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Data, sample sizes, and R code for analysis of: Variation in mutation (co)variances
<p>Because of pleiotropy, mutations affect the expression and inheritance of multiple traits and, together with selection, are expected to shape standing genetic covariances between traits and eventual phenotypic divergence between populations. It is therefore important to find if the M matrix, describing mutational variances of each trait and covariances between traits, varies between genotypes. We here estimate the M matrix for six locomotion behavior traits in lines of two genotypes of the nematode <em>Caenorhabditis elegans </em>that accumulated mutations in a nearly-neutral manner for 250 generations. We find significant mutational variance along at least one phenotypic dimension of the M matrices, but neither their size nor their orientation had detectable differences between genotypes. The number of generations of mutation accumulation, or the number of MA lines measured, was likely insufficient to sample enough mutations and detect potentially small differences between the two M matrices. We then tested if the M matrices were similar to one G matrix describing the standing genetic (co)variances of a population derived by the hybridization of several genotypes, including the two measured for M, and domesticated to a lab-defined environment for 140 generations. We found that the M and G were different because the genetic covariances caused by mutational pleiotropy in the two genotypes are smaller than those caused by linkage disequilibrium in the lab population. We further show that M matrices differed in their alignment with the lab population G matrix. If generalized to other founder genotypes of the lab population, these observations indicate that selection does not shape the evolution of the M matrix for locomotion behavior in the short-term of a few tens to hundreds of generations and suggests that the hybridization of <em>C. elegans </em>genotypes allows selection on new phenotypic dimensions of locomotion behavior.</p>
Data from: Sampling from commercial vessel routes can capture marine biodiversity distributions effectively
<p>Collecting fine-scale occurrence data for marine species across large spatial scales is logistically challenging but is important to determine species distributions and for conservation planning. Inaccurate descriptions of species ranges could result in designating protected areas with inappropriate locations or boundaries. Optimising sampling strategies, therefore, is a priority for scaling up survey approaches using tools such as environmental DNA (eDNA) to capture species distributions. In a marine context, commercial vessels, such as ferries, could provide sampling platforms allowing access to under-sampled areas and repeatable sampling over time to track community changes. However, sample collection from commercial vessels could be biased and may not represent biological and environmental variability. Here, we evaluate whether sampling along Mediterranean ferry routes can yield unbiased biodiversity survey outcomes, based on perfect knowledge from a stacked species distribution model (SSDM) of marine megafauna from online data repositories. Simulations to allocate sampling point locations were carried out representing different sampling strategies (random vs regular), frames (ferry routes vs unconstrained) and number of sampling points. SSDMs were remade from different sampling simulations and compared to the 'perfect knowledge' SSDM to quantify the bias associated with different sampling strategies. Ferry routes detected more species and were able to recover known patterns in species richness at smaller sample sizes better than unconstrained sampling points. However, to minimise potential bias, ferry routes should be chosen to cover the variability in species composition and its environmental predictors in the SSDMs. The workflow presented here can be used to design effective sampling strategies using commercial vessel routes globally, including for eDNA analyses. This approach has potential to provide a cost-effective method to access remote oceanic areas on a regular basis and can recover meaningful data on spatiotemporal biodiversity patterns.</p>
Epidote - Sample 1 NanED Round Robin, Data ESR3
<table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p>ESR3</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td> <p>FEI TECNAI</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p>FEG</p> </td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td> <p>200 kV</p> </td> </tr> <tr> <td> <p>Wavelength</p> </td> <td> <p>0.0251 Å</p> </td> </tr> <tr> <td> <p>Acquisiton Method</p> </td> <td> <p>SAED</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>Eagle – CCD Camera</p> </td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td> <p>2048 x 2048</p> </td> </tr> <tr> <td> <p>Calibration constant (not corrected for Effective Camera length)</p> </td> <td> <p>0.00182 Å<sup>-1</sup>/pixel</p> </td> </tr> <tr> <td> <p><strong>Sample description:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Name</p> </td> <td> <p>Epidote</p> </td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td> <p>Ca2FexAl3-xSi3O13H</p> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p>Stepwise</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td> <p>293 K</p> </td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p>351</p> </td> </tr> <tr> <td> <p>tilt range, tilt step</p> </td> <td> <p>-35° to +35°, 0.2°</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p>1s</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p>TIA</p> </td> </tr> <tr> <td> <p>Software used for processing</p> </td> <td> <p>PETS2</p> </td> </tr> <tr> <td> <p>Software used for Solution</p> </td> <td> <p>Jana</p> </td> </tr> <tr> <td> <p>Software used for refinement</p> </td> <td> <p>Olex2</p> </td> </tr> </tbody> </table>
Natrolite - Sample 2 NanED Round Robin, Data ESR3
<table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p>ESR3</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td> <p>FEI Titan</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p>FEG</p> </td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td> <p>300 kV</p> </td> </tr> <tr> <td> <p>Wavelength</p> </td> <td> <p>0.0197 Å</p> </td> </tr> <tr> <td> <p>Acquisiton Method</p> </td> <td> <p>Nanodiffraction</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>US1000 – CCD Camera</p> </td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td> <p>2048 x 2048</p> </td> </tr> <tr> <td> <p>Calibration constant (not corrected for Effective Camera length)</p> </td> <td> <p>0.00179 Å<sup>-1</sup>/pixel</p> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> <p><strong>Sample description:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Name</p> </td> <td> <p>Natrolite</p> </td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td> <p>Ca2FexAl3-xSi3O13H</p> </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p>Stepwise</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td> <p>293 K</p> </td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p>626</p> </td> </tr> <tr> <td> <p>tilt range, tilt step</p> </td> <td> <p>-70° to +70°, 0.2°</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p>1s</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p>TIA</p> </td> </tr> <tr> <td> <p>Software used for processing</p> </td> <td> <p>PETS2</p> </td> </tr> <tr> <td> <p>Software used for solution</p> </td> <td> <p>Jana</p> </td> </tr> <tr> <td> <p>Software used for refinement</p> </td> <td> <p>Olex2</p> </td> </tr> </tbody> </table>
Data for the paper "Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems"
<p>Data for the paper "Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems"</p>
Sample, test, and validation data for findmycells
<p>findmycells is an open source python package, developed to foster the use of deep-learning based python tools for bioimage analysis, specifically for researchers with limited python coding experience. It is developed and maintained in the following GitHub repository: https://github.com/Defense-Circuits-Lab/findmycells</p> <p><strong>Disclaimer: All data (including the model ensemble) uploaded here serve solely as a test dataset for findmycells and are not intended for any other purposes.</strong></p> <p>For instance, the group, subgroup, or subject IDs don´t refer to the actual experimental conditions. Likewise, also the included ROI-files were only created to allow the testing of findmycells and may not live up to scientific standards. Furthermore, the image data represents a subset of a dataset that is already published here:</p> <blockquote> <p>Segebarth, Dennis et al. (2020), Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses, Dryad, Dataset, <a href="https://doi.org/10.5061/dryad.4b8gtht9d">https://doi.org/10.5061/dryad.4b8gtht9d</a></p> </blockquote> <p>The model ensemble (cfos_ensemble.zip) was trained using deepflash2 (v 0.1.7)</p> <blockquote> <p>Griebel, M., Segebarth, D., Stein, N., Schukraft, N., Tovote, P., Blum, R., & Flath, C. M. (2021). Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2. <em>arXiv preprint arXiv:2111.06693</em>.</p> </blockquote> <p>The training was performed on a subset of the "lab-wue1" training dataset, using only the 27 images with IDs 0000 - 0099 (cfos_training_images.zip) and the corresponding est. GT masks (cfos_training_masks.zip). The images used in "cfos_fmc_test_project.zip" for the actual testing of findmycells are the images with the IDs 0100, 0106, 0149, and 0152 of the aforementioned "lab-wue1" training dataset. They were randomly distributed to the made-up subject folders and renamed to "dentate_gyrus_01" or "dentate_gyrus_02".</p>
Machine-guided path sampling to discover mechanisms of molecular self-organization (Training and validation data)
<p>Training and validation data for the Nature Computational Science manuscript "Machine-guided path sampling to discover mechanisms of molecular self-organization"</p>
Raw data and scripts for "Non-uniform sampling of similar NMR spectra and its application to studies of the interaction between alpha-synuclein and liposomes" by Shchukina et al.
<p>A series of 15N HSQC spectra of aSyn at temperatures 15,17..43C acquired with and without the addition of POPG-based liposomes. The spectra can be processed with sparse undersampling at various levels (scripts are provided).</p>
Data for: Validation of a nutria (Myocastor coypus) environmental DNA assay highlights considerations for sampling methodology
<p>Nutria (<em>Myocastor coypus</em>) is a semi-aquatic rodent species that is invasive across multiple regions within the United States. Here we evaluated a qPCR assay previously described for use in Japan for application across invasive populations in the United States. We also compared two environmental DNA sampling methodologies for this assay: field filtration of large volumes of water passed through filters versus direct sampling of small volumes of water. We validated assay specificity, generality, and sensitivity, compared assay performance between two independent laboratories, and successfully tested the assay<em> in situ </em>on a known wild population. The filtration method required fewer samples for environmental DNA detection than direct sampling, but the choice of methods should be assessed based on specific field conditions and time and budget considerations. Our extensive assay validation and comparison across laboratories suggests that the assay is ready to be applied in environmental DNA monitoring of nutria throughout the United States.</p>
Data from: Detection of vertebrates from natural and artificial inland water bodies in a semi-arid habitat using eDNA from filtered, swept and sediment samples
<p>Climate warming will impact the sustainability of arid and semi-arid zone environments so we need to understand the influence of changes in arid lands on vertebrate populations. However, biomonitoring and biodiversity assessment in arid environments can be prohibitively time-consuming, expensive, and logistically challenging due to their often remote and inhospitable nature. Sampling of environmental DNA (eDNA) coupled with high-throughput sequencing is an emerging biodiversity assessment method. Here we explore the application of eDNA metabarcoding and various sampling approaches to estimate vertebrate richness and assemblage at human-constructed and natural water sources in a semi-arid region of Western Australia. Three sampling methods: sediment samples, filtering through a membrane with a pump, and membrane sweeping in the water body, were compared using two eDNA metabarcoding assays, 12S-V5 and 16smam, for 120 eDNA samples collected from four gnammas (gnamma: Australian Indigenous Noongar language term – granite rock pools) and four cattle troughs in the Great Western Woodlands, Western Australia. We detected higher vertebrate richness in samples from cattle troughs and found differences between assemblages detected in gnammas (more birds and amphibians) and cattle troughs (more mammals, including feral taxa). Total vertebrate richness was not different between swept and filtered samples, but all sampling methods yielded different assemblages. Our findings indicate that eDNA surveys in arid lands will benefit from collecting multiple samples at multiple water sources to avoid underestimating vertebrate richness. The high concentration of eDNA in small, isolated water bodies permits the use of sweep sampling which simplifies sample collection, processing, and storage, particularly when assessing vertebrate biodiversity across large spatial scales.</p>
Over and Under Sampled Data-sets of Code Issues in Java Open-Source Projects
<p>The dataset comprises code changes made to 15 Java Open-Source projects, classified with sentiment values (0 for negative and 1 for positive) based on developer reviews during various revision submissions. The dataset is available in 8 versions, each containing a sampled dataset using an over or under-sampling technique.</p>
rarefaction and extrapolation of sample coverage based on sample-based abundance data
<p>#R code.txt is the R code for plotting figures and constructing Tables</p> <p>#bciabun1010.txt is the species_by_plot matrix that BCI forest Plot is divided by a plot with size 10m*<em>10m</em></p> <p><em>#bciabun2020.txt is the species_by_plot matrix that BCI forest Plot is divided by a plot with size 20m*</em>20m</p> <p><em>#bciabun5050.txt is the species_by_plot matrix that BCI forest Plot is divided by a plot with size 50m*5</em>0m</p> <p>#fus10.txt is the species_by_plot matrix that Fushan forest Plot is divided by a plot with size 10m*<em>10m</em></p> <p><em>#fus20.txt is the species_by_plot matrix that Fushan forest Plot is divided by a plot with size 20m*</em>20m</p> <p>#fus50.txt is the species_by_plot matrix that Fushan forest Plot is divided by a plot with size 50m*<em>50m</em></p> <p><em>#lhc10.txt is the species_by_plot matrix that Lianhuachi forest Plot is divided by a plot with size 10m*</em>10m</p> <p>#lhc20.txt is the species_by_plot matrix that Lianhuachi forest Plot is divided by a plot with size 20m*20m</p> <p>#lhc50.txt is the species_by_plot matrix that Lianhuachi forest Plot is divided by a plot with size 50m*50m</p>
Epidote - Sample 1 NanED Round Robin, Data: ESR10
<p><strong><em>Epidote</em></strong></p> <p>The following submission contains the data collection of datasets for the sample epidote under the NanEd round-robin project. 5 single crystals were identified, and the continuous rotation data acquisition technique was used to collect datasets. All the datasets were processed with REDp, XDS and shelx software. The table below summarizes the data collection parameters for the datasets. The following data is also in the zip folder as a word file.</p> <p><strong>Continuous Rotation:</strong></p> <table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p>ESR10 - Round Robin</p> </td> </tr> <tr> <td> <p>Project Label</p> </td> <td> <p>RR-1</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td> <p>RR-1_SU</p> </td> </tr> <tr> <td> <p>Data set Label</p> </td> <td> <p>RR1-1 to 5</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td> <p>Transmission electron microscope JEOL 2100 LaB6</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p>LaB6</p> </td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td> <p>200 kV</p> </td> </tr> <tr> <td> <p>Wavelength</p> </td> <td> <p>0.0251 Å</p> </td> </tr> <tr> <td> <p>Probe Type</p> </td> <td> <p>Parallel beam</p> </td> </tr> <tr> <td> <p>Beam Diameter</p> </td> <td> <p>6 μm</p> </td> </tr> <tr> <td> <p>Beam Convergence</p> </td> <td> <p>Parallel beam, convergence <0.1mrad</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>Hybrid pixel detector ASI Timepix (bottom mounted)</p> </td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td> <p>512 x 512</p> </td> </tr> <tr> <td> <p>Pixel size</p> </td> <td> <p>55 µm x 55 µm</p> </td> </tr> <tr> <td> <p>Effective camera length</p> </td> <td> <p>250 mm</p> </td> </tr> <tr> <td> <p>Calibration constant</p> </td> <td> <p>0.004990 Å<sup>-1</sup>/pixel</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Sample description:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Name</p> </td> <td> <p>Epidote</p> </td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td> <p>Ca<sub>2</sub>Fe<sub>x</sub>Al<sub>3-x</sub>Si<sub>3</sub>O<sub>13</sub>H</p> </td> </tr> <tr> <td> <p>Sample source</p> </td> <td> <p>Natural source from Val d'Ossola, Italy</p> </td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td> <p>Powder crushed in an agate mortar and deposited on a Cu grid with lacey C film</p> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p>Continuous Rotation</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td> <p>293 K</p> </td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td> <p>5</p> </td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p>See cRED_log.txt in each dataset</p> </td> </tr> <tr> <td> <p>tilt range, tilt step, tilt per frame</p> </td> <td> <p>See cRED_log.txt in each dataset</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p>See cRED_log.txt in each dataset</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p>Instamatic</p> </td> </tr> <tr> <td> <p>Software used for processing</p> </td> <td> <p>XDS</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Authorship and bibliography</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Author(s) of the data</p> </td> <td> <p>Lei Wang (ESR 10)</p> </td> </tr> <tr> <td> <p>Related data</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Publication(s)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Files and data formats</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Image folder</p> </td> <td> <p>REDp: Folder containing images of the diffraction pattern from each frame</p> <p>XDS: Folder containing images of the diffraction pattern from each frame</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p>mrc and img</p> </td> </tr> <tr> <td> <p>Additional folders/files</p> </td> <td> <p>Crystal image : image of the crystal</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>cRED_log: log file of data collection</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>1.ed3d: input file for REDp</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>XDS.INP: input file for the program XDS</p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p><strong>Notes:</strong></p> <p>*Project Label "RR-1" stands for Round Robin 1</p> <p>*RR-1 contains 5 individual datasets.</p> </td> </tr> </tbody> </table>
Ibuprofen - Sample 3 NanED Round Robin, Data: ESR10
<p><strong><em>Ibuprofen</em></strong></p> <p>The following submission contains the data collection of datasets for the sample ibuprofen under the NanEd round-robin project. 10 crystals were identified, and datasets were collected using the continuous rotation data acquisition technique at liquid nitrogen temperature. All the datasets were processed with XDS software. The table below summarizes the data collection parameters for the datasets. The following data is also in the data folder as a Word file.</p> <p> </p> <p><strong>Continuous Rotation:</strong></p> <table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p>ESR10 - Round Robin</p> </td> </tr> <tr> <td> <p>Project Label</p> </td> <td> <p>RR-3</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td> <p>RR-3_SU</p> </td> </tr> <tr> <td> <p>Data set Label</p> </td> <td> <p>RR3-1 to 10</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td> <p>Transmission electron microscope JEOL 2100 LaB6,</p> <p>Gatan 914 cryo-holder</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p>LaB6</p> </td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td> <p>200 kV</p> </td> </tr> <tr> <td> <p>Wavelength</p> </td> <td> <p>0.0251 Å</p> </td> </tr> <tr> <td> <p>Probe Type</p> </td> <td> <p>Parallel beam</p> </td> </tr> <tr> <td> <p>Beam Diameter</p> </td> <td> <p>6 μm</p> </td> </tr> <tr> <td> <p>Beam Convergence</p> </td> <td> <p>Parallel beam, convergence <0.1mrad</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>Hybrid pixel detector ASI Timepix (bottom mounted)</p> </td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td> <p>512 x 512</p> </td> </tr> <tr> <td> <p>Pixel size</p> </td> <td> <p>55 µm x 55 µm</p> </td> </tr> <tr> <td> <p>Effective camera length</p> </td> <td> <p>250 mm, 300 mm</p> </td> </tr> <tr> <td> <p>Calibration constant</p> </td> <td> <p>0.004990 Å<sup>-1</sup>/pixel, 0.004120 Å<sup>-1</sup>/pixel</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Sample description:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Name</p> </td> <td> <p>Ibuprofen</p> </td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td> <p>C<sub>13</sub>H<sub>18</sub>O<sub>2</sub></p> </td> </tr> <tr> <td> <p>Sample source</p> </td> <td> <p>Thermo Scientific (catalog. No: 333200050, lot: A0424385)</p> </td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td> <p>Powder crushed in an agate mortar and deposited on a Cu Quantifoil grid, 300 mesh, R3.5/1.</p> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p>Continuous Rotation</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td> <p>95.15 K</p> </td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td> <p>10</p> </td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p>See cRED_log.txt in each dataset</p> </td> </tr> <tr> <td> <p>tilt range, tilt step, tilt per frame</p> </td> <td> <p>See cRED_log.txt in each dataset</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p>See cRED_log.txt in each dataset</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p>Instamatic</p> </td> </tr> <tr> <td> <p>Software used for processing</p> </td> <td> <p>XDS</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Authorship and bibliography</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Author(s) of the data</p> </td> <td> <p>Lei Wang (ESR 10)</p> </td> </tr> <tr> <td> <p>Related data</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Publication(s)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Files and data formats</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Image folder</p> </td> <td> <p>REDp: Folder containing images of the diffraction pattern from each frame</p> <p>XDS: Folder containing images of the diffraction pattern from each frame</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p>mrc and img</p> </td> </tr> <tr> <td> <p>Additional folders/files</p> </td> <td> <p>Crystal image : images of the crystal</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>cRED_log: log file of data collection</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>1.ed3d: input file for REDp</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>XDS.INP: input file for the program XDS</p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p><strong>Notes:</strong></p> <p>*Project Label "RR-3" stands for Round Robin 3</p> <p>*RR-3 contains 10 individual datasets.</p> </td> </tr> </tbody> </table>
Phylogenomic data exploration with increased sampling provides new insights into the higher-level relationships of butterflies and moths (Lepidoptera)
<p>Genomes, alignments and tree files of the study "Phylogenomic data exploration with increased sampling provides new insights into the higher-level relationships of butterflies and moths (Lepidoptera). Molecular Phylogenetics and Evolution, https://doi.org/10.1016/j.ympev.2024.108113".</p>
Data from: A comparison of non-destructive visceral swab and tissue biopsy sampling methods for genotyping-by-sequencing in the freshwater mussel Fusconaia askewi
<p>Limiting harm to organisms via genetic sampling is an important consideration for rare species. Nondestructive sampling techniques have been developed to address this issue in freshwater mussels. Two methods, visceral swabbing and tissue biopsies, have proven to be effective for DNA sampling, though it is unclear as to which method is preferable for genotyping-by-sequencing (GBS). Tissue biopsies may cause undue stress and damage to organisms, while visceral swabbing potentially reduces the chance of such harm. Our study compared the efficacy of these two DNA sampling methods for generating GBS data for the Unionid freshwater mussel, Texas Pigtoe (<em>Fusconaia askewi</em>). Our results find both methods generate quality sequence data, though some considerations are in order. Tissue biopsies produced significantly higher DNA concentrations and larger numbers of reads when compared to swabs, though there was no significant association between starting DNA concentration and number of reads generated. Swabbing produced greater sequence depth (more reads per sequence) while tissue biopsies revealed greater coverage across the genome (at lower sequence depth). Patterns of genomic variation as characterized in principal component analyses were similar regardless of the sampling method, suggesting that the less invasive swabbing is a viable option for producing quality GBS data in these organisms.</p>
1. Files containing data from each sample studied and ready to be open in Thellier Tool 4.0 software (Leonardt et al., 2004) for evaluation and determination of archaeointensity and archaeoinclination.
<p>Files containing data from each sample studied and ready to be open in Thellier Tool 4.0 software (Leonardt et al., 2004) for evaluation and determination of archaeointensity and archaeoinclination.</p> <p>Data supporting manuscript entitled “Archaeomagnetic studies of bricks from ancient buildings sampled in SE Poland (Central Europe)” by J. Nawrocki, K. Standzikowski, M. Chadima, T. Werner, M. Łanczont, J. Gancarski, Z. Gil submitted to Journal of Archaeological Science: Reports (JASREP-D-23-00078).</p>
Sample data for "Urban Dynamics Through the Lens of Human Mobility"
<p>Sample data in Boston for paper "Urban Dynamics Through the Lens of Human Mobility".</p> <p> </p>
Data for: Soil organic carbon contents of collected soil samples from China's black soil region
<p><span>The long-term use of cropland and cropland reclamation from natural ecosystems led to soil degradation. This study investigated the effect of the long-term use of cropland and cropland reclamation from natural ecosystems on soil organic carbon (SOC) content and density over the past 35 years. Altogether, 2140 topsoil samples (0</span>–<span>20 cm) were collected across Northeast China. Landsat images were acquired from 1985 to 2020 through Google Earth Engine, and the reflectance of each soil sample was extracted from the Landsat image that its time was consistent with sampling. The hybrid model that included two individual SOC prediction models for two clustering regions was built for accurate estimation after k-means clustering. The probability hybrid model, a combination between the hybrid model and classification probabilities of pixels, was introduced to enhance the accuracy of SOC mapping. Cropland reclamation results were extracted from the land cover time series dataset at a 5-year interval. Our study indicated that: (1) Long-term use of cropland led to a 3.07 g kg<sup>-1</sup> and 6.71 Mg C ha<sup>-1</sup> decrease in SOC content and density, respectively, and the decrease of SOC stock was 0.32 Pg over the past 35 years; (2) Nearly 64% of cropland had a negative change in terms of SOC content from 1985 to 2020; (3) Cropland reclamation track changed from high to low SOC content, and almost no cropland was reclaimed on the 'Black soils' after 2005; (4) Cropland reclamation from wetlands resulted in the highest decrease, and reclamation period of years 31</span>–<span>35 decreased when SOC density and SOC stock were 16.05 Mg C ha<sup>-1</sup> and 0.005 Pg, respectively, while reclamation period of years 26</span>–<span>30 from forest witnessed SOC density and stock decreases of 8.33 Mg C ha<sup>-1</sup> and 0.01 Pg, respectively. Our research results provide a reference for SOC change in the black soil region of Northeast China and can attract more attention to the area of the protection of 'Black soils' and natural ecosystems.</span></p>
GSD Sample Data - Extra Small
<p>GSD Open Source Sample Data as found in the Beeline software package but has been reduced to only a 100 cells. It was likely generated using a Boolean Network method described in Ríos, O., Frias, S., Rodríguez, A. <em>et al.</em> (2015). All gene names have been normalized to their NCBI identifiers and any NCBI IDs that contain parentheses such as NCBI:12796(-KTS) or NCBI:12796(+KTS) denoted a sequence variant described by the string within the parentheses.</p>
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