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239 results for “Data-driven”
Research data supporting: "Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles"
<p>This repository contains the set of data shown in the paper <strong>"Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles"</strong>, published on The Journal of Physical Chemistry B (DOI:10.1021/acs.jpcb.2c08726).</p>
Data-driven plasma modelling: Fluorocarbon ICP data set
<p>This is an open source dataset of optical emission spectra and optical images in fluorocarbon plasmas, along with associated tool logs, captured from a Oxford Intruments Plasma Technology PP100 ICP etcher. The dataset consists of Ar, O<sub>2</sub>, Ar/O<sub>2</sub>, CF<sub>4</sub>/O<sub>2</sub> and SF<sub>6</sub>/O<sub>2</sub> gas mixtures etching Si wafers.</p> <p>The data has been split into chunks for uploading to Zenodo, to reconstruct them:</p> <p>$ cat generative_model-fluorocarbon_data_set.tar.xz* > generative_model-fluorocarbon_data_set.tar.xz </p> <p>$ tar -xvf generative_model-fluorocarbon_data_set.tar.xz </p> <p>You can find the code to train an autoencoder model using the optical emission spectra and optical images at our github repo, https://github.com/gregdaly/generative_modelling_for_optical_plasma_diagnostics </p>
Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access
<p><strong>Description</strong><br> <br> The datasets demonstrate the malware economy and the value chain published in our paper, <a href="https://doi.org/10.1145/3600160.3605047"><em>Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access</em></a>, at the 12th International Workshop on Cyber Crime (IWCC 2023), part of the ARES Conference, published by the International Conference Proceedings Series of the ACM ICPS.</p> <p>Using the well-documented scripts, it is straightforward to reproduce our findings. It takes an estimated 1 hour of human time and 3 hours of computing time to duplicate our key findings from MalwareInfectionSet; around one hour with VictimAccessSet; and minutes to replicate the price calculations using AccountAccessSet. See the included README.md files and Python scripts.</p> <p>We choose to represent each victim by a single JavaScript Object Notation (JSON) data file. Data sources provide sets of victim JSON data files from which we've extracted the essential information and omitted Personally Identifiable Information (PII). We collected, curated, and modelled three datasets, which we publish under the <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong>1. MalwareInfectionSet</strong><br> We discover (and, to the best of our knowledge, document scientifically for the first time) that malware networks appear to dump their data collections online. We collected these infostealer malware logs available for free. We utilise 245 malware log dumps from 2019 and 2020 originating from 14 malware networks. The dataset contains 1.8 million victim files, with a dataset size of 15 GB.</p> <p><strong>2. VictimAccessSet</strong><br> We demonstrate how Infostealer malware networks sell access to infected victims. Genesis Market focuses on user-friendliness and continuous supply of compromised data. Marketplace listings include everything necessary to gain access to the victim's online accounts, including passwords and usernames, but also detailed collection of information which provides a clone of the victim's browser session. Indeed, Genesis Market simplifies the import of compromised victim authentication data into a web browser session. We measure the prices on Genesis Market and how compromised device prices are determined. We crawled the website between April 2019 and May 2022, collecting the web pages offering the resources for sale. The dataset contains 0.5 million victim files, with a dataset size of 3.5 GB.</p> <p><strong>3. AccountAccessSet</strong><br> The Database marketplace operates inside the anonymous Tor network. Vendors offer their goods for sale, and customers can purchase them with Bitcoins. The marketplace sells online accounts, such as PayPal and Spotify, as well as private datasets, such as driver's licence photographs and tax forms. We then collect data from Database Market, where vendors sell online credentials, and investigate similarly. To build our dataset, we crawled the website between November 2021 and June 2022, collecting the web pages offering the credentials for sale. The dataset contains 33,896 victim files, with a dataset size of 400 MB.</p> <p><strong>Credits Authors</strong></p> <ul> <li>Billy Bob Brumley (Tampere University, Tampere, Finland)</li> <li>Juha Nurmi (Tampere University, Tampere, Finland)</li> <li>Mikko Niemelä (Cyber Intelligence House, Singapore)</li> </ul> <p><strong>Funding</strong></p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under project numbers 804476 (SCARE) and 952622 (SPIRS).<br> <br> <strong>Alternative links to download:</strong> <a href="https://mega.nz/folder/aJwVyIYJ#9SWh-Z3-TpPfjHZeFxbeew">AccountAccessSet</a>, <a href="https://mega.nz/folder/iUQ3RaKB#48ZkXnFYSR0qXLcbkrZLqw">MalwareInfectionSet</a>, and <a href="https://mega.nz/folder/aNYCFCrK#pbDkJL-PNWjn1ABXbtdR4w">VictimAccessSet</a>.</p>
Comparative Study of Data-driven Solar Coronal Field Models Using a Flux Emergence Simulation as a Ground-truth Data Set
<p>For a better understanding of magnetic field in the solar corona and dynamic activities such as flares and coronal mass ejections, it is crucial to measure the time-evolving coronal field and accurately estimate the magnetic energy. Recently, a new modeling technique called the data-driven coronal field model, in which the time evolution of magnetic field is driven by a sequence of photospheric magnetic and velocity field maps, has been developed and revealed the dynamics of flare-productive active regions. Here we report on the first qualitative and quantitative assessment of different data-driven models using a magnetic flux emergence simulation as a ground-truth (GT) data set. We compare the GT field with those reconstructed from the GT photospheric field by four data-driven algorithms. It is found that, at least, the flux rope structure is reproduced in all coronal field models. Quantitatively, however, the results show a certain degree of model dependence. In most cases, the magnetic energies and relative magnetic helicity are comparable to or at most twice of the GT values. The reproduced flux ropes have a sigmoidal shape (consistent with GT) of various sizes, a vertically-standing magnetic torus, or a packed structure with curled field lines. The observed discrepancies can be attributed to the highly non-force-free input photospheric field, from which the coronal field is reconstructed, and to the modeling constraints such as the treatment of background atmosphere, the bottom boundary setting, and the spatial resolution.</p>
MAOn: A Data-driven Methodology to Generate Living Ontologies
<p>This repository presents the MAnto Lite ontology created with our MAOn methodology in the context of transport and public and the accessibility it provides. Besides, a set of annotated data with the ontology as a validation method is presented. The MAOn methodology is characterized by being data-based, by creating live ontologies and by a thorough evaluation process of the created ontology.</p>
Mehrabi et al. 2020. The global divide in data-driven farming. Supplementary Information.
<p>This repository contains data, data sources, and code for reproducing the results of the paper Mehrabi et al. 2020. The global divide in data-driven farming. Nature Sustainability. https://doi.org/10.1038/s41893-020-00631-0.</p> <p>There are five directories included here, listed in alphabetical order. Directory SI_A contains the script for reproducing the analysis presented in the main text of the paper. Directories SI_B to SI_E contain the scripts for creating the input data sets used in the analysis.</p> <p>This is a baseline study, that is intended to be built upon and improved as and when new data become available. For issues and suggestions, please contact zia.mehrabi@ubc.ca.</p>
Characterization data for the manuscript "A data-driven perspective on the colours of metal-organic frameworks"
<p>Visualize the data in this dataset: <a href="https://www.c6h6.org/zenodo/record/4044212">open entry</a>. </p>
Checkbot API raw results from Libraries, Archives and Museums websites for evaluating a data-driven Search Engine Optimization methodology
<p>Results from Checkbot API to measure and collect 341 websites compatibility on multiple SEO variables (34 variables). Checkbot API indexes the website's code to find features capable of impacting SEO performance. Each website has been tested with the maximum number of links allowed to be crawled equally to 10.000 per test. In this way, we retrieved data about the overall websites performance including their sub-pages, and not only the main domain names. A scale from 0 (lowest rate) to 100 (highest rate) was adopted for each examined variable. This constitutes a useful managerial indicator of dealing with the quantification of websites performance while avoiding complex measurement systems that are difficult to be adopted by administrators. Websites tested were also categorized by the CMS type used. More information about the variables and the meaning of the results can be found at https://www.checkbot.io/ </p>
Data-Driven Identification and Analysis of Waiting Times in Business Processes: A Systematic Literature Review
<p>Supplementary Material for Systematic Literature Review titled "Data-Driven Identification and Analysis of Waiting Times in<br> Business Processes: A Systematic Literature Review"</p>
Accompanying data for the paper "Robustness of the Data-Driven Identification algorithm with incomplete input data"
<h2>Links</h2> <ul> <li>isSupplementTo <em>publication-article</em> <a href="https://doi.org/10.46298/jtcam.12590">https://doi.org/10.46298/jtcam.12590</a></li> <li>isNewVersionOf <em>dataset</em> <a href="../records/10090469">https://zenodo.org/records/10090469</a></li> </ul> <h2>Authors</h2> <ul> <li><strong>Leygue, Adrien</strong>, Ecole Centrale de Nantes, ORCID: <a href="https://orcid.org/0000-0003-0714-822X">0000-0003-0714-822X</a></li> </ul> <h2>Language</h2> <ul> <li>English</li> </ul> <h2>License</h2> <ul> <li>Creative Commons Attribution 4.0</li> </ul> <h2>Funding sources</h2> <ul> <li>This work was performed by using HPC resources of Centrale Nantes Supercomputing Center on the cluster Liger, granted and identified D1705030 by the High Performance Computing Institute(ICI).</li> </ul> <h2>Data structure and information</h2> <p>Synthetic data used in the case study (section 3) of the paper.</p> <p>The data in XDMF (Milou.xdmf ) + hdf5 (Milou.hdf5) format comprises:</p> <ol> <li>The 2D computational mesh with triangular linear elements</li> <li>The nodal Forces for all loading steps (nodal quantity)</li> <li>The displacement for all loading steps (nodal quantity)</li> <li>Cauchy stress fields for all loading steps (cell quantity)</li> </ol>
Supplementary Table for Earth observation data-driven cropland soil monitoring: A review
<p>Table including 46 manuscripts written in English referring to topsoil monitoring related to Earth observation data-driven cropland soil monitoring: A review paper.</p>
Supporting data for: "Data-driven discovery of cardiolipin-selective small molecules by computational active learning"
<p>This repository contains supporting data and code for the paper titled "Data-driven discovery of cardiolipin-selective small molecules by computational active learning" by Bernadette Mohr, Kirill Shmilovich, Isabel Kleinwächter, Dirk Schneider, Andrew L.Ferguson, and Tristan Bereau.</p>
Data from: Data-driven bioregionalization: A seascape-scale study of macrobenthic communities in the Eurasian Arctic
<p><b>Aim: </b>We conduct the first model-based assessment of the biogeographical subdivision of Eurasian Arctic seas to (1) delineate spatial distribution and boundaries of macrobenthic communities on a seascape level; (2) assess the significance of environmental drivers of macrobenthic community structures; (3) compare our modelling results to historical biogeographical classifications; and (4) couple the model to climate-change scenarios of environmental changes to project potential shifts in the distribution and composition of macrobenthic communities by 2100.</p> <p><b>Location: </b>Eurasian Arctic seas, in particular Barents, Kara, and Laptev Seas</p> <p><b>Taxon: </b>Macrobenthic fauna</p> <p><b>Methods: </b>We employed the Region of Common Profile (RCP) approach to assess the regionalization patterns of Eurasian Arctic seafloor communities.</p> <p><b>Results: </b>Four RCPs were identified based on the spatial distribution patterns of 169 macrobenthic species and a set of environmental factors, such as sediment composition, sea-ice concentration, depth of the euphotic zone, particulate organic carbon concentration at the ocean surface, as well as near-bottom water temperature and salinity. The identified regions are in strong agreement with previous classifications of macrobenthic communities. The projections are driven by climate-change scenario "Representative Concentration Pathway 6.0" suggested a general eastward shift of the RCPs over the 21st century, correlated to retreating sea-ice and increasing sea-bottom temperature.</p> <p><b>Main conclusions:</b> The RCP approach allowed us to identify seascape-scale distribution patterns of macrobenthic communities in Eurasian Arctic seas by simultaneously considering biotic and environmental data within one modelling step. This technique can represent biota and ecoregions in a probabilistic form together with assessment of uncertainties of the predictions, and assess the significance of a broad selection of environmental drivers. This first quantitative assessment of potential climate-driven changes in macrobenthic biodiversity will promote their inclusion in conservation measures.</p>
On the estimation of landslide intensity, hazard and density via data-driven models.
<p>The geographic prediction of landslide occurrence is undertaken by assessing whether a slope may be stable or unstable. In other words, current practices treat slopes where a single landslide occurred in the same way as slopes where many landslides occurred. At the slope scale, this procedure inevitably underestimates the effect of multiple landslides.<br> Here we model the number of landslides per slope instead. Then, thanks to the close relation that the number of failures shows with respect to landslide size, we convert the estimated number of landslides into estimated landslide areas. Ultimately, we also estimate the expected proportion of a slope affected by landslides. This framework is more informative than the stable/unstable paradigm and may help landslide risk mitigation strategies.</p>
Integrated data-driven reannotation of the Kluyveromyces marxianus genome reveals an expanded protein coding repertoire
<p>Supplementary data for Fenton et al. 2022. </p> <table> <tbody> <tr> <td>Supplementary Table</td> <td>ID</td> <td>Table Description</td> </tr> <tr> <td>supplementary table 1</td> <td>S1</td> <td>Transcript Start Site (TSS) metrics</td> </tr> <tr> <td>supplementary table 2</td> <td>S2</td> <td>Polyadenylation Site (PAS) metrics</td> </tr> <tr> <td>supplementary table 3</td> <td>S3</td> <td>NTE candidates </td> </tr> <tr> <td>supplementary table 4</td> <td>S4</td> <td>MTS candidates</td> </tr> <tr> <td>supplementary table 5</td> <td>S5</td> <td>iORFs candidates</td> </tr> <tr> <td>supplementary table 6</td> <td>S6</td> <td>uORFs candidates</td> </tr> <tr> <td>supplementary table 7</td> <td>S7</td> <td>ouORFs candidates</td> </tr> <tr> <td>supplementary table 8</td> <td>S8</td> <td>aORFs candidates</td> </tr> <tr> <td>supplementary table 9</td> <td>S9</td> <td>tRNA copy numbers</td> </tr> <tr> <td>supplementary table 10</td> <td>S10</td> <td>novel gene periodicity scores</td> </tr> <tr> <td>supplementary table 11</td> <td>S11</td> <td>description of novel genes</td> </tr> <tr> <td>supplementary table 12</td> <td>S12</td> <td>comparison of published genomes</td> </tr> <tr> <td>supplementary table 13</td> <td>S13</td> <td>table corrections</td> </tr> <tr> <td>supplementary table 14</td> <td>S14</td> <td>start codon corrections</td> </tr> <tr> <td>supplementary table 15</td> <td>S15</td> <td>Genes with splicing (at least one intron)</td> </tr> </tbody> </table>
Code and data sets for "MS²Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates"
<p>Code used to prepare data sets, train and evaluate new MS²PIP models, evaluate MS²Rescore for immunopeptidomics, and generate figures. See README.md for more information on how to use these files and reproduce the results reported in the manuscript titled "MS²Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates".</p>
A physically interpretable data-driven surrogate model for wake steering
<p>PALM input files for the simulations performed in the study "A physically interpretable data-driven surrogate model for wake steering" by Sengers et al. (2022). </p> <p>The PALM code is available at <a href="https://palm.muk.uni-hannover.de/">https://palm.muk.uni-hannover.de</a><br> Additional information to the input files is given in the README file</p> <p>Cite this as:<br> B.A.M. Sengers (2022). Dataset: A physically interpretable data-driven surrogate model for wake steering. https://doi.org/10.5281/zenodo.6821164</p>
WaterBench-Iowa: A Large-scale Benchmark Dataset for Data-Driven Streamflow Forecasting
<p>WaterBench-Iowa is a comprehensive benchmark dataset for streamflow forecasting. It follows FAIR data principles that are prepared with a focus on convenience for utilizing in data-driven and machine learning studies and provides benchmark performance for state-of-art deep learning architectures on the dataset for comparative analysis. By aggregating the datasets of streamflow, precipitation, watershed area, slope, soil types, and evapotranspiration from federal agencies and state organizations (i.e., NASA, NOAA, USGS, and Iowa Flood Center), we provided the WaterBench for hourly streamflow forecast studies. This dataset has a high temporal and spatial resolution with rich metadata and relational information, which can be used for varieties of deep learning and machine learning research. To some extent, WaterBench makes up for the lack of a unified benchmark in earth science research. We highly encourage researchers to use the WaterBench for deep learning research in hydrology.</p>
Data and Code for "Quantifying spatio-temporal risk of Harmful Algal Blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach"
<p>This is a zipped file of all associated code and data for the submitted paper entitled "Quantifying spatio-temporal risk of Harmful Algal Blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach".</p>
Data set, Model, and Catalog for: Data-driven stellar intrinsic colors and dust reddenings for spectro-photometric data
<p>Intrinsic colors of stars are essential for the studies on both stellar physics and dust reddening. In this work, we developed an XGBoost model to predict the stellar intrinsic colors with the atmospheric parameters, Teff , log g, and [M/H], which is an improvement of the widely used blue-edge method. The dust reddening toward each line-of-sight can then be calculated by the observed colors minus the derived intrinsic colors.</p> <p>Here we provide the related data sets:</p> <ul> <li>xgb_model.pkl: the trained XGBoost model.</li> <li>use_xgb.py: a simple script showing how to use the XGBoost model to predict intrinsic colors.</li> <li>data_set.fits: this fits file contains the training and test sets, separated into four data arrays: <ul> <li>X_train: X-data (teff,logg,mh) of the training set.</li> <li>X_test: X-data (teff,logg,mh) of the test set.</li> <li>y_train: y-data (BP-RP, BP-Ks, J-Ks) of the training set.</li> <li>y_test: y-data (BP-RP, BP-Ks, J-Ks) of the test set.</li> </ul> </li> <li>IC_catalog.csv: a catalog containing a representative set of intrinsic colors at three bands ('BPRP0', 'BPK0', and 'JK0' in columns) as a function of typical Teff, logg, and [M/H] ('teff', 'logg', and 'mh' in columns).</li> </ul> <p>With the above data and model, users can apply the trained XGBoost to new sources to predict their intrinsic colors and calculate their dust reddenings. One can further control the quality of the prediction by selecting training-like sources with the training set and estimating the <a>generalization error by the test set.</a></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.