https://researchdata.se/sv/catalogue Search results sv The accurate prediction of the specific absorption rate (SAR) of superparamagnetic iron oxide nanoparticles (SPIONs) is critical for optimizing their performance in magnetic hyperthermia applications. This study presents the development of a predictive model for SAR using advanced machine learning techniques. A comprehensive dataset comprising 1,850 entries was compiled through the integration of 84 relevant scientific articles. The dataset listed 30 predictive features, including physical, chemical, and magnetic SPION properties, along with extrinsic experimental parameters commonly reported. Exploratory data analysis revealed complex nonlinear relationships among the predictive features. Twelve machine learning models were evaluated and refined using Bayesian hyperparameter optimization. The CatBoost algorithm emerged as the most effective model, achieving the lowest mean absolute error (20.92 W/g) and root mean squared error (39.41 W/g), along with a high coefficient of determination (R² = 0.98). Shapley Additive Explanation analysis identified the alternating magnetic field amplitude and frequency as the most influential factors, followed by SPION concentration and the surface area of the core nanoparticle. Conformal prediction analysis confirmed the model's reliability, providing a prediction interval of ±61.94 W/g. The model's generalization capability was validated using an independent dataset of SPIONs with varying sizes (from 7 nm to 30 nm) and dopants (Zn, Mn, Mg, and Co). The CatBoost model accurately predicted SAR values for small-sized nanoparticles (~7 nm), although predictions for medium (~15 nm) and large-sized (~30 nm) SPIONs exhibited greater variability. The study demonstrates that advanced machine learning models, such as CatBoost, can significantly contribute to the identification of nanoparticles with optimal properties for magnetic hyperthermia, thereby supporting their systematic and robust development for broader clinical use. Wed, 20 Aug 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-29835419 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-29835419 Uppsala universitet Edgar Vega Shaquib Rahman Ansari Jiaxi Zhao Yael Suarez-Lopez Alexandra Teleki Per Larsson Data set of 189 earthquakes, 221 mining induced events from the Kiruna mine and 221 blasts from the Kiruna mine, recorded by seismic stations of the Swedish National Seismic Network (SNSN) and neighbouring countries. The earthquakes are mostly located to the west of Kiruna town, along the Pärvie post-glacial fault. The data contains event information (origin time, latitude, longitude, depth magnitude), waveform data, station information and instrument response information. Mon, 01 Jan 2024 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-18159-kir-ebi23 https://researchdata.se/sv/catalogue/dataset/doi-10-18159-kir-ebi23 Uppsala universitet This dataset is associated with a paper that uses the mining sector as a case to illustrate how the Earth System Impact (ESI) score could be used to assess impacts of economic sectors, and how this, in turn, could i) represent a scientific basis for improving prioritization of mitigative action within companies, ii) provide support for decisions on where to direct public and private investments. This dataset includes data on environmental pressures, location, revenues, and primary commodity production for assets from a sample of 10 mining companies and showcases how the ESI score could be calculated and interpreted. This dataset is associated with a paper that uses the mining sector as a case to illustrate how the Earth System Impact (ESI) score could be used to assess impacts of economic sectors, and how this, in turn, could i) represent a scientific basis for improving prioritization of mitigative action within companies, ii) provide support for decisions on where to direct public and private investments. This dataset includes data on environmental pressures, location, revenues, and primary commodity production for assets from a sample of 10 mining companies and showcases how the ESI score could be calculated and interpreted. Microsoft® Excel® for Microsoft 365 MSO, Version 2304 Build 16.0.16327.20200 Sun, 01 Jan 2023 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-7910-dvn-hpydkw https://researchdata.se/sv/catalogue/dataset/doi-10-7910-dvn-hpydkw Stockholms universitet Crona, Beatrice Parlato, Giorgio Lade, Steven Fetzer, Ingo Maus, Victor A dataset containing 2375 samples of Android Process Memory String Dumps. The dataset is broadly composed of 2 classes: "Benign App" Memory Dumps and "Malicious App" Memory Dumps, respectively, split into 2 ZIP archives. The ZIP archives in total are approximately 17GB in size, however the unzipped contents are approximately 67GB. This dataset is derived from a subset of the APK files originally made freely available for research through the AndroZoo project [1]. The AndroZoo project collected millions of Android applications and scanned them with the VirusTotal online malware scanning service, thereby classifying most of the apps as either malicious or benign at the time of scanning. The process memory dumps in this dataset were generated through running the subset of APK files from the AndroZoo dataset in an Android Emulator, capturing the process memory of the individual process and subsequently extracting only the strings from the process memory dump. This was facilitated through building 2 applications: Coriander and AndroMemDumpBeta which facilitate the running of Apps on Android Emulators, and the capturing of process memory respectively. The source code for these software applications is available on Github. The individual samples are labelled with the SHA256 hash filename from the original AndroZoo labeling and the application package names extracted from within the specific APK manifest file. They also contain a time-stamp for when the memory dumping process took place for the specific file. The file extension used is ".dmp" to indicate that the files are memory dumps, however they only contain strings, and thus can be viewed in any simple text editor. A subset of the first 10000 APK files from the original AndroZoo dataset is also included within this dataset. The metadata of these APK files is present in the file "AndroZoo-First-10000" and the 2375 Android Apps that are the main subjects of our dataset are extracted from here.. Our dataset is intended to be used in furthering our research related to Machine Learning-based Triage for Android Memory Forensics. It has been made openly available in order to foster opportunities for collaboration with other researchers, to enable validation of research results as well as to enhance the body of knowledge in related areas of research. References: [1]. K. Allix, T. F. Bissyandé, J. Klein, and Y. Le Traon. AndroZoo: Collecting Millions of Android Apps for the Research Community. Mining Software Repositories (MSR) 2016 Wed, 10 May 2017 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17045-sthlmuni-4989773 https://researchdata.se/sv/catalogue/dataset/doi-10-17045-sthlmuni-4989773 Stockholms universitet Irvin Homem Panagiotis Papapetrou Data set containing features extracted from 211 DNS Tunneling packet captures. The packet capture samples are classified by the protocols tunneled within the DNS tunnel. The features are stored in json files for each packet capture. The features in each file include the IP Packet Length, the DNS Query Name Length and the DNS Query Name entropy. In this "slightly unclean" version of the feature set the DNS Query Name field values are also present, but are not actually necessary. This feature set may be used to perform machine learning techniques on DNS Tunneling traffic to discover new insights without necessarily having to reconstruct and analyze the equivalent full packet captures. Sat, 12 Nov 2016 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17045-sthlmuni-4229399 https://researchdata.se/sv/catalogue/dataset/doi-10-17045-sthlmuni-4229399 Stockholms universitet Irvin Homem Panagiotis Papapetrou