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118 results for “streamlines”
Data for common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline
<p>This upload contains the HZV029 Plasma and HZV029 Two-Phase dataset for reviewers of the "Data for common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline" submission. </p> <p>Both datasets will be uploaded to metabolomics workbench and the upload completed before final publication of the manuscript. For the he HZV029 Plasma datasets only the final run is included for any sample (i.e., failed injections or other samples with data quality issues that were reran during acquisition were omitted).</p> <p>Also included in the upload is the source code for the MetDataModel and the pcpfm at the time of manuscript re-submission and the pcpfm itself. If you find this upload in the future, please check out the github repos for more updated versions:</p> <p>https://github.com/shuzhao-li-lab/PythonCentricPipelineForMetabolomics</p> <p>https://github.com/shuzhao-li-lab/metDataModel</p> <p>The github repo does not store the input the data for space reasons, they only have the notebooks. However, the .zip here has both the notebooks by themselves in the notebook subdirectory and a separate directory with the notebooks and the data used to generate all the figures and results in the manuscript.</p> <p><strong>Some information that is needed to rerun this analysis:</strong></p> <p>Sequence files are critical to the functioning of the pipeline. The sequence files for all analyses are provided under sequence_files.zip. These can be used to recapitulate the analysis by eitehr changing the filepath to each acquisition to where you put it on your sytem or by placing the sequence file in the same directory as the mzml or raw. In the latter case, the pipeline will search for filenames matching the sample names. The sequence files also store some sample metadata such as the type of sample a given acquisition is (unknown, pooled, qc, etc...)</p> <p>.raw to .mzML conversion works well on MacOS but may not work well on other systems. You will need to use the ability to specify your own conversion command or convert files outside of the pipeline. </p> <p>To replicate the results, you do need to have the annotation sources downloaded which can be done using the pipeline. MS2 annotation requires the files in the AcquireX directory which is MS2 acquisitions on pooled HZV029 plasma samples.</p> <p>For the comparison between MetaboAnalystR and the pcpfm, subsets of the datasets were used. These subsets and the sequence files are in Subsets_for_performance_testing.zip. The sequences are also in the sequence_files directory as well</p> <p>The notebooks reference data in the analysis folders. Copies of these files are located with the notebooks to ease reproduction of the exact results in the paper; however, to do so, you will need to change paths to this data in the notebook. This lets the notebooks be ran during a rerun without copying intermediates back and forth and it keeps the github repo clean.</p> <p><strong>Version History:</strong></p> <p>This version is after reviewer comments and is for resubmission.</p> <p> </p> <p><strong>Contributions:</strong></p> <p>Joshua M Mitchell implemented the pipeline and was first author on the manuscript. Shuzhao Li is the corresponding author on the manuscript. </p> <p>Maheshwor Thapa performed the experiments to collect the HZV029 data. Yuanye Chi helped with testing and documenting the pipeline. </p> <p>Jiangou (Jeff) Xia and Zhiqiang Pang provided the R portion of the analysis. </p>
Processed data from SnoHATS and METCRAX II: anisotropic turbulence and geometry of the Reynolds stress tensor in a streamline coordinate system
<p>Datasets used for the paper 'Interpreting turbulence anisotropy in a streamline coordinate system'. Data from SnoHATS and METCRAX II field campaigns. Datasets include turbulent quantities calculated on 30- and 1-min averaging windows for unstable and stable conditions, with prior linear detrending. Planar fit was used in METCRAX II and double rotation in SnoHATS to rotate the flow into the mean wind direction. Datasets include quantities to characterize the anisotropy of the Reynolds stress tensor, such as eigenvalues, eigenvectors, and the angles between the eigenvectors and the streamline coordinate system, defined in the direction of the mean wind vector.</p> <p>1c: one-component Reynolds stress tensor</p> <p>2c: two-component axisymmetric Reynolds stress tensor</p> <p>3c: isotropic Reynolds stress tensor</p>
Tryps-IN: A streamlined palaeoproteomics workflow enables ZooMS analysis of 10,000-year-old petrous bones from Jordan rift-valley
<p>Poor preservation of collagen in dry and/or arid environments has hindered the application of Zooarchaeology by mass spectrometry (ZooMS) analysis in many regions of the world, and as a result many zooarchaeological investigations have relied exclusively on the morphological assessment of fragmentary remains, due to the inadequate preservation of biomolecules. The climatic conditions of Southwest Asia include extreme temperature fluctuations unconducive to preservation of proteins and DNA. We performed zooarchaeological analysis of remains from the 10,000-year-old site of Shkārat Msaied in Jordan and sub-sampled twenty-eight petrous bones, the hardest bone in the mammalian skeleton, for species identification by ZooMS. Using an unconventional and simplified extraction protocol we call Tryps-IN, in which digestion was performed without removal of the demineralising EDTA, we taxonomically identified several fragments, outperforming the established ZooMS work-flow. A subset of identifications was subsequently confirmed using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) protein sequencing. The new methodology presented here opens the possibility of further bioarchaeological investigation of other fragmentary faunal assemblages within this region of archaeological significance. </p>
Supplementary Data for "Streamlining Vocabulary Conversion to SKOS: A YAML-based Approach to Facilitate Participation in the Semantic Web"
<p>This dataset contains quality assessment results for 26 vocabularies. The assessment was conducted using the <a href="https://skos-play.sparna.fr/skos-testing-tool/">qSKOS vocabulary quality assessment tool</a>.</p> <p>The 26 assessed vocabularies were converted from their original formats into the Simple Knowledge Organization System (SKOS) data model using the approach described in our paper titled <a href="https://doi.org/10.1007/978-3-031-62362-2_9">"Streamlining Vocabulary Conversion to SKOS: A YAML-based Approach to Facilitate Participation in the Semantic Web"</a>, presented at the <a href="https://doi.org/10.1007/978-3-031-62362-2">24th International Conference on Web Engineering (ICWE 2024)</a>.</p> <p>The dataset contains a quality assessment for the following vocabularies:</p> <ol> <li>A Taxonomy of Evaluation Towards Standards</li> <li>Cross-Device Taxonomy</li> <li>What Makes a Data-driven Business Model? A Consolidated Taxonomy</li> <li>DDI Aggregation Method</li> <li>DDI Mode of Collection</li> <li>Building a New Taxonomy for Data Discretization Techniques</li> <li>Demopaedia</li> <li>Data Science Glossary</li> <li>A Taxonomy of Evaluation Approaches in Software Engineering</li> <li>Evaluation Thesaurus</li> <li>The Glossary of Human Computer Interaction</li> <li>Human-Factors Taxonomy</li> <li>A Taxonomy to Structure and Analyze Human–Robot Interaction</li> <li>A Taxonomy of Interaction for Instructional Multimedia</li> <li>A Taxonomy of Interrogation Methods</li> <li>Design Vocabulary for Human–IoT Systems Communication</li> <li>Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors</li> <li>Thesaurus Mass Communication</li> <li>Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey</li> <li>A Taxonomy of Quality of Service and Quality of Experience of Multimodal Human-Machine Interaction</li> <li>A Human-Centered Taxonomy of Interaction Modalities and Devices</li> <li>A Taxonomy of Spatial Interaction Patterns and Techniques</li> <li>A Taxonomy of Social Errors in Human-Robot Interaction</li> <li>Taxonomy of Digital Research Activities in the Humanities</li> <li>Virtual Reality and the CAVE: Taxonomy, Interaction Challenges and Research Directions </li> <li>Cross-Device Interaction</li> </ol>
Kuempel et al. (2024) Quantifying global redundant fisheries trade to streamline seafood supply chains
<p>Species trade data to accompany the publication "Quantifying global redundant fisheries trade to streamline seafood supply chains" by Kuempel et al. (2024) and the associated github repository https://github.com/cdkuempel/Redundant_fisheries_trade</p> <p> </p>
Dataset: Streamline Health Solutions, Inc. (STRM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 12 in Palaeobiology and evolutionary context of Angulithes mermeti, a streamlined early Late Cretaceous shallow-water nautiloid
Fig. 12. Comparison of whorl sections, position of siphuncles, sutural sinuosity, and septal spacing of Angulithes mermeti (Coquand, 1862) (A) and a specimen illustrated as Nautilus pompilius Linnaeus, 1758 in the Treatise (Kummel 1964: fig. 329) (B). A. AFK 225 from the upper Cenomanian of Egypt in lateral (A1) and apertural (A2) views; external suture (A3). B. Specimen from Tagnan (Philippines) at D = 130 mm from the SW Pacific (drawn from an artificial internal mould) (B1, B2); suture line (B3) (modified from Wani et al. 2008).
Fig. 11 in Palaeobiology and evolutionary context of Angulithes mermeti, a streamlined early Late Cretaceous shallow-water nautiloid
Fig. 11. Geochronology (Ogg and Hinnov 2012), ammonite biostratigraphy (Wright and Kennedy 2017) and sequence stratigraphy (Robaszynski et al. 1998; Wilmsen 2003) of the Cenomanian Stage plotted against evolutionary trends in the hercoglossid nautiloid genus Angulithes. See text for further explanations.
Fig. 2 in Palaeobiology and evolutionary context of Angulithes mermeti, a streamlined early Late Cretaceous shallow-water nautiloid
Fig. 2. Shell parameters, suture terminology, and biometric factors of the planispiral nautiloid shell (modified after Wilmsen 2016). Abbreviations: Dmax, maximum diameter; U, umbilical width at Dmax; Wb, whorl breadth at Dmax; Wh, whorl height at Dmax.
Fig. 9 in Palaeobiology and evolutionary context of Angulithes mermeti, a streamlined early Late Cretaceous shallow-water nautiloid
Fig. 9. Reconstruction of the life position of the hercoglossid nautilid Angulithes mermeti (Coquand, 1862) in sagittal cross-section (A1) and apertural view (A2) (approximately 1/2 of natural size).
Fig. 6 in Palaeobiology and evolutionary context of Angulithes mermeti, a streamlined early Late Cretaceous shallow-water nautiloid
Fig. 6. Shell form (A1–C1), external sutures (A2–C2), and whorl cross-section (D) of selected hercoglossid nautiloids. A. Nautilus mermeti (Coquand, 1862) from the Cenomanian of Ténoukla near Tébassa, northeast Algeria (after Coquand 1862: pl. 2: 1, 2). B. Nautilus munieri Choffat, 1886 from the upper Cenomanian of Villa Nova d'Ourem, Portugal (after Choffat 1886: pl. 2: 1). C. Angulithes triangularis Montfort, 1808 (MB.C.2052) from the lower Sardinero Formation, lower Middle Cenomanian of Langre, Cantabria, northern Spain (after Wilmsen 2000: pl. 1: 2b, pl. 5: 15). D. Nautilus triangularis Montfort, 1808 from the Cenomanian of Île de Madame, Charente-Maritime, France (after d'Orbigny 1840: pl. 12: 2).
Fig. 4 in Palaeobiology and evolutionary context of Angulithes mermeti, a streamlined early Late Cretaceous shallow-water nautiloid
Fig. 4. Hercoglossid nautiloid Angulithes mermeti (Coquand, 1862) from the Cenomanian of Wadi Ghonima, Egypt. A. AFK 202 in lateral (A1, A3) and ventral (A2) views. B. AFK 218 in lateral (B1) and apertural (B2) views; arrow shows the position of the siphuncle.
Fig. 3 in Palaeobiology and evolutionary context of Angulithes mermeti, a streamlined early Late Cretaceous shallow-water nautiloid
Fig. 3. Hercoglossid nautiloid Angulithes mermeti (Coquand, 1862), AFK 225 from the Cenomanian of Wadi Ghonima, Egypt, in apertural (A1) and lateral (A2) views.
Fig. 8 in Palaeobiology and evolutionary context of Angulithes mermeti, a streamlined early Late Cretaceous shallow-water nautiloid
Fig. 8. Westermann morphospace diagram, simplified and modified after Ritterbush et al. (2014), with placement of the hercoglossid nautiloid Angulithes mermeti (Coquand, 1862) (see Table 2 for raw data; black circle indicates mean value); for comparison, two specimens of Nautilus pompilius Linnaeus, 1758 are plotted in the diagram, too (1, specimen 17 of Tajika et al. 2015; 2, an early Pleistocene specimen from Wani et al. 2008).Abbreviations: Th, shell inflation; U, umbilical exposure; w, whorl expansion.
Fig. 5 in Palaeobiology and evolutionary context of Angulithes mermeti, a streamlined early Late Cretaceous shallow-water nautiloid
Fig. 5. Cross-sections and external sutures of the hercoglossid nautiloid Angulithes mermeti (Coquand, 1862) from the Cenomanian of Wadi Ghonima, Egypt. A. AFK 225, shell shape in apertural view (A1), external sutures (A2); grey shading indicates position of the umbilical saddle. B. AFK 202, shell shape in ventral view. C. AFK 218, whorl shape, showing the position of the siphuncle.
Fig. 10 in Palaeobiology and evolutionary context of Angulithes mermeti, a streamlined early Late Cretaceous shallow-water nautiloid
Fig. 10. Palaeoecology of the hercoglossid nautiloid Angulithes mermeti (Coquand, 1862). A. Reconstruction of A. mermeti (Coquand, 1862) in the lagoonal shallow-water environment of the Galala Formation (background after a subaqueous photograph in the property of MW from a lagoonal site in the present-day Red Sea near Hughhada taken in 1999, treated by greyscale-filtering in Photoshop CS2); rudist illustrations from Mitchell (2002). B. Bioclastic rudist (r) floatstone, the lagoonal host sediment in which A. mermeti has been found in the Wadi Ghonima section (thin-section photomicrograph of sample 080217-18). C. Close-up of Fig. 9B showing the bioclastic packstone matrix in detail, including numerous fragments of dasycladalean algae (gr).
Fig. 7 in Palaeobiology and evolutionary context of Angulithes mermeti, a streamlined early Late Cretaceous shallow-water nautiloid
Fig. 7. Palaeobiogeographical distribution of the hercoglossid nautiloid Angulithes mermeti (Coquand, 1862). Cenomanian palaeogeographical and plate tectonic situation modified after Barrier and Vrielynck (2008); nautiloid occurrences are indicated by asterisks (see text for literature sources). Abbreviations: APB, Anglo-Paris Basin; MEI, Mid-European Island,
Fig. 1 in Applying a modified streamlined disease risk analysis framework to a platypus conservation translocation, with special consideration for the conservation of ecto- and endoparasites
Fig. 1. An outline of how parasite conservation can be considered in translocation planning, reproduced from Carlson et al. (2020).
Linked collectors and determiners for: Streamlining the use of BOLD specimen data to record species distributions: a case study with ten Nearctic species of Microgastrinae (Hymenoptera: Braconidae).
Natural history specimen data linked to collectors and determiners held within, "Streamlining the use of BOLD specimen data to record species distributions: a case study with ten Nearctic species of Microgastrinae (Hymenoptera: Braconidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3a8a3458-675f-46f1-abbc-6e089059e5e8">https://bionomia.net/dataset/3a8a3458-675f-46f1-abbc-6e089059e5e8</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3a8a3458-675f-46f1-abbc-6e089059e5e8">https://gbif.org/dataset/3a8a3458-675f-46f1-abbc-6e089059e5e8</a>. Formatted as a Frictionless Data package.
Genome streamlining: effect of mutation rate and population size on genome size reduction: simulated data
<p>Lineages data of populations simulated with Aevol (<a href="https://gitlab.inria.fr/aevol/aevol">https://gitlab.inria.fr/aevol/aevol</a>), and the Wild-Types sequences used for that.</p> <p>Conditions: change of mutation rate, population size, or both.<br>Mutational bias: none, insertion bias or deletion bias</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.