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10,119 results for “IT Support”
Supporting data to Turek et al. 2023 SOIL https://doi.org/10.5194/egusphere-2023-1077
<p>data_analysis: R scripts for processing data</p><p>lysimeter: reference data on soil water content from lysimeter Zurich Reckenholz and simulated soil moisture</p><p>projections: simulated outputs on water balance and crop compoenents </p><p>soil: soil parameters from lysimeter monolith and estimates of soil hydraulic properties derived from pedotransfer function</p>
How can higher education stakeholders support Edtech?
<p>This video outlines policy recommendations for cross-sector digital transformation in higher education. It highlights the importance of collaboration between policy-makers, stakeholders, EdTech companies, and universities to advance digital technologies in higher education. The findings derive from the ESRC-funded project 'Universities and Unicorns: building digital assets in the higher education industry'.</p>
Supporting Data - Taxonomic reassessment of Tetrapygus niger (Arbacioida, Echinoidea): molecular and morphological evidence support its placement in Arbacia
<p>This dataset contains the accession numbers and links of the sequences of the specimens analyzed by this work, other sequences used for the analyses can be found in the original article. The species from which the sequences were extracted are: Tetrapygus niger Molina, 1782; Arbacia dufresnii Blainville, 1825; Arbacia spatuligera Valenciennes, 1846 and Coelopleurus floridanus A. Agassiz, 1872. The accession numbers for the Cytochrome Oxidase subunit I (COI) and 28S of the nuclear genome are presented separately.</p><p>In addition, the morphological data of Tetrapygus niger (Test diameter, test height and peristome diameter) presented in this study are shown, as well as their collectors, corresponding collection, country and locality.</p>
Dataset and software for support of the article "To Chaos or Not To Chaos: going into the resilience of the ecosystem".
<p>The CAVeg model, as well as its dependencies, and the CAVeg data files with the configurations for the two and three ecophysiological types, including the data needed to perform the Lyapunov exponents calculations with the CRAN-R DChaos package are available in this dataset.</p>
Phlorest phylogeny derived from Chang et al. 2015 'Ancestry-constrained phylogenetic analysis supports the Indo-European steppe hypothesis'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Chang W, Cathcart C, Hall D, & Garrett A. 2015. Ancestry-constrained phylogenetic analysis supports the Indo-European steppe hypothesis. Language, 91(1):194-244.</p> </blockquote>
Phlorest phylogeny derived from Lee & Hasegawa 2011 'Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee S, Hasegawa T (2011) Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages. Proceedings of the Royal Society B: Biological Sciences, 278(1725):3662–9.</p> </blockquote>
Deep learning to extract the meteorological by-catch of wildlife cameras: Supporting data, models and code
<p>This repository contains the data, models and code to train and deploy deep learning models related to the paper "Deep learning to extract the meteorological by-catch of wildlife cameras" published in the journal Global Change Biology (<a href="https://doi.org/10.1111/gcb.17078"><strong>https://doi.org/10.1111/gcb.17078</strong></a>).</p>
Supporting Data: ontophylo: Reconstructing the evolutionary dynamics of phenomes using new ontology-informed phylogenetic methods
<p>This dataset contains all scripts and data for reproducing the analyses of the paper. The README files contain additional information.</p>
Two-Electron Redox Reactivity of Thorium Supported by Redox-Active Tripodal Frameworks
<p>This upload contains raw data (NMR, X-Ray, UV, Electrochemistry and Elemental Analysis) files for the article</p>
Data inputs and results from AI-supported title and abstract screening "Lack of evidence regarding markers identifying acute heart failure in patients with COPD: an AI-supported systematic review"
<p>These comma-separated data files were used to conduct the AI supported screening of [Lack of Evidence Regarding Markers Identifying Acute Heart Failure in Patients with COPD: An AI-supported Systematic Review (working title)], following the methodology described in the publication (URL/doi to be uploaded).</p> <p>These files provide insight into the AI-supported screening process and the choices made by the human reviewer.</p>
Data supporting for: A High-Throughput Framework for Lattice Dynamics
<p><span>We develop an automated high-throughput workflow for calculating lattice dynamical properties </span><span>from first principles including those dictated by anharmonicity. The pipeline automatically calculates </span><span>interatomic force constants (IFC) up to 4th order from perturbed training supercells, and uses the </span><span>IFC to calculate lattice thermal conductivity, coefficient of thermal expansion, and vibrational free </span><span>energy and entropy. It performs phonon renormalization for dynamically unstable compounds to</span><br><span>obtain real effective phonon spectra at finite temperatures and calculates the associated free energy </span><span>corrections.</span> <span>The choice of methods and parameter selection process are done in a manner that </span><span>strikes a balance of computational efficiency and accuracy of results (as assessed through convergence </span><span>testing and comparison to experimental measurements). </span></p> <p><span>The following data includes:</span></p> <ul> <li><span>materials we calculated lattice thermal conductivities (kappa folder)</span></li> <li><span>materials we applied renormalization to get corrected free energies (renormalization folder)</span></li> </ul> <p><span>The target of this dataset is giving users a reference for our inputs and outputs when they want to run our workflow by their own.</span></p>
Data supporting: "Calcium-driven In Silico Inactivation of a Human Olfactory Receptor"
<p>In this repository we deposited trajectories and input files for the paper "Calcium-driven In Silico Inactivation of a Human Olfactory Receptor".</p> <p>The data is organised as follow:</p> <p> </p> <p>DATA:</p> <p>CA / NA / NO_IONS / NEUTRAL</p> <ul> <li>centroid.pdb # centroid calculated with GMX</li> <li>step5_input.gro # input file from CHARMM GUI</li> <li>topol.top # topol file from CHARMM GUI</li> <li>MDPs # folder containing mdp files from CHARMM GUI</li> <li>toppar # folder containing topology files from CHARMM GUI </li> </ul> <p> </p> <p>TRJs:</p> <p>CA / NA / NO_IONS / NEUTRAL</p> <ul> <li>ref.pdb # reference pdb file</li> <li>trj1.xtc # trajectory from replica 1</li> <li>trj2.xtc # trajectory from replica 2</li> <li>trj3.xtc # trajectory from replica 3</li> <li>trj4.xtc # trajectory from replica 4</li> <li>trj5.xtc # trajectory from replica 5</li> <li>trj6.xtc # trajectory from replica 6 (NEUTRAL only)</li> </ul>
Growth of wild cucumber (Echinocystis lobata) in three variants of support (5, 20, 50 cm) and shoot geometry
<p><span>The experiments included three variants of support: 5 cm, 20 cm, and 50 cm. </span><span>The data was collected in the years 2020-2023. After developing their first pair of mature leaves, when the plants were about 20-35 cm in length, the plants were photographed. The shutter was released every 15 minutes. The resulting images were combined into a 10 fps video. The following were used for the analysis: 13 recordings of the 5 cm variant and 12 recordings of the 20 and 50 cm variants.</span></p> <p><span>Next, an analysis was performed based on the resulting video using Tracker</span><span> for kinetic analysis of video objects. The recordings were used to measure the plant growth parameters. The tape measure and point mass tools were used to determine the length of the shoots and to change the position of the apex relative to the areas of the X- and Y-axis photographed over time. To compensate for this, a trend line was drawn (polynomial of the 2nd degree, due to the very good fit, R<sup>2</sup> = 0,975–0,995) and new plant length parameters were calculated. </span></p> <p><span>To enhance the research, cross-sections of the shoots of ten plants were scanned. Samples were taken every 5 cm from the base of the plant. Using ImageJ</span><span> the following parameters were measured: shoot cross-sectional area, tissue area, perimeter, and circularity. </span></p>
Data to Support Predictive Models for Detrital Titanite Provenance with application to the Nanga Parbat syntaxial massif, western Himalaya."
<p>The files published here are metadata that are being used to support a manuscript currently (Mar, 2024) undergoing final reviews in Journal of Geophysical Research: Earth Surface.</p> <p>The intention of these data and code is to support a publication that is about generating a predictive categorisation scheme for the mineral titanite.</p> <p>The code to generate the titanite classification schemes was created in Python3, using Jupyter Notebook. The files also provide more motivation for why a predictive categorisation scheme for the mineral titanite is desirable, and other similar context. Chiefly, the dataset and random forest models published here will allow us to trace titanite in detritus.</p> <p>For info on running Jupyter Notebook, please visit (<a href="https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html">https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html</a>) to seek instructions. We also provide a readme file with some instructions. If you get really stuck, just email the authors.</p> <p>Our Model can be compared to similar previously published works (e.g. <a href="https://doi.org/10.1111/ter.12574">https://doi.org/10.1111/ter.12574</a>). Model was trained using skikit-learn v1.41.</p> <p>The supplementary file "Table_S4_Merged.csv" was used to train and generate the model.</p> <p>Your unknowns must contain the correct elements and labelling for the code to successfully run, these details are provided in the code (Titanite_Random_Forest_Model1_Mar24.ipynb). A template is also provided for you to paste your unknown data into (titanite_data_template.csv)</p> <p>Any new published data are titanite compositional or isotopic data collected by LA-ICP-MS. Description of how those data were collected is given in "OSullivan_et_al_Supp..." file.</p> <p>Some of the data, information and code in this submission has been subject to change after journal review, this is a second version of this content.</p> <p>References for the dataset compilation are provided in File S3.</p> <p>If you have any queries contact:<br>Gary O'Sullivan, Trinity College Dublin</p>
Research data supporting "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains"
<p>Research data supporting the peer-reviewed article "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains" by the same authors.</p>
Choice of the right supporting electrolyte in electrochemical reductions: a principal component analysis
<h2>Introduction</h2> <p>This dataset contains the raw data as well as an HTML-based visualization of our dataset using Python Bokeh. We have also added a feature to highlight commercially available supporting electrolytes. The data is taken from the PubChem database. For each of the 6650 cations, the known neutral compounds in the PubChem dataset were identified with their corresponding anions. For each of these compounds, the vendor information stored in PubChem was queried.</p> <h2>Directory structure</h2> <ul> <li>Raw Data <ul> <li>[<a href="../records/10813969/files/raw_data.tar.xz?download=1" target="_blank" rel="noopener">raw_data.tar.xz</a>] Compressed directory with the output from the automated feature calculation.</li> <li>[<a href="../records/10813969/files/raw_data.csv?download=1" target="_blank" rel="noopener">raw_data.csv</a>] CSV file with the values of the calculated properties of all cations.</li> </ul> </li> <li>Visualization <ul> <li>[<a href="../records/10813969/files/pca_qac_tool_QC.html?download=1" target="_blank" rel="noopener">pca_qac_tool_QC.html</a>] HTML page with Javascript to display PC1 and PC2 for the quantum chemical PCA model.</li> <li>[<a href="../records/10813969/files/pca_qac_tool_RDKit.html?download=1" target="_blank" rel="noopener">pca_qac_tool_RDKit.html</a>] HTML page with Javascript to display PC1 and PC2 for the PCA model based on non empirical RDKit descriptors.</li> </ul> </li> <li>Tools <ul> <li>[<a href="../records/10813969/files/pca_qac_tool.py?download=1" target="_blank" rel="noopener">pca_qac_tool.py</a>] Python script to generate the HTML output using Bokeh. Depends on the data_pca_qac.csv and the data_commercial.json file.</li> <li>[<a href="../records/10813969/files/PubChem_get_Vendor_information.py?download=1" target="_blank" rel="noopener">PubChem_get_Vendor_information.py</a>] Crawler that checks a list of PubChem CIDs for net-neutral compounds and whether they are commercially available.</li> <li>[<a href="../records/10813969/files/RDKit_Descriptor-2D.py?download=1" target="_blank" rel="noopener">RDKit_Descriptor-2D.py</a>] Python script to calculate all available 2D RDkit descriptors based on a list of SMILES strings.</li> <li>[<a href="../records/10813969/files/RDKit_Descriptor-3D.py?download=1" target="_blank" rel="noopener">RDKit_Descriptor-3D.py</a>] Python script to calculate the RDKit 3D descriptors based on the CREST and ORCA GeoOpt geometries.</li> </ul> </li> </ul>
Lipidomics LC-MS analysis support tools for outlier detection
<p>Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in bioinformatics work, and highlights the importance of data-driven outlier detection in assessing spectral outputs – here demonstrated using a machine learning approach based on support vector machine regression combined with leave-one-out cross validation – as well as manual curation, in order to identify software-driven errors driven by closely related lipids and by co-elution issues.</p> <p>The lipidomics case study dataset used in this work analysed a lipid extraction of a human pancreatic adenocarcinoma cell line (PANC-1, Merck, UK, cat no. 87092802) analysed using an Acquity M-Class UPLC system (Waters, UK) coupled to a ZenoToF 7600 mass spectrometer (Sciex, UK). Raw output files are included alongside processed data using MS DIAL (v4.9.221218) and Lipostar (v2.1.4) and a Jupyter notebook with Python code to analyse the outputs for outlier detection.</p>
Presentation of e-DSS: the e-SAFE Decision Support System
<p>e-DSS (e-SAFE Decision Support System) is a simple and intuitive software tool conceived to support professionals during the preliminary co-design process of an e-SAFE building renovation. The users can assess the energy performance of the building in its current state. Then, they are guided in the creation of a renovation project with the e-SAFE solutions (e-PANEL, e-EXOS, e-CLT, e-THERM): the tool facilitates the choice of the most suitable solution in terms of energy and seismic improvement, while also calculating energy savings, environmental benefit, and payback time. The tool is intended for all types of non-historical buildings and is available at <a href="https://esafe.eng.it/">https://esafe.eng.it/</a>.</p> <p>e-DSS has been created in the framework of Work Package 4 in the H2020 "e-SAFE" project. A final updated and refined version will be released at the end of the project. This video wants to show the tool with its different functions, including input data and results. </p> <p>For further information please contact: marilena.lazzaro@eng.it.</p>
Supporting data for "Unveiling Vertebrate Development Dynamics in Frog Xenopus laevis using Micro-CT Imaging"
<p>The dataset contains X-ray Micro Computed Tomography data of Xenopus laevis frog. There are twenty datasets of ten individual animals. Each animal was CT scanned twice – once as a native scan to visualize the hard tissues, and once contrast-stained to visualize the soft tissues. The datasets include nine developmental stages (NF44-45, NF52, NF53, NF54, NF57, NF59, NF62, NF66 and adult). There are two adults, one male and one female. The CT data (in 8bit .tiff format compressed as .tar.gz files) are supported by .stl files created from each dataset. The database also includes .stl files of selected structures of interest (body, skeleton, skull, brain and guts of individual animals).</p>
Data supporting tracking changes in wetlandscape properties of the Lake Winnipeg Watershed using Landsat inundation products (1984–2020)
<p>The workbook contains time series of wetlandscape properties, climate variables, and climate oscillation indices for 1984–2020, and land cover statistics for 1992–2020 in the Lake Winnipeg Watershed. The wetlandsacpe properties were generated as part of a study by Fendereski, Ma, Mohammady, Spence, Trick, and Creed ("Tracking changes in wetlandscape properties of the Lake Winnipeg Watershed using Landsat inundation products (1984–2020)") submitted<span> </span>to the International Journal of Applied Earth Observation and Geoinformation. The use of the data is subject to citing the paper.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.