cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10142982
title: Dataset: Behavior of Participants in Hands-on Cybersecurity Training Suitable for Process Mining

description: The dataset stores event logs of 52 trainees participating in a hands-on cybersecurity exercise organized in the KYPO Cyber Range. The data includes a training scenario (expected behavior), raw event logs in the JSON format, and aggregated behavioral data suitable for process mining analysis.
Content

Data. The data file is structured as follows:

training_definition.json: The exercise content – cybersecurity tasks and hints. The training is based on the Locust 3302 game adapted to an insider attack scenario.
training_events: Recorded progress of trainees within the exercise, i.e., the status of completing tasks.
sandbox_events: Recorded commands executed on network hosts.
process_mining.csv: Complete PM-ready dataset suitable for process discovery or conformance analysis.
process_mining_simplified.csv : Reduced PM-ready dataset with semantically identical events being removed.
Tool: A Java application used to aggregate raw JSON data and transform them into a CSV format suitable for process mining techniques.
DOI: 10.5281/zenodo.10142981
title: Dataset: Behavior of Participants in Hands-on Cybersecurity Training Suitable for Process Mining

description: This repository contains supplementary materials for the following journal paper:

Radek Ošlejšek, Martin Macák, Karolína Dočkalová Burská.Hands-on cybersecurity training behavior data for process mining.In Elsevier Data in Brief. 2023.Available as open-access article on https://doi.org/10.1016/j.dib.2023.109956

Contents

Datasets store event logs of trainees participating in hands-on cybersecurity exercises organized in the KYPO Cyber Range. The data includes training scenarios (expected behavior), raw event logs in the JSON format, and aggregated behavioral data suitable for process mining analysis.



Data1: A dataset of 52 trainees participating in the Locust 3302 exercise adapted an insider attack scenario. No time restrictions were posed on playtime. The data file is structured as follows:



training_definition.json: The exercise content – cybersecurity tasks and hints. The training is based on the Locust 3302 game adapted to an insider attack scenario.

training_events: Recorded progress of trainees within the exercise, i.e., the status of completing tasks.

command_histories: Recorded commands executed on network hosts.

process_mining.csv: Complete PM-ready dataset suitable for process discovery or conformance analysis.

process_mining_simplified.csv : Reduced PM-ready dataset with semantically identical events being removed.



Data2: A dataset of 48 trainees participating in the original Locust 3302 exercise. Three supervised training sessions were restricted to two hours of playtime. The structure follows the structure of Data1.

Tool: A Java application used to aggregate raw JSON data and transform them into a CSV format suitable for process mining techniques.


How to cite

If you use or build upon the materials, please use the BibTeX entry below to cite the original work.

@article{Oslejsek2023dataset,
    author    = {Radek O\v{s}lej\v{s}ek and Martin Mac\'{a}k and Karol\'{i}na {Do\v{c}kalov\'{a} Bursk\'{a}}},
    title     = {Hands-on cybersecurity training behavior data for process mining},
    journal   = {{Data in Brief}},
    publisher = {Elsevier},
    issn      = {2352-3409},
    year      = {2023},
    volume    = {52},
    doi       = {10.1016/j.dib.2023.109956},
    url       = {https://www.sciencedirect.com/science/article/pii/S2352340923009873}
}
DOI: 10.5281/zenodo.10143987
title: Dataset: Behavior of Participants in Hands-on Cybersecurity Training Suitable for Process Mining

description: Datasets store event logs of trainees participating in hands-on cybersecurity exercises organized in the KYPO Cyber Range. The data includes training scenarios (expected behavior), raw event logs in the JSON format, and aggregated behavioral data suitable for process mining analysis.
Contents

Data1: A dataset of 52 trainees participating in the Locust 3302 exercise adapted an insider attack scenario. The data file is structured as follows:

training_definition.json: The exercise content – cybersecurity tasks and hints. The training is based on the Locust 3302 game adapted to an insider attack scenario.
training_events: Recorded progress of trainees within the exercise, i.e., the status of completing tasks.
sandbox_events: Recorded commands executed on network hosts.
process_mining.csv: Complete PM-ready dataset suitable for process discovery or conformance analysis.
process_mining_simplified.csv : Reduced PM-ready dataset with semantically identical events being removed.
Data2: A dataset of 48 trainees participating in the original Locust 3302 exercise. The structure follows the structure of Data1.
Tool: A Java application used to aggregate raw JSON data and transform them into a CSV format suitable for process mining techniques.
DOI: 10.5281/zenodo.10170480
title: Dataset: Behavior of Participants in Hands-on Cybersecurity Training Suitable for Process Mining

description: Datasets store event logs of trainees participating in hands-on cybersecurity exercises organized in the KYPO Cyber Range. The data includes training scenarios (expected behavior), raw event logs in the JSON format, and aggregated behavioral data suitable for process mining analysis.

Contents



Data1: A dataset of 52 trainees participating in the Locust 3302 exercise adapted an insider attack scenario. No time restrictions were posed on playtime. The data file is structured as follows:



training_definition.json: The exercise content – cybersecurity tasks and hints. The training is based on the Locust 3302 game adapted to an insider attack scenario.

training_events: Recorded progress of trainees within the exercise, i.e., the status of completing tasks.

command_histories: Recorded commands executed on network hosts.

process_mining.csv: Complete PM-ready dataset suitable for process discovery or conformance analysis.

process_mining_simplified.csv : Reduced PM-ready dataset with semantically identical events being removed.



Data2: A dataset of 48 trainees participating in the original Locust 3302 exercise. Three supervised training sessions were restricted to two hours of playtime. The structure follows the structure of Data1.

Tool: A Java application used to aggregate raw JSON data and transform them into a CSV format suitable for process mining techniques.


How to cite

If you use or build upon the materials, please use the BibTeX entry below to cite the original work.

@article{Oslejsek2023dataset,
    author    = {Radek O\v{s}lej\v{s}ek and Martin Mac\'{a}k and Karol\'{i}na {Do\v{c}kalov\'{a} Bursk\'{a}}},
    title     = {Hands-on cybersecurity training behavior data for process mining},
    journal   = {{Data in Brief}},
    publisher = {Elsevier},
    issn      = {2352-3409},
    year      = {2023},
    volume    = {52},
    doi       = {10.1016/j.dib.2023.109956},
    url       = {https://www.sciencedirect.com/science/article/pii/S2352340923009873}
}
DOI: 10.5281/zenodo.10298187
title: Dataset: Behavior of Participants in Hands-on Cybersecurity Training Suitable for Process Mining

description: This repository contains supplementary materials for the following journal paper:

Radek Ošlejšek, Martin Macák, Karolína Dočkalová Burská.Hands-on cybersecurity training behavior data for process mining.In Elsevier Data in Brief. 2023.Available as open-access article on https://doi.org/10.1016/j.dib.2023.109956

Contents

Datasets store event logs of trainees participating in hands-on cybersecurity exercises organized in the KYPO Cyber Range. The data includes training scenarios (expected behavior), raw event logs in the JSON format, and aggregated behavioral data suitable for process mining analysis.



Data1: A dataset of 52 trainees participating in the Locust 3302 exercise adapted an insider attack scenario. No time restrictions were posed on playtime. The data file is structured as follows:



training_definition.json: The exercise content – cybersecurity tasks and hints. The training is based on the Locust 3302 game adapted to an insider attack scenario.

training_events: Recorded progress of trainees within the exercise, i.e., the status of completing tasks.

command_histories: Recorded commands executed on network hosts.

process_mining.csv: Complete PM-ready dataset suitable for process discovery or conformance analysis.

process_mining_simplified.csv : Reduced PM-ready dataset with semantically identical events being removed.



Data2: A dataset of 48 trainees participating in the original Locust 3302 exercise. Three supervised training sessions were restricted to two hours of playtime. The structure follows the structure of Data1.

Tool: A Java application used to aggregate raw JSON data and transform them into a CSV format suitable for process mining techniques.


How to cite

If you use or build upon the materials, please use the BibTeX entry below to cite the original work.

@article{Oslejsek2023dataset,
    author    = {Radek O\v{s}lej\v{s}ek and Martin Mac\'{a}k and Karol\'{i}na {Do\v{c}kalov\'{a} Bursk\'{a}}},
    title     = {Hands-on cybersecurity training behavior data for process mining},
    journal   = {{Data in Brief}},
    publisher = {Elsevier},
    issn      = {2352-3409},
    year      = {2023},
    volume    = {52},
    doi       = {10.1016/j.dib.2023.109956},
    url       = {https://www.sciencedirect.com/science/article/pii/S2352340923009873}
}
DOI: 10.5281/zenodo.10404996
title: Knowledge flows in makerspaces in Poland, Czechia and Slovakia

description: Dataset for the sample of makerspaces in Poland, Czechia and Slovakia
DOI: 10.5281/zenodo.10404997
title: Knowledge flows in makerspaces in Poland, Czechia and Slovakia

description: Dataset for the sample of makerspaces in Poland, Czechia and Slovakia
DOI: 10.6084/m9.figshare.23822448
title: Small RNA-seq analysis

description: RNA seq analysis for article "Gene signatures in human granulosa cells from integrated analyses of multiple datasets"
DOI: 10.6084/m9.figshare.23822448.v1
title: Small RNA-seq analysis

description: RNA seq analysis for article "Gene signatures in human granulosa cells from integrated analyses of multiple datasets"
DOI: 10.5281/zenodo.10404939
title: Water Stable Isotopes in the Holthedalfonna Shallow Cores

description: In the dataset, you will find the depth profile of stable oxygen isotopes taken from surface cores of the Holthedhalfonna glacier at Svalbard (79°09′N, 13°23′E; 1150 m a.s.l.). These samples were collected at a spatial resolution of 5 to 7 cm and retrieved in 2012, 2015, 2017, and 2019. The depth is converted to centimeters of water equivalent using density measurements. To align and merge the various cores, depth data from mass balance records available at www.mosj.no were used. All isotopic measurements were conducted utilizing a Picarro L1102-i analyzer coupled with a CTC Pal autosampler. Data for the 2012 cores are available in Spolaor et al. 2013, the 2015 core in Ruppel et al. 2017, and the 2017 core in Burgay et al. 2021. For additional information contact andrea.spolaor@cnr.it. 

This work has been supported by the “Programma di Ricerca in Artico” (PRA, project number PRA2019-0011, Sentinel); by the Svalbard Science Forum/Research Council of Norway through the Arctic Field Grant call (project ASIHAD, ISSICOS, BIOMASS), by French Polar Institute IPEV (Institut Polaire Français Paul-Emile Victor) science funding (programs 399 and 1192) and the Svalbard Strategic Grant (project C2S3, nr. 257636, SnowNet nr. 295779 and BC3D nr. 283466). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 689443 via project iCUPE (Integrative and Comprehensive Understanding on Polar Environments). This research has been partially funded by the University of Perugia Research Action no. 5 “Climate, Energy, and Mobility”. Cryogrid simulations have been supported by the Nansen Legacy project (Research Council of Norway grant 276730) and SIOS infraNor (Research Council of Norway grant 269927).
DOI: 10.5281/zenodo.10404940
title: Water Stable Isotopes in the Holthedalfonna Shallow Cores

description: In the dataset, you will find the depth profile of stable oxygen isotopes taken from surface cores of the Holthedhalfonna glacier at Svalbard (79°09′N, 13°23′E; 1150 m a.s.l.). These samples were collected at a spatial resolution of 5 to 7 cm and retrieved in 2012, 2015, 2017, and 2019. The depth is converted to centimeters of water equivalent using density measurements. To align and merge the various cores, depth data from mass balance records available at www.mosj.no were used. All isotopic measurements were conducted utilizing a Picarro L1102-i analyzer coupled with a CTC Pal autosampler. Data for the 2012 cores are available in Spolaor et al. 2013, the 2015 core in Ruppel et al. 2017, and the 2017 core in Burgay et al. 2021. For additional information contact andrea.spolaor@cnr.it. 

This work has been supported by the “Programma di Ricerca in Artico” (PRA, project number PRA2019-0011, Sentinel); by the Svalbard Science Forum/Research Council of Norway through the Arctic Field Grant call (project ASIHAD, ISSICOS, BIOMASS), by French Polar Institute IPEV (Institut Polaire Français Paul-Emile Victor) science funding (programs 399 and 1192) and the Svalbard Strategic Grant (project C2S3, nr. 257636, SnowNet nr. 295779 and BC3D nr. 283466). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 689443 via project iCUPE (Integrative and Comprehensive Understanding on Polar Environments). This research has been partially funded by the University of Perugia Research Action no. 5 “Climate, Energy, and Mobility”. Cryogrid simulations have been supported by the Nansen Legacy project (Research Council of Norway grant 276730) and SIOS infraNor (Research Council of Norway grant 269927).
DOI: 10.21227/6hx5-2g53
title: Supplementary Dataset For: Simultaneous Removing of Noise and Correction of Motion Warping in Neuron Calcium Imaging Using a Pipeline Structure of Self-supervised Deep Learning Models

description: Calcium imaging visualizes specific activity of neurons through active sensors, which makes it easy to study the neuronal behavior of animals' learning processes and cognition and helps to promote the use of animal models for neuroscience research. However, motion artifacts and background noise can affect calcium imaging, especially when watching awake animals while they are exposed to low-dose laser irradiation. This makes it impossible to fully understand how neural circuits work. As a result, imaging results are often warped and contain significant random noise. This dataset contains two simultaneously warped and noised calcium imaging stacks collected from a confocal microscopy calcium imaging system without postprocessing.
DOI: 10.5519/qd.tj6fsiv2
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (257 records)
DOI: 10.5281/zenodo.10404906
title: Climate (thermal anomalies) and dengue incidence in Brazil aggregated by microregions (MRG)

description: Climate (thermal anomalies) and dengue incidence in Brazil aggregated by microregions (MRG). The frequency of temperature anomalies wee calculated between 2007 and 2020. Dengue disease notification frequency were aggregated by MRG and divided by population to create incidence rates between 2007 and 2020.
DOI: 10.5281/zenodo.10404907
title: Climate (thermal anomalies) and dengue incidence in Brazil aggregated by microregions (MRG)

description: Climate (thermal anomalies) and dengue incidence in Brazil aggregated by microregions (MRG). The frequency of temperature anomalies wee calculated between 2007 and 2020. Dengue disease notification frequency were aggregated by MRG and divided by population to create incidence rates between 2007 and 2020.
DOI: 10.57745/cyey71
title: Barail_dataset_v3.xlsx

description: :unav
DOI: 10.5281/zenodo.7699054
title: Migration Provisions in Preferential Trade Agreements (MITA) Dataset

description: The MITA dataset is the broadest and most inclusive databases on preferential trade agreements with regard to migration. We include all international trade agreements that were signed between 1960 and 2020 and whose text is publicly available. This includes bilateral, plurilateral and regional agreements. We exclude framework agreements. We do not list accessions and withdrawals to existing agreements as they only constitute a change in the signing parties but not a change in an agreement’s provisions. Accordingly, we include consolidated PTAs that include additional migration provisions to the initial agreement. Signing parties can be countries but also group of countries such as regional and supranational organizations. Beside the main text of an agreement, we also take into account annexes and side-letters that are part of an agreement. To maximize the coverage of the database, we combined various data sources such as the World Trade Organization, the DESTA database (Dür et al., 2014) and websites of governments and intergovernmental organizations. As source data, we consider all text documents that are an integrated part of an agreement, such as the main text, annexes, side letters and additional protocols that were signed together with the main agreement.
DOI: 10.5281/zenodo.7837942
title: Migration Provisions in Preferential Trade Agreements (MITA) Dataset

description: The MITA dataset is the broadest and most inclusive databases on preferential trade agreements with regard to migration. We include all international trade agreements that were signed between 1960 and 2020 and whose text is publicly available. This includes bilateral, plurilateral and regional agreements. We exclude framework agreements. We do not list accessions and withdrawals to existing agreements as they only constitute a change in the signing parties but not a change in an agreement’s provisions. Accordingly, we include consolidated PTAs that include additional migration provisions to the initial agreement. Signing parties can be countries but also group of countries such as regional and supranational organizations. Beside the main text of an agreement, we also take into account annexes and side-letters that are part of an agreement. To maximize the coverage of the database, we combined various data sources such as the World Trade Organization, the DESTA database (Dür et al., 2014) and websites of governments and intergovernmental organizations. As source data, we consider all text documents that are an integrated part of an agreement, such as the main text, annexes, side letters and additional protocols that were signed together with the main agreement.
DOI: 10.5281/zenodo.10405036
title: Data for BY-COVID Pathways to MINERVA Analysis Workflow

description: Source data (GSE182152) was analysed with WFHub:688 to generate these datasets
DOI: 10.5281/zenodo.10405035
title: Data for BY-COVID Pathways to MINERVA Analysis Workflow

description: Source data (GSE182152) was analysed with WFHub:688 to generate these datasets
DOI: 10.5281/zenodo.7699053
title: Migration Provisions in Preferential Trade Agreements (MITA) Dataset

description: The MITA dataset is the broadest and most inclusive databases on preferential trade agreements with regard to migration. We include all international trade agreements that were signed between 1960 and 2020 and whose text is publicly available. This includes bilateral, plurilateral and regional agreements. We exclude framework agreements. We do not list accessions and withdrawals to existing agreements as they only constitute a change in the signing parties but not a change in an agreement’s provisions. Accordingly, we include consolidated PTAs that include additional migration provisions to the initial agreement. Signing parties can be countries but also group of countries such as regional and supranational organizations. Beside the main text of an agreement, we also take into account annexes and side-letters that are part of an agreement. To maximize the coverage of the database, we combined various data sources such as the World Trade Organization, the DESTA database (Dür et al., 2014) and websites of governments and intergovernmental organizations. As source data, we consider all text documents that are an integrated part of an agreement, such as the main text, annexes, side letters and additional protocols that were signed together with the main agreement.
DOI: 10.5281/zenodo.7837954
title: Migration Provisions in Preferential Trade Agreements (MITA) Dataset

description: The MITA dataset is the broadest and most inclusive databases on preferential trade agreements with regard to migration. We include all international trade agreements that were signed between 1960 and 2020 and whose text is publicly available. This includes bilateral, plurilateral and regional agreements. We exclude framework agreements. We do not list accessions and withdrawals to existing agreements as they only constitute a change in the signing parties but not a change in an agreement’s provisions. Accordingly, we include consolidated PTAs that include additional migration provisions to the initial agreement. Signing parties can be countries but also group of countries such as regional and supranational organizations. Beside the main text of an agreement, we also take into account annexes and side-letters that are part of an agreement. To maximize the coverage of the database, we combined various data sources such as the World Trade Organization, the DESTA database (Dür et al., 2014) and websites of governments and intergovernmental organizations. As source data, we consider all text documents that are an integrated part of an agreement, such as the main text, annexes, side letters and additional protocols that were signed together with the main agreement.
DOI: 10.6084/m9.figshare.24099831.v1
title: Data for the article: <b>An alpine bird is reducing its distribution and moving to higher altitudes in its southernmost European distribution</b>

description: The dataset corresponds to the presence-absence of the Northern Wheatear (<i>Oenanthe o</i><i>enanthe</i>) in each Operational Geographic Unit (OGU) of our study area for both time periods (2003 and 2022). These are the presence-absence data we used to carry out the analyses of our study, which were obtained from the two most recent Spanish bird atlases.The uploaded excel file contains three columns whose information is explained below:- UTMCODE --&gt; Identifier of each OGU in our study area- Oena_oena_2003 --&gt; Presence (1) / Absence (0) of the species in each OGU in 2003- Oena_oena_2022 --&gt; Presence (1) / Absence (0) of the species in each OGU in 2022
DOI: 10.6084/m9.figshare.24803616
title: Patient 5 Raw Data

description: This item contains the raw 10X Visium data for patient 5 from the manuscript "<b>Niche-DE: niche differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions</b>"
DOI: 10.6084/m9.figshare.24803571
title: Patient 4 Raw Data

description: This item contains the raw 10X Visium data for patient 4 from the manuscript "<b>Niche-DE: niche differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions</b>"
DOI: 10.6084/m9.figshare.24803352
title: Integrated Liver Data

description: This item contains the code and data to perform the integrated liver data analysis from the manuscript "<b>Niche-DE: niche differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions</b>"
DOI: 10.6084/m9.figshare.24803364
title: Liver 1 Subclone Analysis

description: This item contains the code and ads files required to perform the liver sub clone analysis in the manuscript "<b>Niche-DE: niche differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions</b>"