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1,580 results for “Vulnerability”
Dataset for "Architectural Security Weaknesses in Industrial Control Systems: An Empirical Study Based on Security Advisories' Vulnerability Reports"
<p>Supplementary artifacts to "<em>Architectural Security Weaknesses in Industrial Control Systems (ICS): An Empirical Study based on Disclosed Software Vulnerabilities</em>"</p> <p>Published in the Proceedings from the 2019 IEEE International Conference on Software Architecture (ICSA)</p> <p>Package Contains:</p> <p>- Raw output showing Components, CAWEs, and CVEs per report</p> <p>- Frequency Data (# of reports) for those concerns</p> <p>- ICS Component - Term Dictionary </p> <p>- HTML versions of reports studied in paper</p>
The Secret Life of Software Vulnerabilities: A Large-Scale Empirical Study
<p>Online appendix of the paper entitled: "The Secret Life of Software Vulnerabilities: A Large-Scale Empirical Study". It contains all scripts and data required to replicate the four research questions of the study.</p> <p>Abstract: Software vulnerabilities are weaknesses in source code that can be potentially exploited to cause loss or harm. While researchers have been devising a number of methods to deal with vulnerabilities, there is still a noticeable lack of knowledge on their software engineering life cycle, for example how vulnerabilities are introduced and removed by developers. This information can be exploited to design more effective methods for vulnerability prevention and detection, as well as to understand the granularity that these methods should aim at. To investigate the life cycle of software vulnerabilities, we focus on how, when, and under which circumstances vulnerabilities are introduced in software projects, as well as whether, after how long, and how they are removed. We consider 4,097 vulnerabilities with public patches from the National Vulnerability Database—pertaining to 1,163 open-source software projects on GITHUB—and define a six-step process that involves both automated parts (e.g., using the SZZ algorithm to find the vulnerability-inducing commits) and manual analyses (e.g., how vulnerabilities were fixed). The investigated vulnerabilities can be classified in 148 categories, take on average 4.19 commits before being introduced, and remain unfixed for a median of 1,506.50 commits and 691.50 days. Most of them are introduced by developers with high workload, often when doing maintenance activities, and removed with mostly with the addition of new source code aiming at implementing further checks on inputs. We conclude by distilling practical implications on when and how vulnerability detectors should work to better assist developers in early detecting these issues.</p>
Grazing halos reveal differential ecosystem vulnerabilities in vegetated habitats
<p>Minguito-Frutos_etal_2024.xlsx contains the data to explore the relationship between habitat productivity and sea urchin consumption under different contexts. This relationship is represented by individually-produced sea urchin grazing halos, which are influenced by biotic and abiotic factors. </p> <p>Minguito-Frutos_etal_2024.R contains the R reproducible code to run all the analyses carried out in this study. </p> <p>--------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Minguito-Frutos_etal_2025.R</strong> contains the code used in the final version of the manuscript accepted for publication in <em>Ecology</em>. This script includes the final specifications of the linear mixed models (LMMs) fitted in the study, along with all statistical evaluations and the corresponding visualizations.</p>
An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')
<p>These data were produced as part of the study "An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')" (in press in Earth's Future, https://doi.org/10.1029/2023EF003895). We provide raster data at 30 arc seconds spatial resolution (folder 'raster') and vector and table data per administrative unit (folder 'admin') of five social vulnerability variables and the final Global Empirical Social Vulnerability Index (GlobE-SoVI) calculated from the five variables. Please see 'overview_table.pdf' for names and units.</p> <p>The code for data processing and analysis is available at https://github.com/lena-reimann/GlobE-SoVI (https://doi.org/10.5281/zenodo.10671539).</p>
A dataset of seabird collision and displacement vulnerability factors relatively to marine wind farms in Portugal
<p>The implementation of marine wind farms has grown considerably along northern European's northern Atlantic coasts (e.g. Baltic and North Sea) and a boom in these infrastructures is expected to take place along Europe's entire Atlantic and Mediterranean coasts. Accordingly, the Portuguese government has recently proposed priority sites for the construction of wind farms along the mainland coast. We used sensitivity mapping (Garthe & Hüppop, 2004) to assess which areas along the Portuguese coast are most sensitive for seabirds and to what extent the proposed sites for wind farm construction overlap with these areas.</p><p>This dataset contains the base data to estimate a seabird Species Sensitivity Index (SSI) (following Bradbury et al., 2014, Certain et al., 2015), including scores for 11 species-specific ecological and behavioural factors related with seabird species' (i) vulnerability to collision with wind farms (4 factors), (ii) vulnerability to displacement due to disturbance by wind farms and associated maintenance (3 factors), and (iii) conservation status (4 factors). </p><p>We reviewed the literature to mine and compile data on these factors for 34 seabird species that regularly occur along the Portuguese mainland coast. We updated factor scores, particularly for those factors that have been studied in greater detail in recent years using tracking technologies (Clairbaux & Jessopp, 2021). However, in many cases empirical data were unavailable and we used the scores presented in previous sensitivity mapping studies (Garthe & Hüppop, 2004; Bradbury et al., 2014; Certain et al., 2015; Wade et al., 2016; Serratosa & Allinson, 2022).</p>
Dataset for paper "Freihardt (2025): Environmental shocks and migration among a climate-vulnerable population in Bangladesh. Population and Environment. DOI 10.1007/s11111-025-00478-7"
<p>This is the dataset underlying the paper: </p> <p>Freihardt, J. (2025): Environmental shocks and migration among a climate-vulnerable population in Bangladesh. Population and Environment, 47, 6. DOI: 10.1007/s11111-025-00478-7.</p>
Individual behaviour, growth, survival and vulnerability to hunting in a large mammal
<p>Humans have exploited wild animals for thousands of years. Recent studies indicate that harvest-induced selection on life-history and morphological traits may lead to ecological and evolutionary changes. Less attention has been given to harvest-induced selection on behavioural traits, especially in terrestrial systems. We assessed in a wild population of large terrestrial mammals whether decades of hunting led to harvest-induced selection on trappability, a proxy of risk-taking behaviour. We investigated links between trappability, horn growth and survival across individuals in early life and quantified the correlations between early life trappability and horn growth with availability to hunters and probability of being shot. We found positive among-individual correlations between early life trappability and horn growth, early life trappability and survival, and early life horn growth and survival. Faster growing individuals were more likely to be available to hunters and shot at a young age. We found no correlations between early life trappability and availability to hunters or probability of being shot. Our results show that correlations between behaviour and growth can occur in wild terrestrial population but may be context dependent. This result highlights the difficulty in formulating general predictions about harvest-induced selection on behaviour, which can be affected by species ecology, harvesting regulations, and harvesting methods used. Future studies should investigate mechanisms linking physiological, behavioural, and morphological traits and how this effects harvest vulnerability to evaluate the potential for harvest to drive selection on behaviour in wild animal populations.</p>
The Right Atrium Affects In Silico Arrhythmia Vulnerability in Both Atria
<h1>The Right Atrium Affects In Silico Arrhythmia Vulnerability in Both Atria</h1> <div> </div> <div><strong>Authors:</strong> Patricia Martínez Díaz, Jorge Sánchez, Nikola Fitzen, Ursula Ravens, Olaf Dössel, Axel Loewe</div> <div>patricia.martinez@kit.edu / publications@ibt.kit.edu</div> <div><a href="https://doi.org/10.1016/j.hrthm.2024.01.047">doi:10.1016/j.hrthm.2024.01.047</a></div> <div> </div> <div>This dataset contains 8 biatrial meshes and 8 monoatrial (left-only) meshes, derived from MRI and CT segmentations with annotations and fibers, ready for simulations in the cardiac electrophysiology simulator <a href="https://doi.org/10.1016/j.cmpb.2021.106223">openCARP</a>. We also provide the code to reproduce a total of 576 reentries by reading the selected parameters.par and state.roe files. The meshes were generated using <a href="https://github.com/KIT-IBT/AugmentA">AugmentA code</a> and the simulated reentries were induced following the <a href="https://doi.org/10.3389/fphys.2021.656411">PEERP protocol</a> by Azzolin et al. A carputils bundle containing the <a href="https://doi.org/10.35097/1830">openCARP experiment</a>, along with all associated parameters, is publicly available. The original cardiac segmentations are part of Krueger M. et al. Personalization of atrial anatomy and electrophysiology as a basis for clinical modeling of radio-frequency ablation of atrial fibrillation <a href="https://doi.org/10.1109/tmi.2012.2201948">doi:10.1109/TMI.2012.2201948</a></div> <div> </div> <h2>Folder structure</h2> <div>The code is located in the `src` folder, the meshes in the `data` folder and the reentries in the `results` folder. </div> <div>```</div> <div>KIT_2/</div> <div>|-- src/</div> <div> |-- run.py</div> <div> |-- induceReentry.py</div> <div> |-- getStimPoints.py</div> <div> |-- element_tag.csv</div> <div> |-- al_mk_H.par</div> <div> |-- requirements.txt</div> <div> |-- reproduceReentry.py</div> <div>|-- data/</div> <div> |-- meshes/</div> <div> |-- P1/ </div> <div> |-- monoatrial/ </div> <div> |-- biatrial/</div> <div>.</div> <div>.</div> <div>.</div> <div> |-- P8 </div> <div> |-- monoatrial/ </div> <div> |-- biatrial/</div> <div>|-- results/</div> <div>|-- MESH_SCENARIO_STATE_CHAMBER/ (e.g P1_bi_M_LA) </div> <div>|-- point_X_beat_Y</div> <div>| </div> <div>|-- README.md</div> <div>```</div> <div> </div> <div>`src`: contains the source files needed to run PEERP protocol </div> <div> <ul> <li>`run.py` This is the main function to run the pacing protocol (not needed to run if reentries are only reproduced, check reproduceReentry.py)</li> <li>`induceReentry.py` Contains a list of pacing protocols. The PEERP protocol is included here</li> <li>`getStimPoints.py` Extract the stimulation points</li> <li>`element_tag.csv` Region tag numbering</li> <li>`al_mk_H.par` Par file with ionic scaling factors for three states; H:Healthy, M:Mild, S:Severe</li> <li>`requirements.txt` Packages to create the virtual enviroment. (This was my output of ```pip3 list> requirements.txt```)</li> <li>`reproduceReentry.py` Reentries can be reproduced given a selected folder where the .par and .roe files are stored.</li> </ul> </div> <div> </div> <div>`data`: contains the `meshes` folder with the bilayer meshes in openCARP (.elem, .lon and .pts) and .vtk format. Synthetic fibrotic distributions are included in the the .regele files.</div> <div> <ul> <li>`meshes/P1/monoatrial/LA_stim_points.txt` stimulation points for the PEERP protocol</li> <li>`meshes/P1/monoatrial/LA_bilayer_with_fiber_elems_not_conductive_M.regele` Element ids corresponding to synthetic fibrotic distribution with respect to remodeling states M and S. H state was modelled without fibrosis</li> </ul> </div> <div> </div> <h2>Reproduce the reentries </h2> <div>You can generate the .igb file of a specific reentry by selecting the corresponding folder in the results directory. An example is given to reproduce the reentry in P1_bi_M_LA/point_0_beat_2/reproduce_reentry.igb. Select the folder `--par_file_directory`and set `--tend` to define the duration of the simulation in miliseconds.</div> <div>_HINT: We recommend keeping the folder structure so that the other parameters, such as: mesh, scenario, state and chamber, can be read from the --par_file_directory. Otherwise, the meshes and results directories need to be modified._</div> <div>```</div> <div>cd src/</div> <div>reproduceReentry.py --par_file_directory ../results/P1_bi_M_LA/point_0_beat_2 --tend 1500</div> <div> </div> <div>```</div> <div></div> <div> </div> <div> </div> <h2>Preparation before running the PEERP pacing protocol</h2> <div> </div> <div>Follow the next steps if you want to run the PEERP pacing protocol, either for the provided meshes or for your own meshes. To run the PEERP protocol in a controlled environment, it is recommended, before running the run.py, to create a virtual environment. Go to your terminal and type: </div> <div>```</div> <div>cd src/</div> <div>python3 -m venv ./myEnv</div> <div>source ./myEnv/bin/activate</div> <div>pip3 install -r requirements.txt</div> <div>```</div> <div> </div> <div>You need to add carputils to your `PATH`. You can run the code in the terminal or use and IDE to debug the code. </div> <div>Note: I am using PyCharm 2020.3. and in Settings --> Python interpreter --> show all and then in the (+) symbol, add the path to carputils there:</div> <div> </div> <div>Otherwise you can add this extra lines at the beginning of `run.py``:</div> <div>```</div> <p># Replace '/path/to/carputils' with the actual path to your carputils package</p> <p>carputils_path = '/path/to/carputils'</p> <p># Add the carputils path to sys.path</p> <div>sys.path.append(carputils_path)</div> <div>```</div> <h2>Run the PEERP protocol</h2> <div> </div> <div>The following example runs the PEERP from a single stimulation point. If you want to run PEERP over all the points, simply add the flag --run_all_points 1 </div> <div>```</div> <div>cd src/</div> <div>python3 run.py --giL 0.4166 --geL 1.458 --cv 0.8 --mesh monoatrial --protocol PEERP --pacing 122718 --stim_file LA_stim_points.txt --geometry LA_bilayer_with_fiber_um --cell_bcl 500 --model Courtemanche --ionic_prop_file al_mk_S.par --max_n_beats_PEERP 1 --overwrite-behaviour overwrite</div> <div>```</div> <div> </div> <h2>Running your own experiment and making your own changes</h2> <div>Extract the stimulation points on your mesh, where the PEERP protocol will be run: </div> <div>```</div> <div>python3 getStimPoints.py --mesh monoatrial --tolerance 20000 --stim_file LA_stim_points.txt --chamber LA</div> <div>```</div> <div> </div> <div>Tune conduction velocity (CV) and conductivites. The code expects the intracellular end extracellular longitudinal conductivity values as an input. We used `tuneCV` to fit CV=0.7m/s with dx=0.4mm and dt=20us</div> <div>If you want to adjust the values, run in the terminal:</div> <div>```</div> <div>tuneCV --resolution 400 --model Courtemanche --velocity 0.7 --converge True --sourceModel monodomain --surf True --dt 20</div> <div>```</div> <div>You can provide the location of the start of the activation by selecting the desired point ID:</div> <div> <ul> <li>Load the mesh in Paraview (or Meshalyzer)</li> <li>click on the ? symbol</li> <li>save the ID and change the `--pacing` argument </li> </ul> </div> <div> </div> <div>Call `run.py` with a new mesh. The protocol starts by prepacing the mesh and then using the last beat as initial condition tu run the PEERP.</div> <div>Be aware that for a monoatrial mesh you might need to give the new id for the location of the earliest activation. Change `12345` to your desired point ID.</div> <div>```</div> <div>python3 run.py --mesh newMesh --pacing 12345 --protocol prepace --stim_file LA_stim_points.txt</div> <div>```</div> <div> </div> <div>Run the protocol with different electrical remodelling stage. You can change the .par file or select one file from the three provided: </div> <div>```</div> <div>python3 run.py --mesh newMesh --pacing 12345 --protocol PEERP --stim_file LA_stim_points.txt --args.ionic_prop 'l_mk_M.par'</div> <div>```</div> <div> </div> <div>You can also try to run a biatrial example. The biatrial mesh is also provided. You need to extract the points on the RA surface using `getStimPoints.py`, to run the RA experiment: </div> <div>```</div> <div>cd src</div> <div>python3 getStimPoints.py --mesh biatrial --tolerance 20000 --stim_file RA_stim_points.txt --chamber RA</div> <div>```</div> <div>Then run PEERP twice, one per each chamber:</div> <div> </div> <div>```</div> <div>python3 run.py --mesh biatrial --geometry LA_RA_bilayer_with_fiber --pacing 12345 --stim_file LA_endo_2cm.txt --args.ionic_prop 'l_mk_M.par'</div> <div>python3 run.py --mesh biatrial --geometry LA_RA_bilayer_with_fiber --stim_file LA_stim_points.txt --args.ionic_prop 'l_mk_M.par'</div> <p> </p>
Vulnerability tools - Austrian Alps (Austria)
<p>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Austrian Alps Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</p>
Vulnerability tools - Spanish Pyrenees (Spain)
<p><span>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for theSpanish Pyrenees Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</span></p>
Vulnerability tools - Dinaric Mountains (Serbia)
<p><span>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Dinaric Mountains Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</span></p>
Vulnerability tools - Corsica (France)
<p>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Corsica Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</p>
Vulnerability tools - Transdanubian Mountains (Hungary)
<p>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Transdanubian Mountains Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</p>
Vulnerability tools - Slovak Carpathian mountains (Slovakia)
<p><span>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Slovak Carpathian mountains Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</span></p>
Vulnerability tools - Betic Systems (Spain)
<p><span>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Betic Systems Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</span></p>
Vulnerability tools - Northern Apennines (Italy)
<p><span>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Northern Apennines Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</span></p>
Vulnerability tools - Central Apennines (Italy)
<p><span>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Central Apennines Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</span></p>
Vulnerability tools - Maleshevski Mountains (North Macedonia)
<p><span>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Maleshevski Mountains Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</span></p>
Vulnerability tools - Maciço Noroeste (Portugal)
<p><span>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Maciço Noroeste Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</span></p>
Vulnerability tools - Southern Romanian Carpathian mountains (Romania)
<p><span>The MOVING project has developed accessible <strong>tools </strong>designed to assess susceptibility and vulnerability within the region, ready to be used by both experts and the general audience. This document synthesises crucial information for the Southern Romanian Carpathian mountains Region, particularly focusing on the Participatory Vulnerability Matrix and the Spatial Vulnerability Map. Furthermore, it includes <strong>supplementary maps and figures </strong>detailing various aspects such as the delineation of Reference Landscape, distribution of land systems, areas affected by wildfires, susceptibility to floods across different return periods, severity of forest disturbances, rainfall erosivity, and more.</span></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.