li><code>01_CuHF2pyz2PF6b_Phi.tar.xz</code> A single 1750-image 350° φ rotation scan from -175° to 175° with 0.2° rotation per image, an exposure time of 0.1 s per image, ω = -90°, κ = 0° and 2θ = 0°.</li> <li><code>02_CuHF2pyz2PF6b_2T.tar.xz</code> A single 1750-image 350° φ rotation scan from -175° to 175° with 0.2° rotation per image, an exposure time of 0.1 s per image, ω = -90°, κ = 0° and 2θ = 20°.</li> <li><code>03_CuHF2pyz2PF6b_P_O.tar.xz</code> Two sequential rotation scans: <ul> <li><code>CuHF2pyz2PF6b_P_O_01.nxs</code> A 1750-image 350° φ scan from -175° to 175° with ω = -90°, κ = 0° and 2θ = 0°.</li> <li><code>CuHF2pyz2PF6b_P_O_02.nxs</code> A 600-image 120° ω scan from -125° to -5° with φ = -90°, κ = 45° and 2θ = 0°.</li> </ul> Both scans had 0.2° rotation per image and an exposure time of 0.1 s per image.</li> </ul> The same sample was used for all these measurements. Throughout, the sample-to-detector distance was 85 mm and the beam was attenuated to 0.2% of its full intensity. For each rotation scan, the data comprise a single top-level NXmx-format NeXus file named <code><filename>.nxs</code>, one or more image files named <code><filename>_00000n.h5</code>, where <code>n</code> is a numeral, and a single detector metadata file named <code><filename>_meta.h5</code>. The NeXus file contains an HDF5 virtual data set that links to the data in the image file(s), and several HDF5 external links to data in the detector metadata file. For internal reference of Diamond Light Source staff, these data were collected as part of commissioning visit CM31144-1. Some file names and corresponding HDF5 link targets have been altered from their original names for consistency with the file contents.

CaptuRING and scanned wood core samples

Wood samples in digitized cores with CaptuRING (CR_ in files) and scanned with Epson 750VPRO (scan_) for sample digitization comparison. Species: <ol> <li>Fagus sylvatica (FS)</li> <li>Quercus faginea (QF)</li> <li>Quercus ilex (QI)</li> <li>Quercus pyrenaica (QY)</li> <li>Juniperus cedrus (JC)</li> <li>Juniperus thurifera (JT)</li> <li>Pinus nigra (PN)</li> <li>Pinus pinaster (PP) </li> </ol> Graphical scale was fitted with empirical values for CR samples, and theoretical resolution value in scanning software for scanned ones. SupplementaryMaterials2.csv file includes summary table from image comparison data.

Radiofrequency ultrasound signals from bovine cartilage samples degraded with trypsin and collagenase

The folder Repository_RF_data contains the radiofrequency (RF) data acquired with an ArtUS EXT-1H system (Telemed, Italy) equipped with a 192 elements linear probe L15-7H40-A5 working in the frequency range 7.5-15 MHz, in the matlab format ".mat". Data were collected at 15 MHz, with a sampling rate of 40 MHz, adjusting the focus in the middle of the samples. The analysed samples were bovine cartilage samples, divided in three groups: - Control group: cartilage sample without any chemical treatment. - Trypsin group: cartilage samples immersed in a trypsin solution for 4h. - Collagenase group: cartilage samples immersed in a collagenase solution for 24h. The folder Repository_RF_data includes 2 matlab variables: - trypsin.mat = data acquired from 6 samples before and after the trypsin treatment - collagenase.mat = data acquired from 6 samples before and after the collagenase treatment Each variable is a TxN cell, where T is the time point of evaluation and N is the number of samples. The first row of each variable corresponds to the time zero of treatment, that is the control group; while the second row includes measurement at the final time point of treatment (4h for trypsin an 24h for collagenase). In particular, a single RF frame was acquired for all the analyses. Each recorded RF frame resulted in a matrix in which the columns (57) represented the number of RF scanning lines in a specific RF window, while the rows (727) constituted the number of samples in a single scanning line. For the details, see the articles published on Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Sorriento A, Cafarelli A, Valenza G, Ricotti L. Ex-vivo quantitative ultrasound assessment of cartilage degeneration. Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2976-2980. doi: 10.1109/EMBC46164.2021.9630198. PMID: 34891870.

opencc-by-4.0Apr 2022View details →

Eulerian and Lagrangian diagnostics of the dynamical properties of the water masses sampled during the Tara Pacific Expedition 2016-2018

In order to provide a description of the dynamical properties of the water masses sampled, different Eulerian and Lagrangian diagnostics were calculated.  For each of the 246 stations sampled, we proceeded as follows. We identified the water mass sampled at the given station. This was considered as a stadium shape with the two semi-circles centered on the starting and ending points of the transect, respectively. The radius of the stadium semi-circles was considered 0.1°, which is in accordance with previous studies25,29,30. The stadium was filled with virtual particles separated by 0.01°. For each virtual particle inside the stadium shape, we calculated an Eulerian or Lagrangian diagnostic (described above). The Eulerian diagnostics were extracted directly from the velocity field of the day of sampling. Concerning the Lagrangian diagnostics, these were obtained by advecting the virtual particle backward in time for an amount of time 𝞽 from the day of sampling day_S. For the Lagrangian betweenness, the advection was performed between day_S+𝞽/2 and day_S-𝞽/2, so that the advective time window was centered on the sampling day (details in25). For the Lagrangian diagnostics, we used the following advective times 𝞽: 5, 10, 15, 20, 30, and 60 days. The only exception is the retention time, which, by construction, was calculated only with the largest advective time, namely 𝞽=60 days. Once that, a given diagnostic (Eulerian or Lagrangian) was calculated for all the virtual particles filling the stadium shape, we calculated the mean value, and the 25, 50, and 75 percentiles. The percentiles were calculated in order to quantify the spatial variation of the diagnostic inside the stadium shape. Therefore, we associated each station with four values (mean, 25, 50, and 75 percentiles) of a given diagnostic.  Furthermore, two different velocity fields were used, which are described as follows.  Both the velocity fields were downloaded from E.U. Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). The first velocity field used was MULTIOBS_GLO_PHY_REP_015_004 [GlobEkmanDt]. This was produced by combining the altimetry derived geostrophic velocities and modeled Ekman surface currents. It had a spatial resolution of 0.25° and a temporal resolution of one day. The second velocity field was GLOBAL_REANALYSIS_PHY_001_030 [GloryS12]. It was obtained by a NEMO model assimilating altimetry and other observations. It had a spatial resolution of 1/12° and a temporal resolution of 1 day. The following Eulerian diagnostics were calculated: <ul> <li> Absolute velocity ([Uabs], m s-1): sqrt(u2+v2), where u and v are the zonal and meridional components of the horizontal velocity field used (described below) </li> <li> Kinetic energy ([Ekin], m2 .s-2): 0.5*(u2+v2) </li> <li> Divergence ([EulerDiverg], d-1): du/dx + dv/dy </li> <li> Vorticity ([Vorticity], d-1): dv/dx - du/dy </li> <li> Okubo-Weiss ([OW], d-2): s2-vorticity2, where s2 is (du/dx-dv/dy)2 + (dv/dx+du/dy)2. If negative, it indicates that the station sampled was inside an eddy. </li> </ul> The following Lagrangian diagnostics were calculated: <ul> <li> Finite-Time Lyapunov Exponents ([Ftle], d-1): it indicates the rate of horizontal stirring, and it is a means to quantify the intensity of turbulence in a given region. FTLE are commonly used to identify Lagrangian Coherent Structures, i.e. barriers to transport. In this case, a strong FTLE value indicates a region separating water masses which were far away backward in time. </li> <li> Lagrangian betweenness ([betw], adimensional): this diagnostic draws inspiration from Lagrangian Flow Network Theory26. It can identify regions which act as bottlenecks for the circulation, in that they receive waters coming from different origins, and that are then spread over several different destinations. These can represent possible hotspots driving biodiversity25. </li> <li> Lagrangian Divergence ([LagrDiverg], d-1). This diagnostic was calculated by integrating the Eulerian divergence along the backward trajectories. If positive, it indicates a water mass that, during the previous days, was subjected to a strong divergence, thus to a possible upwelling. If negative, it indicates a strong convergence, thus possible downwelling. </li> <li> Retention Time ([RetentionTime], d). This diagnostic indicates how many days a water mass has spent inside an eddy in the previous period. If the water mass is outside an eddy, then its retention time is set to zero. </li> </ul>

opencc-by-4.0Apr 2022View details →

Whole-genome genotype data for French Large White pigs from two distinct sampling times

Genotype data at plink binary format for 36 pigs from the french Large White breed: 13 animals from the female line born in 2014 and 2015, 13 animals from the male line born between 2012 and 2016, and 10 animals from a common ancestral line, born in 1977. These genotypes were obtained from individual whole genome sequencing (WGS) data, whiwh are available at https://www.ebi.ac.uk/ena under the accession number PRJEB51909. Two different genotype datasets were obtained from the raw WGS: 1) snp20_auto_cr (.bed/bim/fam): High quality autosomal SNPs, called by 3 different software, with a call rate of at least 90% 2) all10_auto (.bed/bim/fam): All SNPs or indels called by at least one of 3 different software. More details about these datasets and their use can be found in the following study: Boitard et al (under revision): Whole-genome sequencing of cryo-preserved resources from French Large White pigs at two distinct sampling times reveals strong signatures of convergent and divergent selection between the dam and sire lines.