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66 results for “Vulnerabilities dataset”
Cross-Language Vulnerability Dataset with File Changes and Commit Messages
<p>Cross-Language Vulnerability Dataset with File Changes and Commit Messages</p>
Dataset from 'Age effects on saccadic adaptation: Evidence from different paradigms reveals specific vulnerabilities', Journal of Vision, 17(6):9, 1-18. DOI: 10.1167/17.6.9
<p>Dataset associated with the following publication:</p> <p>Huang, J., Gegenfurtner K., Schütz, A.C., & Billino, J. (2017). Age effects on saccadic adaptation: Evidence from different paradigms reveals specific vulnerabilities. Journal of Vision, 17(6:9, 1-18. DOI: 10.1167/17.6.9 <span><a></a></span></p> <p>--------------------------------------------------------------</p> <p>For each experiment one folder is provided.</p> <p>Each folder contains 2 data files and 1 description file providing column labels.<br> One data file contains data on saccadic eye movements, the other one gives measures of executive control.</p> <p>-------------------------------------------------------------</p> <p> </p>
VulSATDˆ{DS}: a dataset for vulnerabilities and self-admitted technical debt and its replication package
<p>VulSATDˆ{DS} is a dataset of functions in C/C++ extracted from 12 open-source projects and labelled for the existence of self-admitted technical debt (SATD) and vulnerability. It was presented in the paper "VulSATDˆ{DS}: a dataset for vulnerabilities and self-admitted technical debt." This upload contains both the dataset (file: VulSATDDS.zip) and the replication package that allows the recreation of the dataset from its sources (replication.zip).</p>
Replication Package for "Cracks in The Stack: Hidden Vulnerabilities and Licensing Risks in LLM Pre-Training Datasets"
<p>Replication Package for "Cracks in The Stack: Hidden Vulnerabilities and Licensing Risks in LLM Pre-Training Datasets"<br><br>Includes datasets, and bash scripts.</p>
VUDENC - datasets for vulnerabilities
<p>The datasets (plain) and the embedded datasets (samples X and labels Y) for each of the seven types of vulnerabilities.</p>
Coastal dataset including exposure and vulnerability layers, Deliverable 3.1 - ECFAS Project (GA 101004211), www.ecfas.eu
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p><p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.</p><p><i><strong>Description of the containing files inside the Dataset.</strong></i></p><p>The ECFAS Coastal Dataset represents a single access point to publicly available Pan-European datasets that provide key information for studying coastal areas. The publicly available datasets listed below have been clipped to the coastal area extent, quality-checked and assessed for completeness and usability in terms of coverage, accuracy, specifications and access. The dataset was divided at European country level, except for the Adriatic area which was extracted as a region and not at the country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.</p><p>Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the above mentioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Also, the dataset contains 2 layers with statistics information regarding the population of Europe per sex and age divided in administrative units at NUTS level 3. The first layer includes information for the whole of Europe and the second layer has only the information regarding the population at the Coastal area. Finally, the dataset includes the global database of Floods protection standards. Below there are tables which present the dataset.</p><p>* Adriatic folder contains the countries: Slovenia, Croatia, Montenegro, Albania, Bosnia and Herzegovina</p><p>* Malta was added to the dataset</p><p><strong>Copernicus Land Monitoring Service:</strong></p><p><strong>Coastal LU/LC</strong></p><p>Scale 1:10.000; A Copernicus hotspot product to monitor landscape dynamics in coastal zones</p><p><strong>EU-Hydro - Coastline</strong></p><p>Scale 1:30.000; EU-Hydro is a dataset for all European countries providing the coastline</p><p><strong>Natura 2000</strong></p><p>Scale 1: 100000; A Copernicus hotspot product to monitor important areas for nature conservation</p><p><strong>European Settlement Map</strong></p><p>Resolution 10m; A spatial raster dataset that is mapping human settlements in Europe</p><p><strong>Imperviousness Density</strong></p><p>Resolution 10m; The percentage of sealed area</p><p><strong>Impervious Built-up</strong></p><p>Resolution 10m; The part of the sealed surfaces where buildings can be found</p><p><strong>Grassland 2018</strong></p><p>Resolution 10m; A binary grassland/non-grassland product</p><p><strong>Tree Cover Density 2018</strong></p><p>Resolution 10m; Level of tree cover density in a range from 0-100%</p><p> </p><p><strong>Joint Research Center:</strong></p><p><strong>Global Human Settlement Population Grid</strong><br><strong>GHS-POP)</strong></p><p>Resolution 250m; Residential population estimates for target year 2015</p><p><strong>GHS settlement model layer</strong><br><strong>(GHS-SMOD)</strong></p><p>Resolution 1km: The GHS Settlement Model grid delineates and classify settlement typologies via a logic of population size, population and built-up area densities</p><p><strong>GHS-BUILT</strong></p><p>Resolution 10m; Built-up grid derived from Sentinel-2 global image composite for reference year 2018</p><p><strong>ENACT 2011 Population Grid</strong></p><p><strong>(ENACT-POP R2020A)</strong></p><p>Resolution 1km; The ENACT is a population density for the European Union that take into account major daily and monthly population variations</p><p><strong>JRC Open Power Plants Database (JRC-PPDB-OPEN)</strong></p><p>Europe's open power plant database</p><p><strong>GHS functional urban areas</strong><br><strong>(GHS-FUA R2019A)</strong></p><p>Resolution 1km; City and its commuting zone (area of influence of the city in terms of labour market flows)</p><p><strong>GHS Urban Centre Database</strong><br><strong>(GHS-UCDB R2019A)</strong></p><p>Resolution 1km; Urban Centres defined by specific cut-off values on resident population and built-up surface</p><p> </p><p><strong>Additional Data:</strong></p><p><strong>Open Street Map (OSM)</strong></p><p>BF, Transportation Network, Utilities Network, Places of Interest</p><p><strong>CEMS</strong></p><p>Data from Rapid Mapping activations in Europe</p><p><strong>GeoNames</strong></p><p>Populated places, Adm. units, Hydrography, Forests, Hills/Mountains, Parks, etc.</p><p><strong>Global Administrative Areas</strong></p><p>Administrative areas of all countries, at all levels of sub-division</p><p><strong>NUTS3 Population Age/Sex Group</strong></p><p>Eurostat population by age and sex statistics interescted with the NUTS3 Units</p><p><strong>FLOPROS</strong></p><p>A global database of FLOod PROtection Standards, which comprises information in the form of the flood return period associated with protection measures, at different spatial scales</p><p> </p><p><i><strong>Disclaimer:</strong></i></p><p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p><p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p>
Dataset: Automatic Derivation of Vulnerability Models for Software Architectures
<p>Dataset for our publication "<em>Automatic Derivation of Vulnerability Models for Software Architectures</em>"</p>
Digital Literacy, Ability and Vulnerability Dataset
<p>Dataset for Digital Literacy, Ability and Vulnerability Dataset</p>
Datasets for Main results for Publication "Strong hydroclimatic controls on vulnerability to subsurface nitrate contamination across Europe"
<p>These datasets contain the main underlying results of the manuscript entitled "Strong hydroclimatic controls on vulnerability to subsurface nitrate contamination across Europe" by Kumar et al., 2019. Details about the datasets can be obtained from the netcdf attributes or through description provided in the manuscript.</p> <p>Datasets based on analysis of the Damkoehler (DK) number are given in two formats: In v0.3, the (denitrification) reaction rates (RT) only consider the impact of instantaneous daily environmental variables; whereas in v0.4, they are estimated acknowledging different impact of environmental conditions over the entire transport times. </p> <p>Further queries regarding these datasets can be directed to Rohini Kumar: rohini.kumar (at) ufz.de. </p> <p> </p> <p> </p> <p> </p>
Dataset for: Heat domes increase vulnerability of native stingless bees by simultaneously weakening key survival traits
Open the record for dataset details and reuse information.
Dataset used for: Breeding migrations by bighorn sheep males: searching for mates increases vulnerability to hunting
<p>In some species where male mating success largely depends on intrasexual competition, males can adopt migratory orresident strategies to seek breeding opportunities. The resulting mixture of resident and migrant tactics within a population can have important ecological, genetic and evolutionary consequences at the metapopulation level. Bighorn sheep <em>Ovis canadensis</em> males establish a linear dominance hierarchy that influences their mating tactics. Some males perform breeding migrations during the pre-rut and rut to seek mating opportunities, but little is known about these seasonal movements. We analysed presence/absence data for 62 marked bighorn males during six mating seasons (20-32 males/year) in the Sheep River Provincial Park, Alberta, Canada, where hunting was not allowed. On average, about half of males left their natal population to rut elsewhere. The proportion of males leaving (yearly range 15% - 69%) increased as the number of resident mature males increased and the populational sex ratio decreased, with fewer females during the pre-rut. Among those leaving the park, 24% did so in October, while the trophy sheep hunting season was open. Detailed monitoring of breeding migrations in protected populations could inform management strategies to limit evolutionary impacts of hunting.</p>
Evaluating Deep Learning-based Vulnerability Detection Models on Realistic Datasets
<p>This package contains all the data we used for our experiments.<br> </p>
Dataset used for: Breeding migrations by bighorn sheep males: searching for mates increases vulnerability to hunting
Open the record for dataset details and reuse information.
Graph D2A: D2A Dataset for Vulnerability Detection Transformed into ECPG Format for Training GNNs
<p>The Graph D2A is based on the dataset 'D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis'. Graph D2A was created by transforming individual samples from D2A into the form of Extended Code Property Graphs (ECPGs). The resulting dataset is designed for graph neural network models (GNNs) for detection on vulnerabilities in source code.</p> <p>Graph D2A was developed as part of the Master's thesis 'Evaluating Reliability of Static Analysis Results Using Machine Learning' at BUT FIT.</p>
Dataset used to assess the vulnerability of chondrichthyan fishes of the Eastern Tropical Pacific to climate change
<p><span>Information and data on ETP chondrichthyan species distribution, life history, ecology, and physiology were gathered from the IUCN Red List, FishBase, published literature, unpublished data/grey literature, and expderts advice.</span></p>
ASE2021 vulnerability fix dataset
<p>The dataset of "Finding A Needle in a Haystack: Automated Mining of Silent Vulnerability Fixes", which was accepted in the 36th IEEE/ACM Automated Software Engineering (<em>ASE</em>) Conference.</p> <p>Followings are the descriptions of columns:</p> <ol> <li>commit_id: The commit ID/hash.</li> <li>repo: The Github Author and repository (e.g., "apache/hive").</li> <li>filename: The name of the file changed in the commit.</li> <li>partition: Which dataset the commit information belongs to (i.e., "train", "val", or "test").</li> <li>PL: Programming Language (PL) (i.e., "java" or "py").</li> <li>label: Label of the commit, 0 for non-vulnerability fixing commit and 1 for vulnerability fixing commit.</li> <li>diff: The entire code change information of the file in this commit.</li> <li>committer_date: The date of the commit (e.g., 2015-03-02 13:48:25+13:00)</li> <li>msg: The commit message (NA if empty).</li> <li>MOD_DIFF: The code change of the file in this commit after preprocessing: filtering out lines that are not added lines or removed lines, and removing refactoring information and comments.</li> <li>BPE_MOD_DIFF: BPE processing applied to MOD_DIFF information (using codeprep Python package).</li> <li>ADD_DIFF: The added lines from the MOD_DIFF information (indicated as a line starting with '+' character).</li> <li>REM_DIFF: The removed lines from the MOD_DIFF information (indicated as a line starting with '-' character).</li> <li>LOC_ADD: Total lines of code added in this file change.</li> <li>LOC_REM: Total lines of code removed in this file change.</li> <li>LOC_MOD: Total lines of code modified in this file change (LOC_ADD + LOC_REM).</li> <li>commit_repo: The commit ID and repository concatenated.</li> <li>cve_list: A list of CVEs which the commit fixes (e.g., CVE-2015-5348, CVE-2016-8902).</li> </ol> <p>Following is the code snippet to reproduce Table 1.</p> <pre><code class="language-python">import pandas as pd all_commits = pd.read_csv('./ase_dataset_sept_19_2021.csv') #Separate by language, since the Java commits are missing some info which we will add later on. py = all_commits[all_commits.PL == 'python'] java = all_commits[all_commits.PL == 'java'] #Java first: partition into train/val/test and check # of commits print("Java VF vs NVF for train/val/test") java_train = java[java.partition =="train"] java_val = java[java.partition == "val"] java_test = java[java.partition == "test"] print(java_train.drop_duplicates(subset='commit_id').label.value_counts()) print(java_val.drop_duplicates(subset='commit_id').label.value_counts()) print(java_test.drop_duplicates(subset='commit_id').label.value_counts()) #Python: partition into train/val/test and check # of commits print("Py VF vs NVF for train/val/test") py_train = py[py.partition =="train"] py_val = py[py.partition == "val"] py_test = py[py.partition == "test"] print(py_train.drop_duplicates(subset='commit_id').label.value_counts()) print(py_val.drop_duplicates(subset='commit_id').label.value_counts()) print(py_test.drop_duplicates(subset='commit_id').label.value_counts())</code></pre> <p> </p>
Replication Package for "A Characterization of Datasets for Vulnerability Prediction Studies"
<p>The present upload constitute the Replicability Package for the "A Characterization of Datasets for Vulnerability Prediction Studies" paper.</p>
Dataset for software vulnerability detection
<ol><li>raw_C.csv<ul><li>Raw (it may includes comments) source code in C.</li><li>Attributes:<ul><li>cwe_id</li><li>name: method name</li><li>repo_name: repository name</li><li>repo_url: repository url</li><li>old_path</li><li>file_change_id: used as a key in SQLite</li><li>method_change_id: used as a key in SQLite</li><li>code: source code</li><li>before_change: indicates whether the code is vulnerable (1) or not (0)</li></ul></li></ul></li><li>clean_C.csv<ul><li>Pre-processed (removed comments) source code in C.</li><li>Attributes (the same as raw_C.csv).</li></ul></li></ol>
FixFox: A Dataset on Mozilla Firefox Vulnerabilities and Fixes
Open the record for dataset details and reuse information.
FixFox: A Dataset on Mozilla Firefox Vulnerabilities and Fixes
Open the record for dataset details and reuse information.
ScienceDex guides
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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.