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71 results for “bulk sample”
Fig. 10 in Biodiversity of museum and bulk field samples compared: The Chiampo sponge fauna (Eocene, Lessini Mountains, Italy)
Fig. 10. Percent similarity (A) and Jaccard similarity (B) measurements between bulk and museum collection material at different taxonomic levels.
Fig. 3 in Biodiversity of museum and bulk field samples compared: The Chiampo sponge fauna (Eocene, Lessini Mountains, Italy)
Fig. 3. Bulk collection of Eocene sponges at Lovara Quarry. Some of the sponge specimens are indicated by white arrows.
Fig. 1 in Biodiversity of museum and bulk field samples compared: The Chiampo sponge fauna (Eocene, Lessini Mountains, Italy)
Fig. 1. Sponge fossils from the Eocene of Chiampo. A. The lyssacinosid Stauractinella eocenica Frisone, Pisera, and Preto, 2016. B. The lychniscosid Callicylix eocenicus Pisera and Busquets, 2002. C. The lithistid demosponge Platychonia sp. D. The hexactinosid Laocoetis patula Pomel, 1872. Scale bars 10 mm. Photos S. Castelli.
Linked collectors and determiners for: NEON Biorepository Zooplankton Collection (Remaining Bulk Taxonomy Sample).
Natural history specimen data linked to collectors and determiners held within, "NEON Biorepository Zooplankton Collection (Remaining Bulk Taxonomy Sample)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/60526e0c-6ef7-4610-9027-e604d448b959">https://bionomia.net/dataset/60526e0c-6ef7-4610-9027-e604d448b959</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/60526e0c-6ef7-4610-9027-e604d448b959">https://gbif.org/dataset/60526e0c-6ef7-4610-9027-e604d448b959</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: NEON Biorepository Zooplankton Collection (Unsorted Bulk Sample).
Natural history specimen data linked to collectors and determiners held within, "NEON Biorepository Zooplankton Collection (Unsorted Bulk Sample)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/aca0fb62-58f7-4d4e-b4a4-2ec485448490">https://bionomia.net/dataset/aca0fb62-58f7-4d4e-b4a4-2ec485448490</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/aca0fb62-58f7-4d4e-b4a4-2ec485448490">https://gbif.org/dataset/aca0fb62-58f7-4d4e-b4a4-2ec485448490</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: NEON Biorepository Benthic Macroinvertebrate Collection (Unsorted Bulk Sample).
Natural history specimen data linked to collectors and determiners held within, "NEON Biorepository Benthic Macroinvertebrate Collection (Unsorted Bulk Sample)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/591c4da7-967c-4cad-ad60-b276e60fb4d2">https://bionomia.net/dataset/591c4da7-967c-4cad-ad60-b276e60fb4d2</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/591c4da7-967c-4cad-ad60-b276e60fb4d2">https://gbif.org/dataset/591c4da7-967c-4cad-ad60-b276e60fb4d2</a>. Formatted as a Frictionless Data package.
Synchrotron X-ray Diffraction Results - Measuring Bulk Crystallographic Texture from Differently-Orientated Ti-6Al-4V Samples
<p>A dataset of crystallographic texture results for both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases, measured from six differently orientated Ti-6Al-4V (Ti-64) samples, using two different analysis techniques of synchrotron X-ray diffraction (SXRD) data. The texture results are produced from two refinement methods for fitting intensities from SXRD pattern images; an established Rietveld refinement method using the software package <a href="http://maud.radiographema.eu">MAUD (Materials Analysis Using Diffraction)</a> and a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package. The texture results were also compared with electron backscatter diffraction (EBSD) measurements from a single sample orientation. The SXRD and EBSD textures were analysed using <a href="https://mtex-toolbox.github.io">MTEX</a> to enable a direct comparison of the pole figures, orientation distribution functions (ODFs) and numerical values for the texture indices. The SXRD texture is calculated 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. The texture variation measured using stage-scan SXRD is directly compared with EBSD, by splitting up the EBSD map into an equivalent grid matrix using an automated script in MTEX.</p> <p><strong>Material </strong></p> <p>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. The run numbers from the experiment reference six different sample orientations, according to their 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.</p> <p><strong>MAUD / MTEX Analysis</strong></p> <p>The α and β phase texture for each of the six different sample orientations was calculated using MAUD, included in this <a href="https://doi.org/10.5281/zenodo.7311323">analysis dataset</a>, which produced ODFs in the form of text files. The texture files were analysed in MTEX using scripts from the <a href="https://github.com/LightForm-group/MAUD-batch-analysis">MAUD-batch-analysis</a> package, for plotting of the pole figures and ODF slices, along with calculation of pole figure maxima, ODF maxima and texture indices. The same procedure was used to analyse texture from all six orientations together; using MTEX to fit a single ODF text file. And a series of ODF text files were analysed to calculate texture variation from an X-Y stage scan of Sample 1 (103845). Two different ODF resolutions of 5º and 15º were initially used to fit the texture in MAUD, with the same ODF resolution applied to analyse the data in MTEX. However, an ODF resolution of 15° was found to reproduce the most reasonable texture strength intensity values, with the closest match to the EBSD results.</p> <p><strong>Continuous-Peak-Fit / MTEX Analysis </strong></p> <p>The lattice plane intensities for 21 α and 4 β phase peaks were extracted from the Continuous-Peak-Fit analysis, included in this <a href="https://doi.org/10.5281/zenodo.7311323">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 and texture indices. The same procedure was used to analyse texture from all six orientations together, along with combinations of different sample orientations, by fitting combined lattice intensity text files in MTEX. And a series of lattice intensity text files were analysed to calculate texture variation from the X-Y stage scan of Sample 1 (103845). Lattice plane intensity distributions which had been normalised to a Ti-64 powder sample measurement were also analysed, to see if this had any effect on the texture intensities. Nevertheless, the powder-corrected texture was found to exactly match the raw intensity measurements. Three different ODF resolutions of 5º, 10º and 15º were initially used to fit the texture in MTEX. However, a kernel half-width of 10° was found to produce optimal data fitting, for highly accurate texture strength intensity values.</p> <p><strong>EBSD / MTEX Analysis </strong></p> <p>The indexed α-phase EBSD measurements were recorded over an area of around 100 mm<sup>2</sup>, with an equivalent sized map of β-phase orientations reconstructed from the data. Both the α and the β phase maps were analysed using the <a href="https://github.com/LightForm-group/MTEX-texture-block-analysis">MTEX-texture-block-analysis</a> package, which was used to split up the map into 387 individual square sections, with equivalent dimensions to the SXRD stage-scan measurement grid. For each of the 387 sections, MTEX was used to plot pole figures and ODF slices, and to calculate pole figure maxima, ODF maxima and texture indices.</p> <p><strong>Texture Variation Comparison</strong></p> <p>The texture values calculated from the SXRD stage scan measurements, with the two analysis methods, were used for a direct comparison with the texture variation recorded using EBSD. This analysis was recorded in the <a href="https://github.com/LightForm-group/texture-strength-comparison">texture-strength-comparison</a> package. The results show differences in texture variation across the piece depending on the method used to analyse the SXRD data. The Continuous-Peak-Fit analysis method shows the closest match with EBSD, producing clear texture intensity spikes for the different α and β lattice plane pole figure intensities, ODF maxima and texture indices, at the centre of the piece. The results were also used to develop SXRD maps showing the distribution of texture intensities across the sample.</p> <p><strong>Metadata </strong></p> <p>An accompanying YAML text file contains associated processing metadata for the SXRD and EBSD analyses, recording information about the different packages used to process the data, along with details about the different files contained within this results dataset.</p>
Bulk Carbon and Amino Acid nitrogen isotope data from Baltic cod (Gadus morhua) and European flounder (Platichthys flesus) muscle tissue samples from the western and central Baltic Sea
<p><span>Eutrophication, increased temperatures and stratification can lead <span>to massive, filamentous, N<sub>2</sub>-fixing cyanobacterial (FNC) blooms in coastal ecosystems with largely unresolved consequences for the mass and energy supply in pelagic and benthic food webs. Mesozooplankton adapt to not top-down controlled FNC blooms by switching diets from phytoplankton to microzooplankton, resulting in a directly quantifiable increase in its trophic position (TP) from 2.0 (herbivore) to as high as 3.0 (carnivore). If this process in mesozooplankton, we call trophic lengthening, was transferred up to higher trophic levels of a food web, a large loss of energy could result in massive declines of fish biomass. </span></span><span>We used compound-specific nitrogen stable isotope data of amino acids (CSIA) to estimate and compare </span><span>the nitrogen (N) sources and TPs of cod and flounder (mesopredators) from areas</span><span> </span><span>with influence of FNC blooms (central Baltic Sea) and without it (western Baltic Sea)</span><span>. We tested if FNC-caused </span><span>trophic lengthening in mesozooplankton is carried over to fish.</span><span> The TP of cod from the western Baltic, feeding mainly on decapods, was equal to the global mean value (4.1, secondary carnivore). Only cod from the central Baltic, mainly feeding on zooplanktivorous pelagics, had a higher TP (4.8, near-tertiary carnivore), indicating a strong carry-over effect of </span><span>FNC-</span><span>caused trophic lengthening from mesozooplankton. In contrast, the TP of molluscivorous flounder (3.2 ± 0.2 in both areas), associated with the benthic food web, was unaffected by trophic lengthening. This suggests that FNC blooms cause a large loss of energy in zooplanktivorous but not in molluscivorous mesopredators. If FNC blooms continue to detour energy at the base of the pelagic food web, the TP of cod will not return to global mean values and the fish stock not recover. Monitoring the TP of key species can identify fundamental changes in ecosystems and provide useful information for resource management.</span></p>
03_HTMD_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes. </p> <p># The forders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p> <p> </p> <p> </p>
05_HTMD_Cavity_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output, and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes. </p> <p># The folders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, *run_adaptiveMD.py* : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p>
Bulk Carbon and Amino Acid nitrogen isotope data from Baltic cod (Gadus morhua) and European flounder (Platichthys flesus) muscle tissue samples from the western and central Baltic Sea
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Data from: An accessible metagenomic strategy allows for better characterization of invertebrate bulk samples
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Biodiversity Soup II: A bulk‐sample metabarcoding pipeline emphasizing error reduction
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Image-based taxonomic classification of bulk biodiversity samples using deep learning and domain adaptation
<p>Complex bulk samples of insects from biodiversity surveys present a challenge for taxonomic identification, which could be overcome by high-throughput imaging combined with machine learning for rapid classification of specimens. These procedures require that taxonomic labels from an existing source data set are used for model training and prediction of an unknown target sample. However, such transfer learning may be problematic for the study of new samples not previously encountered in an image set, e.g. from unexplored ecosystems, and require methods of domain adaptation that reduce the differences in the feature distribution of the source and target domains (training and test sets). We assessed the efficiency of domain adaptation for family-level classification of bulk samples of Coleoptera, as a critical first step in the characterisation of biodiversity samples. Neural network models trained with images from a global database of Coleoptera were applied to a biodiversity sample from understudied forests in Cyprus as the target. Within-dataset classification accuracy reached 98% and depended on the number and quality of training images and on dataset complexity. The accuracy of between-datasets predictions (across disparate source-target pairs that do not share any species or genera) was at most 82% and depended greatly on the standardisation of the imaging procedure. Algorithms for domain adaptation significantly improved the prediction performance of models trained by non-standardised, low-quality images. Our findings demonstrate that existing databases can be used to train models and successfully classify images from unexplored biota, but the imaging conditions and classification algorithms need careful consideration.</p>
Supplementary material 7 from: {"en": "Buchner D, Haase P, Leese F (2021) Wet grinding of invertebrate bulk samples – a scalable and cost-efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics 5: e67533. https://doi.org/10.3897/mbmg.5.67533"}
Figure S2. Baseline-corrected amplification curves (left half) and melting -curves (right half) for A & B) kick-net samples and C & D) malaise trap samples
Image-based taxonomic classification of bulk biodiversity samples using deep learning and domain adaptation
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Bulk geochemistry of beach sand samples, Ria de Vigo (Spain): March 2023
<p>Document compiling the geochemical results (bulk sample) of 10 beach samples collected in the Vigo Estuary, in March 2023. Analysis were carried out in IGME’s laboratory located in Tres Cantos-Madrid. TiO2, MnO, P2O5, LREE, HRRE, As, Ba, Cu, Hf, Li, Nb, Ni, Ta, Sb and V were reported.</p> <p> </p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Bulk geochemistry of beach sand samples, Ria de Vigo (Spain): March 2023</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Document compiling the bulk geochemical results for CRM of 10 beach sediment samples collected in the Ria de Vigo, in March 2023. The analysis were carried out in IGME’s central laboratory, Madrid.</p> <p> </p> <p>Methods:</p> <p> </p> <p>X-Ray Fluorescence (XRF) = TiO2, MnO and P2O5.</p> <p>Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) = LREE, HRRE, As, Ba, Cu, Hf, Nb, Ni, Ta, Sb and V.</p> <p>Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) = Li.</p> <p> </p> <p>Others:</p> <p>LHRR = La+Ce+Pr+Nd+Sm+Eu, HREE = Gd+Tb+Dy+Ho+Er+Tm+Yb+Lu+Y, pc = percentage, µg/g = microgram/gram.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Geochemistry, Marine Placers, Beach Samples, Heavy Minerals, Ria de Vigo, CRM</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Ria de Vigo (Vigo Estuary)</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Marine mineral resources, geology</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>10/10/2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>05/11/2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>XLSX</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>+/- 3 m</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>ETRS89 / UTM zone 29N (EPSG: 25829)</p> </td> </tr> <tr> <td> <p><strong>Constraints related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>Restricted</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>None</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Francisco J. González</p> <p>fj.gonzalez@igme.es </p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> IGME-CSIC</td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Francisco J. González</p> <p>fj.gonzalez@igme.es</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Supplementary material 1 from: Moore MA, Scheible MK, Robertson JB, Meiklejohn KA (2022) Assessing the lysis of diverse pollen from bulk environmental samples for DNA metabarcoding. Metabarcoding and Metagenomics 6: e89753. https://doi.org/10.3897/mbmg.6.89753
Table S1
Supplementary material 2 from: Moore MA, Scheible MK, Robertson JB, Meiklejohn KA (2022) Assessing the lysis of diverse pollen from bulk environmental samples for DNA metabarcoding. Metabarcoding and Metagenomics 6: e89753. https://doi.org/10.3897/mbmg.6.89753
Table S2
Supplementary material 2 from: Majaneva M, Diserud OH, Eagle SHC, Hajibabaei M, Ekrem T (2018) Choice of DNA extraction method affects DNA metabarcoding of unsorted invertebrate bulk samples. Metabarcoding and Metagenomics 2: e26664. https://doi.org/10.3897/mbmg.2.26664
Final species table. :
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.