https://researchdata.se/sv/catalogue Search results sv This is event log data of activities generated by autonomous agents in the computer network "ADS-24", implemented in FOI's cyber range Crate. The main goal of the experiment was to measure the performance of automated defensive agents when the network is attacked for a set period of time. We also tested how variations to the problem, in the form of user agents, attacker strategies and network topology, affected the returns of the agents.  Please note that ADS-24 is an entirely fictional network, and all content (such as hostnames, usernames, email contents, etc.) was constructed for this experiment. The data was collected over the course of two months by running repeated episodes, where each episode contains one attack. The two hour episodes contain different combinations of an attacker agent, various defender agents and simulated users. The data is grouped by month and episode identifiers, where the episode id is a combination of the date and hour the episode started.  Network The network we have implemented is based on a scenario description written by domain experts at fmv. The scenario primarily consists of a specification of a fictive maintenance management system, named “AIR-DELIVERY-SYSTEM24” (ADS-24). The intended functions of is to keep track of maintenance needs, purchase new spare parts and store costs and salaries. ADS-24 consists of four subnets, identified as “CLIENT”, “DMZ”, “SRV” and “SOC”, which are all connected through a shared firewall server. The 37 machines across the subnets run either Linux or Windows, with machines running Windows being the most frequent. The SOC network contains machines related to monitoring and is inaccessible to all agents to not disturb logging during experiments. The layout of is illustrated as a graph in [ads-24.png]. Alert Rules & Active Responses A Wazuh database server was run in the SOC section of the network and each host, except “flightlogs”, had a Wazuh agent service that sends events to the central database. The parser rules used by the Wazuh agents were selected based on a combination of reviews, recommendations and standards. This includes the log policy of the Swedish Armed Forces and widely used configurations such as that for Sysmon by SwiftOnSecurity. The rules cover log data from Snort for IP packets, Auditd and Syslog for Linux hosts as well as Sysmon and event logs for Windows systems. Along with Wazuh’s default rule set, 586 rules from the Sigma repository were included. The Sigma rules were selected based on their relevance to the scenario, meaning that rules in the Sigma repository related to services not part of the scenario were excluded. Every host in runs Osquery to collect host information, such as user accounts and network interfaces. Information about these elements are sent to Wazuh at regular intervals. We used the Wazuh feature “Active Response” to allow defense agents to execute a set of prepared commands on hosts in the network by calling the Wazuh REST API. Two commands were implemented: one that powers off a given machine, and one that blocks traffic between a given host and other subnets in both directions. Blocking is done by adding new firewall rule entries to the host “fw1”. The commands are parametrized with a single argument; the identifier of the Wazuh agent service that should execute the command. Network Activity Regular activity in ADS-24 is composed of periodic updates to the log database from external IP addresses, computer-to-computer communication within the Windows domain, and actions performed by simulated users. The simulated users follow predefined schedules in which they exchange emails, access internal web interfaces, use remote desktop applications, and open files of various types. Occasionally, a simulated system administrator connects to machines and executes commands using Remote Desktop, PsExec, or wmi. We also generate threat actor activity using the red-team automation tool Lore. The initial entry point machine of Lore was always the host “flightlogs”. As mentioned, “flightlogs” does not run a Wazuh agent, making it functionally invisible to and untouchable by defender agents. Lore can therefore never be fully expelled from the network. Lore attempts to compromise systems by selecting different actions from a pool of available options, according to its configuration, which may or may not succeed. Two configurations for Lore were used, which we call “Guided” and “Exploratory”. With the guided configuration, Lore is configured with blacklists to ignore machines that are not along the fastest path between the entry point and the designated crown jewels in the DMZ segment. With the exploratory configuration, Lore may prioritize attacking machines not taking it closer to the DMZ, but is also more flexible and less predictable. Lore records its activity in a log stored in the control plane. The log, and thus the “true” attacker state, is therefore not accessible from the event plane where the defender agent receives data from. MAL Modeling We assume the defender agent make decisions based on the MAL data model of the network. To model the components of ADS-24, we created a smaller version of the MAL language CoreLang , titled CadsLang. The language models two attack vectors leading to access to a host’s data: one through using a software vulnerability, and one where access is gained through brute-forcing credentials. The language contains two defense steps, “Application.NotPresent” and “ConnectionRule.Restricted”, to correspond with the two commands implemented in Wazuh. Functionally, “NotPresent” blocks both attack vectors for a host, making it impossible to access its data for an attacker. The “Restricted” step blocks the “ConnectionRule” attack steps representing access to the host from a different network, but still allows an attacker to access the host through internal subnet connections. Each MAL attack step was associated with a set of Wazuh rule identifiers, a set of Wazuh rule groups and a set of rule that should be ignored. Since attack steps are associated with assets, the event being mapped needs to contain an identifier that was also encountered in the instance model creation procedure, such as an IP address, username or host identifier. The mapping is done by the Wazuh/MAL Interface.We tested the alert mappings by collecting multiple two hour periods of data from the network with and without user agents, and without any attacker or defender agents. Under the assumption that no adversarial actions are taken in the network during this time, we treat all observed attack steps as false positives. This yielded an average false positive probability per time step for the alert mapping at 1.7% per attack step without users and 3.3% with users. Data Collection Method Blue agent interaction and logging was done by the Wazuh/MAL Interface, which also handled experiment scheduling. The Wazuh/MAL interface was run on a laptop running virtual machines, with VPN access to CRATE. A diagram showing the machine setup we used is shown in [experiment_setup.png].  Vejde agents trained with reinforcement learning were trained using the MAL Simulator, using the Vejde/MAL Simulator interface. The Wazuh logs and Lore JSON files were produced in CRATE, with FOI tooling. Agents using policies optimized with RL were trained using the MAL Simulator, with the Vejde library for agent architectures. Training was done using 2 million transitions, sampled from a combination of simulation environments using different attacker policies, attacker entry-points and network topology variations for each episode. Experiment Procedure We ran experiments in an episodic fashion, with each episode lasting two hours. The Crate snapshot functionality was used to start each episode from the same system state. To allow the system to settle after being restored, episodes were started half an hour after the network was restored. We evaluated the following agents: “Vejde”, a policy trained in the MAL Simulator with reinforcement learning; “Vejde w/ Noise”, same as “Vejde”, but trained with a 1% false positive and false negative rate per attack step; “Heuristic”, a policy that selects an associated defense step of an asset if an associated attack step is observed; “NoOp”, a policy that does nothing. All agents use the same MAL data model for its input. Each episode used a single defender agent, sampled without replacement from the set of available agents.  During episodes, Wazuh was queried for new events at a fixed 30-second interval, and if any returned events were matched with attack step, an instance of the step was appended to the observation database. To select an action for the time step, the current database was fed to the defender agent, producing a single action in accordance with its policy. When there was no recorded change to the database between two time steps, no action was requested from the current agent. If the agent selected an action other than waiting, the mal defense step was added to the observation database, mapped to a corresponding active response, and sent to the Wazuh server through the REST API. As in the simulator, assets with defense steps were removed from the observation along with any associations to other assets or attack steps it was involved in. If an asset that had been removed appeared in an alert at a later time, it was temporarily reintroduced to the model for a single step. The command used to run the experiments with the Vejde/MAL Simulator interface was: uv run scripts/run_on_repeat.py \--config-file configs/tired_hope.yml \--start-time "00:30" \--run_length_minutes 120 \--period_minutes 180 \--polling_time_seconds 30 \--number-runs 8 \--agent_selection.method "shuffle" \ Episode Variations Experiments were run with a set of variable factors, which were selected at random before the start of each episode. Simulated Users To test the defender agent’s robustness to noise, we ran episodes with and without the simulated user agents. Each host in the client section of is assigned a simulated user agent, which will perform actions based on a given policy. Attacker Strategy To test how the defender agent handles different attack profiles, we used the two Lore configurations described above. This determines which machines Lore prioritizes, and what methods it uses. Network Topology To test how the agents handles variations to the network topology, we randomly remove hosts, selected from “rootca”, “timereporter” and “print”, from before the episode. These machines are not part of the list of machines Lore is directed at with the “Guided” policy. Data Description What follows are descriptions of what the data contains. The file [utils.py] includes a number of functions to faciliate easier manipulation of the data. Also included are [cia_values.json] that lists the Confidentiality, Integrirty and Availability prioritites for each host as well as [to_agent_ip_mapping.json] and [agent_to_subnet.json] that lists which network segments hosts belong to. Defender Agents These are the identifiers for different defender agents that appear in episodes: WhackAMoleAgent: Heuristic agent that enables defenses on assets with observed attack steps. NOPAgent: Defender agent that does nothing. nebulous-snail-417: Vejde agent trained with no false positives. bustling-doe-36: Vejde agent trained with false positives. Episode Folder Contents Episodes were generated between 00:00 and 21:00 each day in three hour intervals. The 00:00 episode always uses the "do nothing" defender, meaning that Lore can run uninterrrupted. Each episode folder contains a number of files from Wazuh, Lore and the ADS Monitor. The main files we have used for analysis are: wazuh.json: Exported Wazuh data, in the OpenSearch document format. summary.json: Episode summary from Crate. Contains info about starting times, shutdown machines and lore configurations used for the episode. blue_agent_recording.json: Blue agent observations and actions from the Monitor, expressed in CadsLang attack and defense steps. lore.json: Full Lore action log.     The following files are included for debugging purposes: lore_knowledge.json: Lore knowledge log. Contains assets Lore discovered. sved_log_*: Simulated user agent action log. Contains actions performed by the simulated users, if they were present. lore_standard.json: Shortened Lore action log. episode.log. Log file for experiment setup from Crate. Summary File The file summary.json is a processed summary of all the episodes, combining fields from the files listed above, with some derived data like the blue agent return. The following list attemps to briefly describes each field: episode_id: Episode identifier composed of the date and Crate start time. schedule: Times crate was reset and Lore _should_ start. time_start: Time lore actually started. This was set to half an hour after the Crate reset for all episodes. time_end: Time lore ended. lore_scenario_id: lore scenario id. 1-4. Combination of entrypoint and priority. lore_scenario_instance_id: Not relevant. bot_game_id: Not relevant. user_agents: Whether simulated users were active or not in the episode. remaining_goals: Goals that Lore has been given but not achieved in the episode. These are hosts that Lore attempts to reach. all_goals_met: Whether Lore obtained all goals. goals_remaining: Names of remaining goals. points: Points Lore received according to its own scoring. blue_agent: What blue agent, out of those listed above, that was used for the episode. blue_agent_start_time: When the blue agent was started. This was set to half an hour after the Crate reset for all episodes. blue_agent_return: Score of the blue agent, as measured by the Monitor interface. Note that this will count false positives as real alerts, and is therefor not a reliable metric. lore_entrypoint: Machine Lore used as entry. Lore will always start from flightlogs, but will prioritize the machine listed here. lore_priority: Lore policy. malicious_users: Whether users turn on machines or not. This can be considered a bug, so episodes where this is true should be treated  as such. block_blinds_blue: Whether the block IP actions also blocks machines from sending alerts. This can be considered a bug, so episodes where this is true should be treated  as such. shutdown_machines: Machines that were turned off for the episode. blue_agent_attack_penalty: Blue agent penalty for being attacked. This is calculated as the sum of C + I priorities for each compromised host per minute they are compromised.  A machine is considered as compromised when Lore has run an elevated shell command successfully. blue_agent_defense_penalty: blue agent return only for defending. The penalty is the A priority for an asset. This is also calculated per minute. Shutting down a host incurs the full penality, blocking IP traffic half. blue_agent_score: Blue agent return. Sum of attack and defense penality. blue_agent_compromised_hosts: machines lore gained an elevated shell on. blue_agent_defended_hosts: assets that had defenses ran on them. blue_agent_episode_length: how many steps the blue agent took. Dependent on the polling rate of the monitor. The wall time of an episode is always two hours. Wed, 27 May 2026 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/oai-datarepository-kth-se-4mh6z-zw065 https://researchdata.se/sv/catalogue/dataset/oai-datarepository-kth-se-4mh6z-zw065 Kungliga Tekniska högskolan Nyberg, Jakob Sommestad, Teodor Ekstedt, Mathias Johnson, Pontus This record contains machine-learned interatomic potential (MLIP) and cluster expansion (CE) models for the AuCuPd:CO,H system along with reference data from density functional theory (DFT) calculations. Technical details are provided in Pernilla Ekborg-Tanner and Paul ErhartCompeting adsorption of H and CO on Pd-alloy surfaces: Mechanistic insight into the mitigating effect of Cu on CO poisoningdoi: https://doi.org/10.48550/arXiv.2603.00776 MLIP models Models based on the neuroevolution potential (NEP) architecture are provided in the nep-*.txt files. The "full" model has been trained against all available data whereas the "split" models have been trained against a random selection of 90% structures from the available structures. Models based on the MACE architecture are provided in the MACE-*.model files. CE models Models are provided for (111), (110), and (100) surfaces in the *.ce files along with reference structures in the *.xyz files Reference data Reference data from DFT calculations is provided in the reference-structures.db file, which is a sqlite database generated using the ase package. Sat, 28 Feb 2026 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-17670909 https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-17670909 Chalmers tekniska högskola Ekborg-Tanner, Pernilla Erhart, Paul This record contains models based on the neuroevolution potential (NEP) approach, charge unaware (conventional) NEP models (nep-*.txt) and charge-aware qNEP models (qnep-*.txt). It also contains the respective reference datasets used for training and validation of these models (references-*.txt). When using any of these models make sure to cite both the original publication for these models as well as, where applicable, the source publications for the reference data (see below). Sources of reference datasets Water models The reference data for the energies, forces, and virials are from Ke Xu, Yongchao Hao, Ting Liang, Penghua Ying, Jianbin Xu, Jianyang Wu, and Zheyong FanThe Journal of Chemical Physics 158, 204114 (2023)Accurate Prediction of Heat Conductivity of Water by a Neuroevolution Potentialdoi: 10.1063/5.0147039 The reference data for the Born effective charges are from Zheyong Fan, Benrui Tang, Esmée Berger, Ethan Berger, Erik Fransson, Ke Xu, Zihan Yan, Zhoulin Liu, Zichen Song, Haikuan Dong, Shunda Chen, Ziliang Wang, Lei Li, Yizhou Zhu, Julia Wiktor, and Paul ErhartJournal of Chemical Theory and Computation 22, 4787 (2026)qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulationsdoi: 10.1021/acs.jctc.6c00146 The original structures were generated in Linfeng Zhang, Han Wang, Roberto Car, and Weinan EPhysical Review Letters 126, 236001 (2021)Phase Diagram of a Deep Potential Water Modeldoi: 10.1103/PhysRevLett.126.236001 Li7La3Zr2O12 garnet models The reference data are from Zihan Yan and Yizhou ZhuChemistry of Materials 36, 11551 (2024)Impact of lithium nonstoichiometry on ionic diffusion in tetragonal garnet-type Li7La3Zr2O12doi: 10.1021/acs.chemmater.4c02454 BaTiO3 models The reference data are from Zheyong Fan, Benrui Tang, Esmée Berger, Ethan Berger, Erik Fransson, Ke Xu, Zihan Yan, Zhoulin Liu, Zichen Song, Haikuan Dong, Shunda Chen, Ziliang Wang, Lei Li, Yizhou Zhu, Julia Wiktor, and Paul ErhartJournal of Chemical Theory and Computation 22, 4787 (2026)qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulationsdoi: 10.1021/acs.jctc.6c00146 MgOH models The reference data are from Z. Liu, J. Sha, G.-L. Song, Z. Wang, and Y. ZhangChemical Engineering Journal 516, 163578 (2025)Understanding magnesium dissolution through machine learning molecular dynamicsdoi: 10.1016/j.cej.2025.163578 Thu, 22 Jan 2026 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-18335947 https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-18335947 Chalmers tekniska högskola Fan, Zheyong Tang, Benrui Berger, Esmée Berger, Ethan Fransson, Erik Xu, Ke Yang, Zihang Liu, Zhoulin Song, Zichen Dong, Haikuan Chen, Shunda Li, Lei Wang, Ziliang Zhu, Yizhou Wiktor, Julia Erhart, Paul 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 This database was created by a joint effort from the Realistic 3D research group at Mid Sweden University (Sundsvall, Sweden) and the School of Telecommunications Engineering in Xidian University (Xi'an, China). The database details are explained thoroughly in the publication, which is accepted (and to appear in the proceedings of) the 27th IEEE International Workshop on Multimedia Signal Processing (MMSP) in 2025.  You can use this database in your work under the Creative Commons Attribution 4.0 International (CC-BY 4.0) licence, provided that you cite the database as below: Zerman, E., Takhtardeshir, S., Trioux, A., Qin, J., Wu, W., Olsson, R., & Sjöström, M. (2025). Subjective Visual Quality Assessment of Compressed Light Field Images: Learning-based vs. Conventional Methods. The 27th IEEE International Workshop on Multimedia Signal Processing (MMSP).DOI: (To be updated after publication) BibTeX: @inproceedings{zerman2025subjective  title        = {Subjective Visual Quality Assessment of Compressed Light Field Images: Learning-based vs. Conventional Methods},  author       = {Zerman, Emin and Takhtardeshir, Soheib and Trioux, Anthony and Qin, Jianlong and Wu, Wenjie and Olsson, Roger and Sj{\"o}str{\"o}m, M{\aa}rten},  booktitle    = {The 27th IEEE International Workshop on Multimedia Signal Processing (MMSP)},  year         = {2025},  organization = {IEEE}} This database contains 85 light field stimuli, rendered as pseudo-video sequences with a spiral trajectory, and the subjective quality scores collected by 40 people in two different countries (19 in Mid Sweden University, Sweden; and 21 in Xidian University, China). The 85 LF stimuli were generated from 5 source LFs using 4 different LF compression methods, comprising two conventional methods (H.265/HEVC and JPEG Pleno) and two learning-based methods (RLVC and EF-VAE). Fri, 08 Aug 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-16778671 https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-16778671 Mittuniversitetet Zerman, Emin Takhtardeshir, Soheib TRIOUX, Anthony Olsson, Roger Sjöström, Mårten The work has been published as Parajuli et al. (2025) Diabetologia (https://doi.org/10.1007/s00125-025-06502-7) Frequent self-sampling of blood has the potential to identify early, disease-predictive markers, including proteins. In a study to test this hypothesis, we conducted regular microsampling of a mouse model over 14 days and monitored their molecular response to a type 1 diabetes (T1D)-associated virus. This longitudinal approach involved the collection of dried blood samples, which were subsequently analysed for 92 circulating proteins. The data revealed transient molecular changes in the virus-infected mice. Utilising machine learning techniques, we achieved a prediction accuracy of over 90% for infection status after day 2 post-infection. This high level of accuracy enabled timely treatment interventions with immune serum, which could potentially prevent the onset of diabetes in the infected animals. The data of this study underscores the utility of frequent blood microsampling as a method for monitoring disease progression during the pre-symptomatic phase, allowing for prompt medical interventions of immune-mediated inflammatory diseases, including T1D. Description of data files:- Mouse DBS_Batch 1_ProtPQN data_rmd1021.csv: ProtPQN normalised NPX values for Study batch 1. The signals are in log2-scale. All columns not described below contain protein measurements. - Mouse DBS_Batch 2_ProtPQN data.csv: ProtPQN normalised NPX values for Study batch 2. The signals are in log2-scale. All columns not described below contain protein measurements. - zscore_data_combined.csv: Z-score transformed measurements for both studies. All columns not described below contain protein measurements. - sample information study batch 1 and 2.csv: Sample information for all of the mice. - Mouse DBS_NPX_below LOD.xlsx; Raw data from Olink Signature NPX software for Study batch 1. Values are reported as NPX values and are in log2-scale. This file contains values for limit of detection (LOD) per protein. - Mouse DBS Plate 2_NPX_belowLOD.xlsx: Raw data from Olink Signature NPX software for Study batch 2. Values are reported as NPX values and are in log2-scale. This file contains values for limit of detection (LOD) per protein Thu, 31 Jul 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-27368322 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-27368322 Karolinska Institutet Anirudra Parajuli Annika Bendes Fabian Byvald Virginia M. Stone Emma Ringqvist Marta Butrym Emmanouil Angelis Sophie Kipper Stefan Bauer Niclas Roxhed Jochen Schwenk Malin Flodström Tullberg This record accompanies the publication "Predicting neutron experiments from first principles: A workflow powered by machine learning". It comprises the machine-learned interatomic potentials (MLIPs) constructed and employed in that work with their respective training data as well as the experimental inelastic neutron scattering data for crystalline benzene presented in the publication. Hydrogenated Sc-doped BaTiO3 nep-BaScTiOH.txt – MLIP based on the neuroevolution potential (NEP) form nep-BaScTiOH.zip – model ensemble with the underlying training and validation data BaScTiOH-R2SCAN.db – database with reference data, in sql-lite format, readable using the ase package Benzene nep-benzene.txt – MLIP based on the neuroevolution potential (NEP) form nep-benzene.zip – model ensemble with the underlying training and validation data benzene-CX.db – database with reference data, in sql-lite format, readable using the ase package reduced-benzene-tosca.zip – experimental inelastic neutron scattering data Fri, 04 Jul 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-15809229 https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-15809229 Chalmers tekniska högskola Lindgren, Eric Jackson, Adam J. Fransson, Erik Berger, Esmée Rudic, Svemir Skoro, Goran Turanyi, Rastislav Mukhopadhyay, Sanghamitra Erhart, Paul This record contains a database of atomic structures for the MA(1-x)FA(x)PbI3 system with reference data from density functional theory calculations (reference-structures.db) that was used for training the machine-learned interatomic potential model described and used in the associated publication. The database is in SQL format and is accessible via the database module of the atomic simulation environment. The record furthermore contains the neuroevolution potential model (nep.txt) that was used for the production runs in the associated publication along with five models (nep-split*.txt) that were trained on different (random 90-10) splits of the reference data. The latter can be used as an ensemble model to assess the uncertainty of predictions. Finally, there is a short Python script (demo.py) that demonstrates how the database can be accessed and the model used. This script has been tested on Python 3.12.3 using ase version 3.23.1b1 and calorine version 3.0. Fri, 11 Apr 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-15197547 https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-15197547 Chalmers tekniska högskola Hainer, Tobias Fransson, Erik Dutta, Sangita Wikor, Julia Erhart, Paul This record contains data and code that accompany the paper "Optical line shapes of color centers in solids from classical autocorrelation functions". Specifically it includea databases in ase sqlite format (*.db) with reference data from density functional theory calculations. These data were used in the construction of the machine-learned potential model using the neuroevolution potential (NEP) methodology. The model (nep.txt) is included in a format suitable for the GPUMD package (https://gpumd.org). Note that the atom type information in the databases already includes the labeling of the defect environment that is expected by the NEP model, according to the following rules Si(gs) →  P C(gs) → N Si(ex) → S C(ex) → O The "bulk" species (Si, C) remain unchanged. Fri, 09 Aug 2024 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-13284739 https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-13284739 Chalmers tekniska högskola Linderälv, Christopher Österbacka, Nicklas Wiktor, Julia Erhart, Paul This database is created by the Realistic 3D research group at Mid Sweden University, Sundsvall, Sweden. The database details are explained thoroughly in the publication which was published at the 16th International Conference on Quality of Multimedia Experience (QoMEX) in 2024. This database was also reviewed as part of the submission and publication process. You can use this database in your work under the Creative Commons Attribution 4.0 International (CC-BY 4.0) licence, provided that you cite the database as below: Zerman, E., Gond, M., Takhtardeshir, S., Olsson, R., & Sjöström, M. (2024). A Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications. The 16th International Conference on Quality of Multimedia Experience (QoMEX). IEEE. DOI: 10.1109/QoMEX61742.2024.10598264 BibTeX: @inproceedings{zerman2024spherical,  title        = {A Spherical Light Field Database for Immersive Telecommunication and Telepresence Applications},  author       = {Zerman, Emin and Gond, Manu and Takhtardeshir, Soheib and Olsson, Roger and Sj{\"o}str{\"o}m, M{\aa}rten},  booktitle    = {The 16th International Conference on Quality of Multimedia Experience (QoMEX)},  year         = {2024},  organization = {IEEE},  doi          = {10.1109/QoMEX61742.2024.10598264}} This database contains 20 spherical light fields of 1 x 60 views, captured with a consumer-grade 360-degree camera: Insta360 X3. The capture was done using a dolly to ensure the separation between consecutive views is exactly 1 cm. In addition to the original captures, this database also provides outputs for two different use cases: compression and view synthesis. Several parameters, features, and objective quality metric values are also included. N.B. Only the README file and this description have been updated after the initial submission on 2024-02-09. Fri, 09 Feb 2024 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-13342006 https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-13342006 Mittuniversitetet Zerman, Emin Gond, Manu Takhtardeshir, Soheib Olsson, Roger Sjöström, Mårten