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1,580 results for “Vulnerability”
Characterisation of Social Vulnerability to the environmental hazard of heat in Logroño, and the surrounding La Rioja region in Spain, derived from national census and EU Copernicus datasets.
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for Logroño, and the surrounding La Rioja region, Spain. The input variables used in this dataset come from the national census data for Spain and EU Copernicus data.</p> <div> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p> </div>
Characterisation of Social Vulnerability to the environmental hazard of flooding in Cork City and County, Ireland, derived from national census and EU Copernicus datasets.
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for the region of Cork, Ireland. The input variables used in this dataset come from the national census data for Ireland and EU Copernicus data.</p> <div> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p> </div>
Characterisation of Social Vulnerability to the environmental hazard of heat in Milan, derived from national census and EU Copernicus datasets
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for the region of Milan, Italy. The input variables used in this dataset come from the national census data for Italy and EU Copernicus data.</p> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p>
A collection of datasets for software vulnerability detection
<p>This is a collection of datasets that are used for AI-based software vulnerability detection. All the datasets are in the .csv format and each row represents a sample. Each dataset includes a set of functions written in C and the target of each function is either 0 (non-vulnerable) or 1 (vulnerable).</p> <ol> <li><strong>data_C_Lin2017_test.csv:</strong> <ul> <li>Reference paper: <a href="https://dl.acm.org/doi/10.1145/3133956.3138840">Vulnerability Discovery with Function Representation Learning from Unlabeled Projects</a>, 2017.</li> <li>Data source on GitHub: <a href="https://github.com/DanielLin1986/function_representation_learning">https://github.com/DanielLin1986/function_representation_learning</a></li> <li>This dataset includes 44 vulnerable and 577 non-vulnerable functions from the LibPNG project.</li> </ul> </li> <li><strong>data_C_LineVul_test.csv:</strong> <ul> <li>Reference paper: <a href="https://ieeexplore.ieee.org/document/9796256">LineVul: A Transformer-based Line-Level Vulnerability Prediction</a>, 2022.</li> <li>Data source on Hugging Face: <a href="https://huggingface.co/datasets/Partha117/LineVul_Test_Dataset">https://huggingface.co/datasets/Partha117/LineVul_Test_Dataset</a></li> <li>This dataset includes 1055 vulnerable and 17809 non-vulnerable functions.</li> </ul> </li> <li><strong>data_C_PrimeVul_test.csv:</strong> <ul> <li>Reference paper: <a href="https://arxiv.org/abs/2403.18624">Vulnerability Detection with Code Language</a><br><a href="https://arxiv.org/abs/2403.18624">Models: How Far Are We?</a> 2024.</li> <li>Data source on GitHub: <a href="https://github.com/DLVulDet/PrimeVul">https://github.com/DLVulDet/PrimeVul</a></li> <li>From the data source, the primevul_test.jsonl was used to created this dataset.</li> <li>This dataset includes 695 vulnerable and 25213 non-vulnerable functions.</li> </ul> </li> <li><strong>data_C_Choi2017_test.csv:</strong> <ul> <li>Reference paper: <a href="https://www.ijcai.org/proceedings/2017/0214.pdf">End-to-End Prediction of Buffer Overruns from Raw Source Code</a><br><a href="https://www.ijcai.org/proceedings/2017/0214.pdf">via Neural Memory Networks</a>, 2017.</li> <li>Data source on GitHub: <a href="https://github.com/mjc92/buffer_overrun_memory_networks">https://github.com/mjc92/buffer_overrun_memory_networks</a></li> <li>From GitHub, all the data in trainnig_100.txt, test_1_100.txt, test_2_100.txt,test_3_100.txt,test_4_100.txt, and corresponding _labels.txt files are combined to create this dataset.</li> <li>This dataset includes 7054 vulnerable and 6946 non-vulnerable functions.</li> </ul> </li> <li><strong>data_C_Devign_test.csv:</strong> <ul> <li>Reference paper: <a href="https://proceedings.neurips.cc/paper_files/paper/2019/file/49265d2447bc3bbfe9e76306ce40a31f-Paper.pdf">Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks</a>, 2019</li> <li>Data source on Hugging Face: <a href="https://huggingface.co/datasets/claudios/code_x_glue_devign">https://huggingface.co/datasets/claudios/code_x_glue_devign</a></li> <li>From Hugging Face, all the data in train, validation, and test are combined to create this dataset.</li> <li>This dataset includes 12460 vulnerable and 14858 non-vulnerable functions.</li> </ul> </li> <li><strong>data_C_Ours_{train,test}.csv:</strong> <ul> <li>This dataset is manually collected from projects on GitHub that have registered CVEs into NVD from 2002 to 2023. The 6,766 non-vulnerable code functions are extracted from the <a href="https://dl.acm.org/doi/10.1145/3607199.3607242">DiverseVul dataset</a> to increase the code diversity. </li> <li>This training set includes 5413 vulnerable and 5413 non-vulnerable functions.</li> <li>The test set includes 1353 vulnerable and 1353 non-vulnerable functions.</li> </ul> </li> </ol>
Pan‐Arctic Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw
<p>The datasets are issued from the combination of records of the ESA EO4PAC and Permafrost_cci and HORIZON 2020 Nunataryuk projects. The EO4PAC project aimed to develop a new generation of geospatial products for the observation of permafrost and associated changes from space with a special focus on the coastal Arctic. Four components were considered in the creation of the datasets:</p> <p>(1) Landsat-7/8 for the detection of coastline changes over the 2000-2020 period (Tanguy et al., 2024).</p> <p>(2) Sentinel-1/2 for the detection and mapping of coastal infrastructures (Bartsch et al. 2024), updating Wang et al. (2021).</p> <p>(3) Permafrost_cci timeseries for retrieval of trends of ground temperature and active layer thickness for the 2000-2020 period (Obu et al. 2021a,b), evaluated based on Martin et al (2023) and CALM et al. (2024).</p> <p>(4) Sea level rise by 2100 (Garner et al. 2022).</p> <p>The respective output provides a consistent mapping of settlements along arctic and permafrost-dominated coasts (2), and associated coastline and permafrost conditions changes during the last 20 years (1, 3). Combined together, an assessment of Arctic infrastructures at risk due to permafrost change (GT, ALT) and coastline erosion was possible, the latter with projections for the years 2030, 2050 and 2100.<a name="_heading=h.jkogrw14ymt"></a></p> <p>References</p> <p>Bartsch, Annett, Pointner, Georg, & Nitze, Ingmar. (2023). Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) (Version 2) [Data set]. Zenodo. https://zenodo.org/records/10160636.</p> <p>CALM, GTN-P, Wieczorek, M., Heim, B., Streletskiy, D., Bartsch, A., 2024, GTN-P CALM: 34 years of Active Layer Thickness (ALT) across latitudinal and elevational gradients in the Northern Hemisphere [dataset]. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.972777</p> <p>Garner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., et al. (2022). IPCC AR6 sea level projections [Dataset]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6382554">https://doi.org/10.5281/zenodo.6382554</a></p> <p>Martin, Julia; Boike, Julia; Chadburn, Sarah; Zwieback, Simon; Anselm, Norbert; Goldau, Maybrit; Hammar, Jennika; Abramova, Ekatarina N; Lisovski, Simeon; Coulombe, Stéphanie; Dakin, Brampton; Wilcox, Evan James; Giamberini, Mariasilvia; Rader, Fieke; Suominen, Otso; Rudd, Daniel Alexander; Mastepanov, Mikhail; Young, Amanda (2023): T-MOSAiC 2021 myThaw data set [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.956039, In: Boike, Julia; Hammar, Jennika; Goldau, Maybrit; Miesner, Frederieke; Anselm, Norbert (2024): Circumarctic seasonal measurements of permafrost parameters (thaw depth, snow depth, vegetation and tree height, water level and soil properties) [dataset publication series]. PANGAEA, https://doi.org/10.1594/PANGAEA.971787</p> <p>Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. <a href="https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85">https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85</a></p> <p>Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. <a href="https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85">https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85</a></p> <p>Tanguy, R., Bartsch, A., Nitze, I., Irrgang, A., Petzold, P., Widhalm, B., von Baeckmann, C., Boike, J., Martin, J., Efimova, A., Vieira, G., Whalen, D., Heim, B., Wieszorek, M., Grosse, G.: Pan‐Arctic Assessment of Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw, Earth’s Future, 10.1029/2024EF005013.</p> <p>Wang, S., Ramage, J., Bartsch, A., & Efimova, A. (2021). Population in the Arctic Circumpolar Permafrost Region at settlement level (Version 2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4529610" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.4529610</a></p>
FixMe: An Incremental Lightweight Method for Vulnerability Data Collection for Security Patch Prediction
<div> <div>This repository has the FixMe dataset and the source code for extracting the new dataset. is a lightweight approach for collecting code patches based on analyzing the commits of various version control systems. The practical framework is designed to generate patches across a wide array of programming languages. This open-source tool streamlines the process of gathering vulnerability records from the Common Vulnerabilities and Exposures (CVE) database through an incremental approach. By embracing an incremental methodology, we expedite the acquisition of data, ensuring the inclusion of newly identified vulnerabilities and their corresponding patch pairs. Our methodology involves extracting security issues, obtaining vulnerability-fixing commits, and retrieving relevant source code from various projects. The extracted dataset by the FixMe tool supports for the automated patch prediction, automated program repair, commit classification, vulnerability prediction and more.</div> </div>
Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction [dataset]
<p>This dataset contains the extension of a publicly available dataset that was published initially by Ferenc et al. in their paper:</p> <p><em>“Ferenc, R.; Hegedus, P.; Gyimesi, P.; Antal, G.; Bán, D.; Gyimóthy, T. Challenging machine learning algorithms in predicting vulnerable javascript functions. 2019 IEEE/ACM 7th InternationalWorkshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE). IEEE, 2019, pp. 8–14.”</em></p> <p>The dataset contained software metrics for source code functions written in JavaScript (JS) programming language. Each function was labeled as vulnerable or clean. The authors gathered vulnerabilities from publicly available vulnerability databases.</p> <p>In our paper entitled: “<strong>Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction</strong>” and cited as:</p> <p><em>“Kalouptsoglou I, Siavvas M, Kehagias D, Chatzigeorgiou A, Ampatzoglou A. Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction. Entropy. 2022; 24(5):651. <a href="https://doi.org/10.3390/e24050651">https://doi.org/10.3390/e24050651</a>”</em></p> <p>, we presented an extended version of the dataset by extracting textual features for the labeled JS functions. In particular, we got the dataset provided by Ferenc et al. in CSV format and then we gathered all the GitHub URLs of the dataset's functions (i.e., methods). Using these URLs, we collected the source code of the corresponding JS files from GitHub. Subsequently, by utilizing the start and end line information for every function, we cut off the code of the functions. Each function was then tokenized to construct a list of tokens per function.</p> <p>To extract text features, we used a text mining technique called sequences of tokens. As a result, we created a repository with all methods' source code, the token sequences of each method, and their labels. To boost the generalizability of type-specific tokens, all comments were eliminated, as well as all integers and strings, which were replaced with two unique IDs.</p> <p>The dataset contains 12,106 JavaScript functions, from which 1,493 are considered vulnerable.</p> <p>This dataset was created and utilized during the Vulnerability Prediction Task of the Horizon2020 IoTAC Project as training and evaluation data for the construction of vulnerability prediction models. The dataset is provided in the csv format. Each row of the csv file has the following parts:</p> <ul> <li>Label: Flag with values ‘1’ for vulnerable and ‘0’ for non-vulnerable methods</li> <li>Name: The name of the JavaScript method</li> <li>Longname: The longname of the JavaScript method</li> <li>Path: The path of the file of the method in the repository</li> <li>Full_repo_path: The GitHub URL of the file of the method</li> <li>TokenX: Each next row corresponds to each token included in the method</li> </ul>
Spatial Variability in Marsh Vulnerability and Coastal Forest Loss in Chesapeake Bay
Sea level rise (SLR) and saltwater intrusion are driving shifts in coastal ecosystems that must migrate to survive. Marsh migration into adjacent uplands is a primary mechanism for sustaining coastal marshes, but potentially limited by natural and anthropogenic barriers. In this study, we focus on the Chesapeake Bay as a case study and combine previous delineations of the marsh-forest boundary and high-resolution topobathymetric data with sea level rise predictions to uniquely assess marsh migration potential on the scale of U.S. Geological Survey HUC10 watersheds. Combining these predictions results in a high-resolution Chesapeake Bay-wide assessment of marsh migration potential through the end of the century. Additionally, we analyze high-resolution land use data within the potential migration area to assess what ecosystems are at risk of loss to marsh via salinization and what potential anthropogenic features exist in the marsh migration corridor. The data consists of 3 files created from analyses conducted during the study: 1) A table summarizing characteristics of the study sites, including elevation and land use, and 2) A zipped Shapefile containing the boundaries of the HUC10 watersheds, 3) A zipped raster (CB_MarshMigrationArea.tif) of elevation categories. Cell values indicate: 1 = area below threshold elevation 2 = area between threshold elevation and 0.5 m of SLR. 3 = area between 0.5 and 1 m of SLR. 4 = area between 1 and 1.5 m of SLR. 5 = area between 1.5 and 2 m of SLR. 6 = area between 2 and 2.5 m of SLR. 7 = area above 2.5 m of SLR. Additionally, uploaded are 10 additional files containing the exact copies of the publicly available data we analyzed to create the above files. To obtain these files from their original sources (i.e. USGS, NOAA, etc) please see the links provided in the Metadata-LO-Letters-dat-V3.rtf file. 1) Points at the marsh-forest boundary 2) Chesapeake Conservancy High-Resolution Land Use 3) Chesapeake Conservancy High-Resolution L
Multiscale Social Vulnerability in U.S.
<p>Social vulnerability indices created using the SoVI (2016) recipe for the U.S. using multiple data resolutions and index construction extents for 2018-2021. Indices were created in R using an automated construction script that can be found at <a href="https://github.com/katesnelson/MultiscalarSVI">https://github.com/katesnelson/MultiscalarSVI</a>. </p> <p>The data and accompanying manuscript are published at </p> <p>Nelson, K. S. (2025). Where scale matters for social vulnerability indices: a multiyear analysis of the US. <em>International Journal of Disaster Risk Reduction</em>, 105513. <a href="https://doi.org/10.1016/j.ijdrr.2025.105513">https://doi.org/10.1016/j.ijdrr.2025.105513</a></p>
Food fraud vulnerability assessment data (on spice/ginger and wine)
<p>The dataset includes the results of food fraud vulnerability assessments (on spice/ginger and wine) of various companies based in China and Europe. The data form part of WP3 (Task 3.2): <em>Implementation of innovations in food authenticity. </em>The data is generated to better understand the food fraud vulnerability within selected food chains. The data is useful for anyone working in the field of food authentication.</p>
Evidence of a Vulnerable Marine Ecosystem documented via tourist submarine off Cape Well-Met, Vega Island, Eastern Antarctic Peninsula (Subarea 48.1) - Multimedia
<p>Video evidence of a Vulnerable Marine Ecosystem (VME) was collected via submarine deployed by the tourist super-yacht MY Scenic Eclipse flagged with Malta. The dive was conducted on the 29th November 2019 within Subarea 48.1. The video of this resource supplements the dataset "Evidence of a Vulnerable Marine Ecosystem documented via tourist submarine off Cape Well-Met, Vega Island, Eastern Antarctic Peninsula (Subarea 48.1) - data'' available at <a href="https://ipt.biodiversity.aq/resource?r=cape-well-met_2019">https://ipt.biodiversity.aq/resource?r=cape-well-met_2019</a>.</p> <p>Method step description:</p> <ol> <li> <p>Video evidence of a Vulnerable Marine Ecosystem (VME) was collected via submarine deployed by the tourist super-yacht MY Scenic Eclipse flagged with Malta. Recordings begin at the greatest depth and continue as the submarine travels up the wall. Footage was taken with a GoPro Hero 7 Black mounted in the pilot window of a U-Boat Worx Cruise Sub 7-300<a href="https://www.uboatworx.com/model/cruisesub"> (https://www.uboatworx.com/model/cruisesub).</a> Four submarine dives were filmed.</p> </li> <li> <p>Prior to footage clean-up it was decided that the longest resulting video would be the one that would be analysed. Footage of each of these dives were provided in multiple files.</p> </li> <li> <p>Final Cut Pro X was first used to join the files into one video file per dive.</p> </li> <li> <p>The videos were then cropped to remove the edge of the pilot’s window frame and to adjust the colour balance.</p> </li> <li> <p>Clean-up then followed the same methodology as was used for analyzing the submarine footage for the successful nomination of four VMEs in WG-EMM-18/35 to remove unusable sequences. For the Cape Well-Met footage that meant the removal of any sequences where the submarine was too far from the wall, where the visibility was poor and when the submarine was paused.</p> </li> <li> <p>Footage from Dive C was the longest resulting video after the completion of this clean-up procedure, thus it became the footage that was analysed.</p> </li> </ol> <p>This project is funded by The Soap and The Sea, a Swiss organic and ocean-friendly soap enterprise that donates half of its profits to Ocean Conservation initiatives.</p>
Vulnerable Marine Ecosystem Indicator Taxa recorded by submarine as evidence of the presence of Vulnerable Marine Ecosystems, Antarctic Peninsula - images
<p>This resource contains images that are framegrabs from video recorded by submarine deployed by the MY Arctic Sunrise during their Antarctica expeditions. The first took place in 2018 and focused within the Gerlache Strait and along the western Antarctic Peninsula and the Antarctic Sound in January 2018. Dives were conducted beginning 19th to 27th January 2018. This resource supplement the images for “Vulnerable Marine Ecosystem Indicator Taxa recorded by submarine as evidence of the presence of Vulnerable Marine Ecosystems, Antarctic Peninsula - data”</p>
Output files corresponding to "Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States"
<p>This dataset corresponds to the output files that were produced for the study reported in:</p> <p>Knights, Deon, Kevin C. Parks, Audrey H. Sawyer, Cédric H. David, Trevor N. Browning, Kelsey M. Danner, and Corey D. Wallace, (2017), Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States, <em>Journal of Hydrology,</em> 554, 331-341</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php. Region used is: Great Lakes (04)</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS. Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p> </p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>Flowlines</em>: This folder contains a shapefile (<em>GL_coastcatchment_NHDflowline</em>) with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in the region used. </li> <li><em>Catchment</em>: This folder contains a shapefile (GL_coastcatchment_polygon) with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in the region used. </li> <li><em>Centroid</em>: This folder contains a shapefile (GL_coastcatchment_centroid) with the centroids of the above catchments. </li> <li><em>DischargeVulnerabilities.csv</em>. This .csv file contains the following data (units are in parentheses): <ul> <li>COMID: Unique feature identifier in NHDPlusV2 ().</li> <li>Length_km: Length of coastline feature (km).</li> <li>Area_sqkm: Area of coastal catchment feature (km<sup>2</sup>).</li> <li>Infiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>REACHCODE: Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature ().</li> <li>RLength_km: Total length of coastline accumulated by REACHCODE (km).</li> <li>RArea_sqkm: Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>RInfiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>DGWD: Average annual direct groundwater discharge for REACHCODE (m<sup>2</sup>/y).</li> <li>Vulnerable_percent: Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>Vulnerable: Vulnerability to coastal contamination (0- not vulnerable; 1-vulnerable)</li> </ul> </li> </ul> <p> </p>
Assessment of vulnerability to climate change of coastal communities in the Gulf of California and the Yucatan Peninsula: vulnerability outputs
<p>The dataset includes the outputs of the project: "Assessment of vulnerability to climate change of coastal communities in the Gulf of California and the Yucatan Peninsula: vulnerability outputs" funded by the David and Lucille Packard Foundation and awarded to H. Reyes-Bonilla (UABCS). </p> <p>This study analyzed vulnerability of fisheries-dependent coastal communities based on three components: a) adaptive capacity (84 indicators), which reflect the ability of a community to respond and recover after adverse events; b) susceptibility (11 indicators) which was determined based on fishing dependence; and c) exposure (31 indicators) that was evaluated with current environmental data. Future vulnerability was determined for a 2050 horizon and based on two climate change scenarios: SSP126, which represents low emissions, and SSP585, which takes into consideration that the amount of greenhouse gases will continue to increase. These data come from the Coupled Model Intercomparison Project 6 (CMIP6), which serves as the basis for the 6th IPCC report. We evaluated vulnerability using indicators what were available at the local scale.</p>
Software vulnerability detection datasets - function/method level
<p>This dataset is for software vulnerability detection and includes source code in eight programming languages (C, C++, Java, JavaScript, Go, PHP, Ruby, Python). All data is collected from GitHub.</p><p>data<i>{programming language}_vul.json: a set of vulnerable code samples in a certain programming language.</i></p><p>data<i>{programming language}_patch.json: a set of patching code samples in a certain programming language.</i></p><p> </p><p>Each source code sample includes the following 16 properties: </p><p><strong>index</strong>: index of code. If is_vulnerable==False, this index indicates that this code is a patch of the indexing vulnerable code.</p><p><strong>code</strong>: raw source code (may include comments).</p><p><strong>is_vulnerable</strong>: the code is vulnerable (<strong>True</strong>) or a patch (<strong>False</strong>).</p><p><strong>programming_language</strong>: programming language of the code.</p><p><strong>method_name</strong>: name of the method.</p><p><strong>file_name</strong>: name of the file where the source code is extracted.</p><p><strong>repo_url</strong>: url of the project repository.</p><p><strong>repo_owner</strong>: owner of the repository.</p><p><strong>committer</strong>: developer who pushed the commit.</p><p><strong>committer_date</strong>: date when the commit was pushed.</p><p><strong>commit_msg</strong>: the commit message.</p><p><strong>cwe_id</strong>: If is_vulnerable==True, the CWE id; otherwise None.</p><p><strong>cwe_name</strong>: If is_vulnerable==True, the name of corresponding CWE; otherwise None.</p><p><strong>cwe_description</strong>: If is_vulnerable==True, the description of corresponding CWE; otherwise None.</p><p><strong>cwe_url</strong>: If is_vulnerable==True, the url to obtain more details of corresponding CWE; otherwise None.</p><p><strong>cve_id</strong>: If is_vulnerable==True, the CVE id; otherwise None.</p>
Regional Heat Vulnerability Map and Cooling Solutions: A webtool of the Healthy Urban Environments Initiative
## Regional Heat Vulnerability Map and Cooling Solutions The regional heat vulnerability map and cooling solutions webtool offers two data sources for equitable heat mitigation. The dashboard layers vulnerability data onto land surface temperature regional rankings to identify areas with high and low heat exposure and vulnerability as well as the existing assets in each census block group. Additional layers can be added into the heat vulnerability map to highlight how heat affects critical infrastructures including schools, mobile home parks, parking lots, public transportation stops, pedestrian thoroughfares, and bikeways. The solutions tab showcases a variety of heat mitigation solutions and the research behind them. Heat-related solutions and resources from urban Maricopa County are included, including solutions funded through the Healthy Urban Environment Initiative. The data catalogued here are the underlying data that populate the webtool. ## Healthy Urban Environment (HUE) Initiative - Overview HUE is a solutions-focused research, policy and technology incubator to create healthier communities across Maricopa County (central Arizona, USA) through collaboration between researchers, practitioners and community members. As such, HUE funded rapid development, testing and deployment of heat-mitigation and air-quality improvement strategies and technologies. Heat emerged as the urgent focus, as urban centers across the desert Southwest continue to grow in size and density, aggravating existing challenges posed by the expansion of the built environment. In Phoenix, AZ, this expansion of the built environment creates conditions which magnify the intensity and duration of heat – making it difficult for residents to achieve thermal comfort throughout the day and night. Further, the legacies of urban sprawl and transportation planning in the Phoenix, Arizona metropolitan area have contributed to challenges with atmospheric pollutants. Importantly, urban heat and air qua
Supplementary data to: Importance and vulnerability of the world's water towers
<p>This archive contains data produced for a study assessing the importance and vulnerability of the world’s water towers. Code (R-scripts) used to process these files is available on the <a href="https://github.com/mountainhydrology/pub_ngs-watertowers">MountainHydrology Github page</a></p> <p>The archive is organized in directories with specific topics. Each directory contains input files (optional) and output/processed files. The input files can be used in combination with the R-scripts published on <a href="https://github.com/mountainhydrology/pub_ngs-watertowers">Github</a> to generate the processed files included in this archive. In many cases external published data is used as input data for the calculations. In that case the data is not included in this archive but literature references and links to the specific files are provided in the description below. Files which have been preprocessed before use in the R-scripts are included in this archive. For calculation details please see the publication, in particular Extended Data Tables 3 and 4.</p> <p><strong>Archive contents</strong></p> <p>The archives contents are organized in eight separate directories, which are listed here, along with their contents:</p> <ul> <li><strong>ERA5</strong></li> </ul> <p>Precipitation and evaporation data are extracted from ERA5 reanalysis available online in the Copernicus Climate Data Store at https://cds.climate.copernicus.eu</p> <p>This directory includes:</p> <p><em>Input</em></p> <pre><code>ERA5_evaporation_avgannual_2001_2017.nc - Average annual evaporation (mm) for 2001-2017 ERA5_evaporation_ymonmean_2001_2017.nc - Multi-year mean monthly evaporation (mm) for 2001-2017 era5_total-precipitation_ymonmean_2001-2017_global.tif - Multi-year mean monthly precipitation (mm) for 2001-2017 era5_total-precipitation_yearsum_2001-2017.tif - Average annual precipitation (mm) for 2001-2017</code></pre> <p><em>Output</em></p> <pre><code>P_avg_annual_basin_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to basins P_avg_annual_DS_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to downstream basins P_avg_annual_mm.tif - Average annual precipitation 2001-2017 (mm) P_avg_annual_WT_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to Water Tower Units P_var_interannual.tif - Interannual variablity in precipitation 2001-2017 P_var_interannual_basin.tif - Interannual variablity in precipitation 2001-2017 aggregated to basins P_var_interannual_DS.tif - Interannual variablity in precipitation 2001-2017 aggregated to downstream basins P_var_interannual_WT.tif - Interannual variablity in precipitation 2001-2017 aggregated to Water Tower Units P_var_intraannual.tif - Intra-annual variablity in precipitation 2001-2017 P_var_intraannual_basin.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to basins P_var_intraannual_DS.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to downstream basins P_var_intraannual_WT.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to Water Tower Units WTU_P_indicators.csv - Table listing all calculated precipition indicators per Water Tower Unit</code></pre> <ul> <li><strong>Glaciers</strong></li> </ul> <p>Glacier volume and mass balance are derived from published datasets. This directory includes:</p> <p><em>Output</em></p> <pre><code>Glac_area_WT_km2.tif - Glacier area (km2) aggregated for Water Tower Units Glac_volume_WT_km3.tif - Glacier volume (km3) aggregated for Water Tower Units WTU_Glacier_indicators.csv - Table listing all derived glacier indicators per Water Tower Unit WTU_MB.shp - shapefile of Water Tower Units including the glacier mass balance per Water Tower Units as attribute</code></pre> <p><em>External data</em></p> <p>Glacier volume data published in<em> Farinotti et al., 2019, Nature Geoscience</em>, were used.<br> Reference: Farinotti, D. et al. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nat. Geosci. 12, 168–173 (2019).<br> Glacier volume (km3) and glacier area (km2) at 0.05 degrees spatial resolution were used, which are available <a href="https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/315707/global_fraction-of-degree_grids.zip?sequence=60&isAllowed=y">here</a>.<br> The used files are <em>p05_degree_glacier_area_km2.tif</em> and <em>p05_degree_glacier_volume_km3.tif</em></p> <p>Glacier mass balance data published by the World Glacier Monitoring Service were used to derive an average glacier mass balance per Water Tower Unit.<br> References:<br> Zemp, M. et al. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Nature 568, 382–386 (2019).<br> World Glacier Monitoring Service. Fluctuations of Glaciers (FoG) Database. (2018). doi:10.5904/wgms-fog-2018-06</p> <ul> <li><strong>HydroLAKES</strong></li> </ul> <p>Surface lake and water storage per Water Tower Unit was calculated. This directory includes:</p> <p><em>Output</em></p> <pre><code>WTU_lake_storage_volume.csv - Table listing lake and reservoir volume (km3) per Water Tower Unit WTU_surface_water_storage_km3.tif - Lake and reservoir storage volume (km3) aggregated to Water Tower Units</code></pre> <p><em>External data</em></p> <p>For surface water lakes and reservoirs the HydroLAKES dataset is used. The shapefile <em>HydroLAKES_polys_v10.shp</em> can be downloaded from <a href="http://https://97dc600d3ccc765f840c-d5a4231de41cd7a15e06ac00b0bcc552.ssl.cf5.rackcdn.com/HydroLAKES_polys_v10_shp.zip">HydroSheds</a></p> <p>Reference: Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 1–11 (2016).</p> <ul> <li><strong>Indicators</strong></li> </ul> <p>All indicators and subindicators calculated for the Water Tower Index calculation are stored per Water Tower Unit.</p> <p>This directory includes:</p> <pre><code>indicators.csv - Table with all indicators and subindicators per Water Tower Unit</code></pre> <ul> <li><strong>Snow</strong></li> </ul> <p>The MODIS MOD10CM006 snow cover product was used to derive snow persistence.<br> Reference: Hall, D. K. & Riggs, G. A. MODIS/Terra Snow Cover Monthly L3 Global 0.05Deg CMG, Version 6. (2015). doi:10.5067/MODIS/MOD10CM.006</p> <p>This archive includes:<br> <em>Input</em></p> <pre><code>MOD10CM006_yearmean_2001-2017.tif - Annual mean snow cover 2001-2017 MOD10CM006_ymonmean_2001-2017.tif - Multi-year mean monthly snow cover 2001-2017</code></pre> <p><em>Output</em></p> <pre><code>Snow_persistence_avg_annual.tif - Average annual snow persistence 2001-2017 Snow_persistence_avg_annual_WT.tif - Average annual snow persistence 2001-2017 aggregated to Water Tower Units Snow_persistence_var_interannual.tif - Interannaul variability in snow persistence 2001-2017 Snow_persistence_var_interannual_WT.tif - Interannaul variability in snow persistence 2001-2017 aggregated to Water Tower Units Snow_persistence_var_intraannual.tif - Intra-annaul variability in snow persistence 2001-2017 Snow_persistence_var_intraannual_WT.tif - Intra-annaul variability in snow persistence 2001-2017 aggregated to Water Tower Units WTU_Snow_indicators.csv - Table listing all derived snow indicators per Water Tower Unit</code></pre> <ul> <li><strong>Uncertainty</strong></li> </ul> <p>The directory contains the uncertainty ranges used in the uncertainty analysis<br> The directory includes:</p> <pre><code>ET_uncertainty_per_downstream.csv - Table listing SD in evaporation per downstream basin ET_uncertainty_per_WTU.csv - Table listing SD in evaporation per Water Tower Unit P_uncertainty_per_downstream.csv - Table listing SD in precipitation per downstream basin P_uncertainty_per_WTU.csv - Table listing SD in precipitation per Water Tower Unit WTU_IceVol_uncertainty.csv - Table listing uncertainty in ice volume per Water Tower Unit</code></pre> <ul> <li><strong>Water demands</strong></li> </ul> <p>Net water demands for irrigation, industrial and domestic water use, as well as the environmental flow requirement are extracted from PCR-GLOBWB hydrological model output.<br> Reference: Wada, Y., De Graaf, I. E. M. & van Beek, L. P. H. High-resolution modeling of human and climate impacts on global water resources. J. Adv. Model. Earth Syst. 8, 735–763 (2016).</p> <p>The directory includes:<br> <em>Input</em></p> <pre><code>Dom_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net domestic water demand 2001-2014 at 0.05 degrees resolution (km3) Ind_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net industrial water demand 2001-2014 at 0.05 degrees resolution (km3) Irr_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net irrigation water demand 2001-2014 at 0.05 degrees resolution (km3) Tot_use_ymonmean_2001_2014_005.tif - Sum of the three above global_historical_riverdischarge_ymonmean_m3second_5min_2001_2014.nc4 - Multi-year mean monthly natural discharge (m3/s) 2001-2014</code></pre> <p><em>Output</em></p> <pre><code>Domestic_use_avg_annual_basin_km3.tif - Average annual net domestic water demand 2001-2014 aggregated to basins Domestic_use_avg_annual_km3.tif - Average annual net domestic water demand 2001-2014 Industrial_use_avg_annual_basin_km3.tif - Average annual net industrial water demand 2001-2014 aggregated to basins Industrial_use_avg_annual_km3.tif - Average annual net industrial water demand 2001-2014 Irrigation_use_avg_annual_basin_km3.tif - Average annual net irrigation water demand 2001-2014 aggregated to basins Irrigation_use_avg_annual_km3.tif - Average annual net irrigation water demand 2001-2014 Natural_demand_avg_annual_basin_km3.tif - Average annual natural water demand 2001-2014 aggregated to basins Total_human_demand_avg_annual_basin_km3.tif - Average annual net human (sum of domestic, industrial and irrigation) water demand 2001-2014 aggregated to basins Water_gap_average_annual_basin.tif - Average annual water gap 2001-2014 aggregated to basins WTU_Demand_DS_P_available.csv - Table listing dowstream water availability per sector per basin WTU_Demand_indicators.csv - Table listing demand per sector per basin WTU_Domestic_Water_Gap_monthly.csv - Table listing multi-year average monthly domestic water gap per basin WTU_Industrial_Water_Gap_monthly.csv - Table listing multi-year average monthly industrial water gap per basin WTU_Irrigation_Water_Gap_monthly.csv - Table listing multi-year average monthly irrigation water gap per basin WTU_Natural_Water_Gap_monthly.csv - Table listing multi-year average monthly natural water gap per basin WTU_Total_Water_Gap_monthly.csv - Table listing multi-year average monthly water gap per basin</code></pre> <ul> <li><strong>WTU units</strong></li> </ul> <p>The spatial units for all calculations are the Water Tower Units, their downstream basins, and the entire basins (Water Tower Unit + downstream basin). They are extracted using definitions of basins and mountain ranges. This directory includes:</p> <p><em>Output</em></p> <pre><code>basins.tif - Definition of basins with Water Tower Units at 0.05 degrees spatial resolution basins_downstream.tif - Definition of downstream basins at 0.05 degrees spatial resolution basins_vector.shp - Definition of basins with Water Tower Units as vector data downstream_vector.shp - Definition of downstream basins as vector data gmba_all.shp - All GMBA mountain ranges including glacier volume and snow persistence gmba_ss.shp - GMBA mountain ranges included in Water Tower Units WTU.tif - Definition of Water Tower Units as 0.05 degrees spatial resolution WTU_specs.csv - Table with set of specifications of Water Tower Units WTU_vector.shp - Definition of Water Tower Units as vector data</code></pre> <p><em>External data</em></p> <p>FAO's classification of major hydrological basins and FAO's classification of subbasins per continent are used. These are based on HydroSheds and are available as shapefiles at <a href="http://www.fao.org/nr/water/aquamaps/">FAO Aquamaps</a></p> <p>The specific shapefiles used are:</p> <p><a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=38047&fname=Major_hydrological_basins.zip&access=private">major_hydrobasins.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37039&fname=hydrobasins_asia.zip&access=private">hydrobasins_asia.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37174&fname=hydrobasins_southam.zip&access=private">hydrobasins_southam.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=38044&fname=hydrobasins_northam.zip&access=private">hydrobasins_northam.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37299&fname=hydrobasins_neareast.zip&access=private">hydrobasins_neareast.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37250&fname=hydrobasins_europe.zip&access=private">hydrobasins_europe.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37173&fname=hydrobasins_centralam.zip&access=private">hydrobasins_centralam.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37251&fname=hydrobasins_austpacific.zip&access=private">hydrobasins_austpacific.shp</a>:</p>
Output files corresponding to "Continental patterns of submarine groundwater discharge reveal coastal vulnerabilities"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to the output files that were produced for the study reported in:</p> <ul> <li>Sawyer, Audrey H., Cédric H. David, and James S. Famiglietti, (2016), Continental patterns of submarine groundwater discharge reveal coastal vulnerabilities, Science, 353(6300), 705-707. DOI:10.1126/science.aag1058. </li> </ul> <p> </p> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p> </p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php. Regions used are: Northeast (NE: 01), Mid-Atlantic (MA: 02), South-Atlantic North (SAN: 03N), South-Atlantic South (SAS: 03S), South-Atlantic West (SAW: 03W), Lower Mississippi (MS: 08), Texas (TX: 12), California (CA: 18), and Pacific Northwest (NW: 17).</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS. Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2010 Census dataset (CENSUS 2010), obtained from: http://www2.census.gov/geo/tiger/TIGER2010DP1/County_2010Census_DP1.zip.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p> </p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>NHDFlowline_CONUS_coastline.zip. </em>This zip file contains a shapefile with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in regions used. </li> <li><em>Catchment_CONUS_coastline.zip. </em>This zip file contains a shapefile with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in regions used. </li> <li><em>Catchment_CONUS_coastline_centroid.zip</em>.<em> </em>This zip file contains a shapefile with the centroids of the above catchments. </li> <li><em>SGD_Coastal_Vulnerabilities.csv</em>. This .csv file contains the following data (units are in parentheses): <ul> <li>COMID. Unique feature identifier in NHDPlusV2 (-).</li> <li>LENGTHkm. Length of coastline feature (km).</li> <li>REACHCODE. Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature (-).</li> <li>AREAsqkm. Area of coastal catchment feature (km<sup>2</sup>).</li> <li>REGION. NHDPlusV2 region: NE = Northeast, MA = Mid-Atlantic, SAN = South Atlantic North, SAS = South Atlantic South, SAW = South Atlantic West, TX = Texas, MS = Lower Mississippi, CA = California, PN = Pacific Northwest (-).</li> <li>RLENGTHkm. Total length of coastline accumulated by REACHCODE (km).</li> <li>RAREAsqkm. Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>BGRUNkgpsqm. Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>SGDsqmpy. Average annual fresh SGD rate for REACHCODE (m<sup>2</sup>/y).</li> <li>RCOUNT. Number of features by REACHCODE (-).</li> <li>PDENpsqkm. Population density for coastal catchment feature (km<sup>-2</sup>).</li> <li>SWIVULN. Vulnerability to saltwater intrusion: - 1 = vulnerable, 0 = not vulnerable (-).</li> <li>PCTDEV11. Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>CONTVULN. Vulnerability to offshore contamination associated with direct groundwater discharge: - 1 = vulnerable, 0 = not vulnerable (-).</li> </ul> </li> </ul> <p> </p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>No bugs have been unveiled since publication of this dataset or the associated manuscript. Vulnerability thresholds are subjective and could be adjusted for different applications, refer to published manuscript for approaches used here.</p> <p> </p> <p><strong>Funding</strong></p> <p>This work was supported by the Ohio State University School of Earth Sciences, and NSF grant EAR-1446724 (A.H.S); the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA, and grants from the NASA SWOT and Sea Level Science Teams (C.H.D. and J.S.F.).</p>
Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches (Replication Package Part 3: OpenSSL dataset)
<h1><strong>The Replication Package of</strong></h1> <h1><strong>"Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches"</strong></h1> <h3><strong>Part 3 (OPENSSL Dataset)</strong></h3> <div> <div>This repository includes:</div> <ol> <li><em><strong>Code.zip</strong></em> that contains the codes to replicate some parts of this study:<br>a. <em>1_generate_datasets</em> implements our methodology to generate the datasets.<br>b. <em>2_run_models</em> runs the ML models during the evaluation.<br>c. <em>3_result_replication </em>generates charts presented in the paper from the ML evaluation results.</li> <li><em><strong>Datasets.zip</strong></em> that contain 2 folders:<br>a. <em>original</em> datasets: 1 from <a href="https://github.com/CGCL-codes/VulDeePecker" target="_blank" rel="noopener">NVD Vuldeepecker</a> and 3 extracted from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a>.<br> <div> <div>b. <em>OPENSSL</em> datasets: train, validation, test sets for each time of observation extracted using our methodology from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a> dataset for project <em>openssl</em>.</div> </div> </li> <li><em><strong>Pretrained-models.zip</strong></em> that we generated during our evaluation (3 test results for each time point in the timeline [2013-2019]).</li> <li><em><strong>Results.zip</strong></em> of our evaluation, the folder <em>ALL</em> contains the overall results and other folders are results by model.</li> </ol> <p><strong>UPDATED version 5<br></strong>- added a GLOBAL_README.md which contains the 3 stages and how they are connected to each other<br>- updated LineVul.ipynb: import AdamW from torch.optim instead of transformers<br>- updated README.md in Code2Vec with the prerequisites of Java to run gradlew for astmine</p> <p><strong>UPDATED version 6<br></strong>- updated CodeBert.ipynb: import AdamW from torch.optim instead of transformers</p> <p>Documentations</p> <ol> <li><em><strong>INSTALL.pdf </strong></em>: how to install the codes</li> <li><em><strong>README.pdf</strong></em>: readme file</li> <li><em><strong>REQUIREMENTS.pdf</strong></em>: hardware and software requirements</li> <li><em><strong>STATUS.pdf</strong></em> : status for artifact submission</li> <li><em><strong>LICENSE.pdf</strong></em>: the license of this artifact</li> <li><em><strong>PAPER.pdf</strong></em>: the camera-ready version of the paper</li> </ol> </div> <div> <div>Please refer to the following repositories for the other datasets and pre-trained models:</div> <div>- Part 1 NVD Vuldeeepecker : <a href="https://doi.org/10.5281/zenodo.8207883" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8207883</a></div> - Part 2 LINUX : <a href="https://doi.org/10.5281/zenodo.10960662" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10960662</a><br> <div>- Part 4 POPPLER : <a href="https://doi.org/10.5281/zenodo.14713143">https://doi.org/10.5281/zenodo.14713143</a></div> <div> </div> <div>This work was partly funded by the EU under the H2020 Program AssureMOSS (Grant n. 952647) and the Horizon Europe Program Sec4AI4Sec (Grant n. 101120393), by the Italian Ministry of University and Research (MUR) under the P.N.R.R. – NextGenerationEU grant n.\ PE00000014 (SERICS subproject COVERT), and by the Dutch Research Council (NWO) under the grant NWA.1215.18.006 (Theseus) and grant KIC1.VE01.20.004 (HEWSTI). </div> </div>
European Building Vulnerability Data Repository
<p>A repository for the European vulnerability database developed as part of the European Seismic Risk Model 2020 (ESRM20).</p> <p>More information available in the following paper: Crowley et al. (2021) “Open models and software for assessing the vulnerability of the European building stock,” COMPDYN 2021, 8th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Greece.</p>
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