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10,119 results for “IT Support”
Analysis - Threatened North African seagrass meadows have supported green turtle populations for millennia
<p>Scripts and data to run bagplots and discriminant analysis associated with the paper:</p> <p>Threatened North African seagrass meadows have supported green turtle populations for millennia, de Kock et al.</p>
Data and supplementary materials in support of "Development and Preliminary Validation of an Open Access, Open Data and Open Outreach Indicator"
<p>Data and supplementary materials in support of "Evgenios Vlachos, Regine Ejstrup, Thea Marie Drachen, Bertil Fabricius Dorch (2023) Development and Preliminary Validation of an Open Access, Open Data and Open Outreach Indicator, Frontiers in Research Metrics and Analytics, doi: 10.3389/frma.2023.1218213" </p> <p>It includes the anonymized dataset with the OADO values for all researchers from 10 departments (2 per faculty), the R code to perform the analysis and the graphs, the Scopus search string for the background search on Open Access and metrics, and the documentation for pulling data out from Pure.</p>
Illustrations for AI for multiple long-term conditions: Research Support Facility
<p>Illustrations created by <a href="http://www.scriberia.co.uk/">Scriberia</a> as part of <a href="https://www.turing.ac.uk/research/research-projects/ai-multiple-long-term-conditions-research-support-facility">AI for multiple long-term conditions: Research Support Facility.</a> The archived website link is available <a href="https://web.archive.org/web/20250212145350/https://www.turing.ac.uk/research/research-projects/ai-for-multiple-long-term-conditions-research-support-facility?__cf_chl_rt_tk=XKaDrLiiU8hmzZk2rKaWDBVpnO8exVqC4EHijqlWTIQ-1739372030-1.0.1.1-hmHFksRkdOQcBH9PsOgRVdNAatQdr3i4hFOqtyPcyZU">here</a>.</p> <p>The AIM RSF is funded by the NIHR Artificial Intelligence for Multiple Long-Term Conditions (AIM) programme (NIHR202647).</p> <p>When using any of the images, please credit them with</p> <p><em>"This image was created by <a href="http://www.scriberia.co.uk/">Scriberia</a> for AI for multiple long-term conditions: Research Support Facility and is used under a CC-BY licence."</em><br><br>We have created Alternative texts (Alt text) for all images stored in PDF and text format.</p>
Research data supporting: "Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling"
<p>This repository contains the set of data shown in the paper "Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling" published on PNAS (DOI: 10.1073/pnas.2300565120).</p>
Supporting data for "Reliable interpretability of biology-inspired deep neural networks"
<p><strong>Contents</strong></p> <p><em>data.tgz</em> contains all data necessary for reproducing the analysis in the manuscript. After cloning the GitHub repository, extract the contents of this file into folder <em>data</em>. The archive contains the following subfolders:</p> <ul> <li><em>dtox</em><br> DTox results, one subfolder per seed <ul> <li><em>module_relevance.tsv</em>: contains node importance scores, with the following columns: <ul> <li>(first, unnamed): compound identifier</li> <li>remaining columns: node identifiers (UniProt and Reactome IDs)</li> </ul> </li> <li><em>test_labels.csv</em>: predictions for the test set, with two columns: <ul> <li>truth: true label (0 or 1)</li> <li>predicted: predicted label (decimal number between 0 and 1)<br> </li> </ul> </li> </ul> </li> <li><em>mskimpact_[cancer type]_[experiment]</em><br> P-NET results using the MSK-IMPACT 2017 dataset, one subfolder per seed<br> [cancer type] is one of bc (breast cancer), cc (colorectal cancer), nsclc (non-small cell lung cancer), or pc (prostate cancer)<br> [experiment] is one of original (original setup) and shuffled (shuffled labels)<br> </li> <li><em>pnet_[experiment]</em><br> P-NET results using the original (prostate cancer) dataset, one subfolder per seed<br> [experiment] is one of deterministic (deterministic input data), original (original setup), and shuffled (shuffled labels) <ul> <li><em>node_importance.csv</em>: contains node importance scores, with the following columns: <ul> <li>(first, unnamed): node name</li> <li>coef: original node importance scores</li> <li>coef_graph: indegree plus outdegree of node</li> <li>coef_combined: adjusted node importance score (= coef / coef_graph if coef_graph > mean(coef_graph) + 5 sd(coef_graph) in the respective layer)</li> <li>coef_combined_zscore: scaled coef_combined</li> <li>coef_combined2: z(z(coef_graph) - z(coef))</li> <li>layer: layer of the node</li> </ul> </li> <li><em>predictions_test.csv</em>: predictions for the test set, with the following columns: <ul> <li>(first, unnamed): sample name</li> <li>pred: predicted class (unfortunately, encoded by a double 1.0 or 0.0)</li> <li>pred_scores: probability of the predicted class</li> <li>y: true class (encoded as integer 1 or 0)</li> </ul> </li> <li><em>predictions_train.csv</em>: predictions for the training set (same columns as above)</li> <li><em>link_weights_[layer].csv</em>: only in subfolder 234_20080808; matrices with edge weights</li> </ul> </li> </ul> <p> </p> <p><strong>Changelog</strong></p> <p><em>v1.1.0 – 2023-06-28</em></p> <ul> <li>added DTox results</li> <li>added results of P-NET experiments with MSK-IMPACT 2017 dataset</li> </ul> <p><em>v1.0.0 – 2023-03-22</em></p> <ul> <li>initial release</li> </ul>
Supporting material of "A nonequilibrium alchemical method for drug-receptor absolute binding free energy calculations: the role of restraints"
<p>Supporting material of the paper "A nonequilibrium alchemical method for drug-receptor absolute binding free energy calculations: the role of restraints".</p> <p>The directory is fully documented with README files.</p> <p>Differences of V2.0 with V1.0:</p> <ul> <li>Due to a software bug we had to re-parametrize the ligands whose torsions were parametized with ANI-2.X (ligand 6 and 7).</li> <li>During the peer reviewing process we also parametrized with ANI-2.X and docked ligand 8.</li> </ul>
Supporting data for "Detection of single ions in a nanoparticle coupled to a fiber cavity"
<p>Data for Fig. 2b, 2d, 3 and 4 of the paper "Detection of single ions in a nanoparticle coupled to a fiber cavity"</p>
Supporting data for "Linearity of the climate system response to raising and lowering West Antarctic and coastal Antarctic topography" by Andrew G. Pauling, Cecilia M. Bitz and Eric J. Steig
<p>Contains the model output and topography files necessary to reproduce the results of "Linearity of the climate system response to raising and lowering West Antarctic and coastal Antarctic topography" by Andrew G. Pauling, Cecilia M. Bitz and Eric J. Steig. Published in Journal of Climate, <a href="https://doi.org/10.1175/JCLI-D-22-0416.1">https://doi.org/10.1175/JCLI-D-22-0416.1</a>.</p> <p>Please download and extract the data from each of the tar.gz.files. A description of the directories, run names, and use of the topography files is given in the file readme.txt within the dataset.</p>
Supporting code and data for: Proportion of forest area burned at high-severity increases with increasing forest cover and connectivity in western US watersheds
<p>This is the R code and shapefiles of the coniferous HUC-12 watersheds and their western US boundary for the publication, "Proportion of forest area burned at high-severity increases with increasing forest cover and connectivity in western US watersheds" </p>
Supporting Data: Characterizing Sub-Glacial Hydrology Using Radar Simulations
<p>Raw data from "A Simulation Approach to Characterizing Sub-Glacial Hydrology". </p> <p>THW2_UBH0c_X243a.M1D1 is the 1 dimensionally focused, high gain radargram from the IPR radar survey. </p> <p>Actual along-track IPR pick data for THW2_UBH0c_X243a are contained in file X243a_rad_data_foc.csv. Metadata are as follows:</p> <ul> <li>pst: flight line name</li> <li>long: observation longitude in decimal degrees (EPSG:4326)</li> <li>lat: observation latitude in decimal degrees (EPSG:4326)</li> <li>bed: bed pick elevation in [m]</li> <li>year: timestamp year</li> <li>day: timestamp day</li> <li>t_ms: timestamp miliseconds</li> <li>srf: surface pick elevation in [m]</li> <li>echo: bed echo strength [dB]</li> <li>thk: ice thickness [m]</li> <li>Lice: estimated 2-way attenuation loss [dB]</li> <li>R_bed: bed absolute reflectivity [dB]</li> <li>L_unc: 2-way attenuation loss uncertainty [dB]</li> </ul> <p>Simulated data: A folder for each simulation contains the files listed below.</p> <ul> <li>Inputs: <ul> <li>[INPUT] Trajectory_MRS.mat: values for simulated aircraft trajectory, including total # of rangelines (TrackLengthTot), aircraft position at each rangeline in [m] (sc_position_x, sc_position_y, sc_position_z), and aircraft velocity in [m/s] (sc_vel_x, sc_vel_y, sc_vel_z)</li> <li>bed.csv: lists all z-values in [m] for the bed surface</li> <li>chan.csv: lists all z-values in [m] for the channel surface</li> <li>params.txt: lists all pertinent parameters from the simulation</li> <li>surf.csv: lists all z-values in [m] for the ice surface</li> <li>xx.csv: lists all x-values in [m] for the ice surface</li> <li>xx_b.csv: lists all x-values in [m] for the bed and channel surface</li> <li>yy.csv: lists all y-values in [m] for the ice surface</li> <li>yy_b.csv: lists all y-values in [m] for the bed and channel surface</li> </ul> </li> <li>Radargrams: <ul> <li>RgramFocPWR_RM3_hamm.mat: Focused radargram power [dB]</li> <li>RgramRawPWR.mat: Raw radargram power [dB]</li> <li>RgramRCPWR.mat: Range-compressed radargram power [dB]<br> </li> </ul> </li> </ul>
Experimental data: Single- and double-wythe brick masonry walls subjected to four-point bending tests under different support conditions: Simply supported, rigid, non-rigid
<p>This dataset contains the results of laboratory quasi-static monotonic four-point bending tests conducted at RISE Research Institutes of Sweden on eleven natural-scale unreinforced brick masonry walls. The walls were spanning vertically between two reinforced concrete slabs and were tested under three different support conditions defined according to the American manual UFC 3-340-02: simply supported, rigid, non-rigid. The influence of these support conditions on the out-of-plane behavior of the walls was studied on elements with varying thickness – single and double wythe – and subjected to different levels of axial compression (or overload). The walls were tested inside of a bi-axial test setup that allowed not only the lateral, out-of-plane force but also the axial, arching action to be measured throughout the tests. Optical full-field displacement measurements were also acquired by two systems of cameras making use of the 2D and 3D Digital Image Correlation (DIC) technique.</p> <p>The data generated from these tests are made here available to support further investigations on masonry structures subjected to extreme lateral, out-of-plane actions. The dataset includes 3 compressed folders, ordered from 01 to 03, along with an auxiliary document describing the content and organization of the dataset. </p> <p>The data presented here are described in the following research article:</p> <blockquote> <p><a href="https://www.sciencedirect.com/science/article/pii/S0950061823022602?via%3Dihub">Godio M, Flansbjer M, Williams Portal N (2023). Single- and double-wythe brick masonry walls subjected to four-point bending tests under different support conditions: simply supported, rigid, non-rigid, Construction and Building Materials</a></p> </blockquote> <p>To cite this dataset, please refer to the article.</p> <p>The Authors</p>
University Helpdesk Support Data Set
<p>This repository contains the data set presented in the publication "Identifying NFRs conflicts using quality ontologies" by Al Balushi et al. [1]. The data set has been extracted from the manuscript for easier reuse.</p> <p>[1] Al Balushi, T., Khod, O., Sampaio, P. R. F., Patel, M., Manchester, B. S. W., Corcho, O., & Loucopoulos, P. (2008). Identifying NFRs conflicts using quality ontologies. <em>SEKE 2008</em>, 929.</p>
Supporting Dataset for "Reducing a tropical cyclone weak-intensity bias in a global numerical weather prediction system"
<p>This archive supports the submission of "Reducing a tropical cyclone weak intensity bias in a global numerical weather prediction system" to Monthly Weather Review. It contains model configurations, the software used to create ensemble perturbations, the software used to compute the diagnostics discussed in the text, and the software use to plot figures.</p> <p>After downloading, the contents can be extracted using:</p> <blockquote> <p>tar -xzf idealtc1_archive-1.tgz</p> </blockquote>
Dataset supporting the paper "Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities"
<p>Datasets involved in the construction and benchmarking of the CollecTRI-derived regulons as presented in the paper "Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities".</p>
Supporting Information from The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)
<p>Supporting Information (Open Data) from The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)</p>
Supplementary Data: Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS
<p>Supplementary data for the SCINPAS analysis as part of the manuscript "Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS".</p> <p>It includes the output of SCINPAS, available here: <a href="https://github.com/zavolanlab/SCINPAS">https://github.com/zavolanlab/SCINPAS</a>, used for the analyses. Additionally, the scripts and data for the comparison with other tools is provided. Please read the README.md for more information.</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part I: Pre-processing)
<p>This is Part I of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files to generate the simulation grids.</p>
Supporting Information for "Solvent modulated specific ion effects: PNIPAM brushes in non-aqueous electrolytes"
<p>This deposition contains the data and analysis (Jupyter notebooks) detailed in <em>Solvent modulated specific ion effects: PNIPAM brushes in non-aqueous electrolytes</em>. All Jupyter notebooks have also been converted into PDF files for ease of viewing.</p> <p> </p> <p>All data and code (notebooks) required to reproduce the analysis can be found within the “supporting_data_analysis.zip” archive. This archive contains three sub-directories:</p> <ul> <li>Ellipsometry <ul> <li>Data directories containing all raw ellipsometry data for the ‘thick’ and ‘thin’ PNIPAM brushes.</li> <li>Spatial maps of the polymer brush used for spectroscopic ellipsometry data analysis: “surface_map.png”.</li> <li>“refellips_Spectroscopic_SL.ipynb” notebook to reproduce the analysis of the hydrated (solid-liquid) ‘thin’ polymer brush. Relevant plotting tools can be found in the <a href="https://github.com/refnx/refellips">refellips repo</a>.</li> <li>“Ellipsometry_logistical_fitting.ipynb” notebook and “water_results.csv” file for the demonstration of the extraction of a thermotransition temperature from an ellipsometry dataset. </li> <li>“refellips_Spectroscopic_SL_VFP.ipynb” notebook to reproduce the analysis of the hydrated (solid-liquid) ‘thick’ polymer brush.</li> </ul> </li> <li>Neutron_reflectometry <ul> <li>Data directory containing all relevant reduced reflectivity profiles from the Platypus reflectometer at ANSTO.</li> <li>“refnx_dry.ipynb” and “refnx_solvent.ipynb” notebooks required to reproduce the analysis pertaining to a dry polymer brush and a solvated brush, respectively.</li> <li>Additional code required to model the hydrated polymer brush and various plotting tools can be in the <a href="https://github.com/igresh/refnxtoolbox">refnxtoolbox repo</a>.</li> </ul> </li> </ul>
mapspamc_db: a database with global spatial datasets to support the implementation of the mapspamc R package.
<p>This repository contains the mapspamc database (mapspamc_db), a collection of global spatial datasets to support the implementation of the <a href="https://github.com/michielvandijk/mapspamc">mapspamc</a> R package. The database also includes subnational crop statistics and matching country shapefiles for several country examples. For more information on how to use the mapspamc package in combination with mapspamc_db, see the <a href="https://michielvandijk.github.io/mapspamc/">mapspamc documentation</a>. Detailed information on the contents of mapspam_db, such as the sources of information and pre-processing is described in the mapspamc_db documentation (pdf file) that is part of the repository.</p>
Securing Your Crypto-API Usage Through Tool Support - A Usability Study
<p>Developing secure software is essential for protecting passwords and other sensitive data. Despite the abundance of cryptographic libraries available to developers, prior work has shown that developers often unknowingly misuse the provided Application Programming Interfaces (APIs), resulting in serious security vulnerabilities. Eclipse CogniCrypt is an IDE plugin that aims at helping developers use cryptographic APIs more easily and securely by providing three main functionalities: (1) it provides a use-case oriented view of cryptographic APIs and guides the developer through their configuration, (2) it generates the code needed to accomplish the chosen use case based on the selected choices, and (3) it continuously analyzes the developer’s code to ensure that no API misuses are introduced later. However, so far the effectiveness of CogniCrypt was never empirically evaluated. In this work, we fill this gap through a controlled experiment with 24 Java developers. We evaluate the tool’s effectiveness in reducing API misuses and saving developer time. The results show that CogniCrypt significantly improves code security and also speeds up development for cryptograph-related tasks. The feedback received during the study suggests that developers particularly appreciate CogniCrypt’s code generation. Its static-analysis is valued for keeping the code up-to-date. Yet, the further integration of generated code into a developer’s project still presents a major challenge. Nonetheless, our results show that CogniCrypt effectively helps application developers produce more secure code.</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.