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1,608 results for “Fixed”
Dataset: Dimensional Global Core Plus Fixed Income ETF (DFGP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Abacus Life, Inc. 9.875% Fixed Rate Senior Notes due 2028 (ABLLL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Wisdomtree Bianco Fixed Income Total Return Fund (WTBN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Brandywineglobal-U.S. Fixed Income ETF (USFI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: TPG Operating Group II, L.P. 6.950% Fixed-Rate Junior Subordinated Notes due 2064 (TPGXL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Stitch Fix, Inc. (SFIX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Perma-Fix Environmental Services, Inc. (PESI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: NewtekOne, Inc. 8.00% Fixed Rate Senior Notes due 2028 (NEWTI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: NewtekOne, Inc. 8.50% Fixed Rate Senior Notes due 2029 (NEWTG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Figure 2 in Nitrogen-fixing Cyanothece sp. as a mixotroph and silver nanoparticle synthesizer: a multitasking exceptional cyanobacterium
Figure 2. Disc inhibition zone against MRSA Staphylococcus aureus using (a) gold nanoparticles, (b) silver nanoparticles, and (c) both silver nanoparticles and gold nanoparticles.
CVEfixes Dataset: Automatically Collected Vulnerabilities and Their Fixes from Open-Source Software
<p><em>CVEfixes</em> is a comprehensive vulnerability dataset that is automatically collected and curated from Common Vulnerabilities and Exposures (CVE) records in the public <a href="https://nvd.nist.gov/">U.S. National Vulnerability Database (NVD)</a>. The goal is to support data-driven security research based on source code and source code metrics related to fixes for CVEs in the NVD by providing detailed information at different interlinked levels of abstraction, such as the commit-, file-, and method level, as well as the repository- and CVE level.</p> <p>This release, v1.0.8, covers all published CVEs up to 23 July 2024. All open-source projects that were reported in CVE records in the NVD in this time frame _and_ had publicly available git repositories were fetched and considered for the construction of this vulnerability dataset. The dataset is organized as a relational database and covers 12107 vulnerability fixing commits in 4249 open source projects for a total of 11873 CVEs in 272 different Common Weakness Enumeration (CWE) types. The dataset includes the source code before and after changing 51342 files and 138974 functions. The collection took 48 hours with 4 workers (AMD EPYC Genoa-X 9684X).</p> <p>This repository includes the SQL dump of the dataset, as well as the JSON for the CVEs and XML of the CWEs at the time of collection. The complete process has been documented in the paper <em>"CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software"</em>, which is published in the Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE '21). You will find a copy of the paper in the Doc folder. </p> <p><em><strong>Citation and Zenodo links</strong></em></p> <p>Please cite this work by referring to the published paper:</p> <ul> <li>Guru Bhandari, Amara Naseer, and Leon Moonen. 2021. CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software. In Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE '21). ACM, 10 pages. <a href="https://doi.org/10.1145/3475960.3475985">https://doi.org/10.1145/3475960.3475985</a></li> </ul> <pre><code>@inproceedings{bhandari2021:cvefixes, title = {{CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software}}, booktitle = {{Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE '21)}}, author = {Bhandari, Guru and Naseer, Amara and Moonen, Leon}, year = {2021}, pages = {10}, publisher = {{ACM}}, doi = {10.1145/3475960.3475985}, copyright = {Open Access}, isbn = {978-1-4503-8680-7}, language = {en} }</code></pre> <p>The dataset has been released on Zenodo with DOI:<a href="https://doi.org/10.5281/zenodo.4476563">10.5281/zenodo.4476563</a>. The GitHub repository containing the code to automatically collect the dataset can be found at <a href="https://github.com/secureIT-project/CVEfixes">https://github.com/secureIT-project/CVEfixes</a>, released with DOI:<a href="https://doi.org/10.5281/zenodo.5111494">10.5281/zenodo.5111494</a>.</p>
Data set: Control design, implementation and evaluation for an in-field 500 kW wind turbine with a fixed-displacement hydraulic drivetrain
<p>Data set of Wind Energy Science (WES) paper: Control design, implementation and evaluation for an in-field 500 kW wind turbine with a fixed-displacement hydraulic drivetrain</p>
Are Static Analysis Violations Really Fixed? A Closer Look at Realistic Usage of SonarQube. Dataset for OSS organizations
<p>Dataset containing all rules, files and issues mined for Apache Software Foundation and Eclipse Foundation.</p>
Conductivity Temperature and Depth Data Fixed Locations Alfacs Bay
<p>A dye tracing study was conducted in Alfacs Bay, Catalonia in March 2019. Star Oddi CTD sensors were attached to fixed points within Alfacs Bay to monitor water conditions throughout the study.</p> <p>Times are Greenwich Mean Time</p>
Text-fig. 6. Most parsimonious tree obtained after addition of Acaciaephyllum to the data set of Doyle (2008), with modifications discussed in the text, and with relationships of other taxa fixed with a backbone constraint tree based on results of Doyle (2008). Relative parsimony of alternative positions of Acaciaephyllum is indicated as in Text-fig. 2. Gnet = Gnetales. in Early Cretaceous Monocots: A Phylogenetic Evaluation
Text-fig. 6. Most parsimonious tree obtained after addition of Acaciaephyllum to the data set of Doyle (2008), with modifications discussed in the text, and with relationships of other taxa fixed with a backbone constraint tree based on results of Doyle (2008). Relative parsimony of alternative positions of Acaciaephyllum is indicated as in Text-fig. 2. Gnet = Gnetales.
Automatically fixing dependency breaking changes
<h1>Replication Data for Automated Dependency Update Experiments</h1> <h2>Dataset Description</h2> <p>This repository contains a comprehensive collection of data from experiments on automated dependency updates using Large Language Models (LLMs). The dataset consists of two main components:</p> <ol> <li><strong>Execution Traces</strong>: Detailed logs of LLM interactions and repair attempts, captured using OpenTelemetry and OpenInference (<a href="https://github.com/Arize-ai/openinference">https://github.com/Arize-ai/openinference</a>). These traces provide insights into the behavior and performance of various LLMs in addressing breaking changes caused by dependency updates in Java projects.</li> <li><strong>Docker Images</strong>: Pre-built Docker images containing the state of Java projects after successful repair attempts. These images allow for direct inspection and verification of the changes made by our automated repair system.</li> </ol> <p>This dataset enables analysis and replication of our study on automated dependency updates, offering both low-level interaction data and high-level repair outcomes.</p> <h2>Data Components</h2> <h3>1. Execution Traces</h3> <ul> <li><strong>Format</strong>: JSON Lines, persisted via a custom OpenTelemetry exporter</li> <li><strong>Content</strong>: Spans capturing various aspects of LLM interactions, including: <ul> <li>Input processing</li> <li>LLM query generation</li> <li>LLM response parsing</li> <li>Code modification attempts</li> <li>Compilation and test execution results</li> </ul> </li> <li><strong>Purpose</strong>: Enables detailed analysis of LLM decision-making processes and performance metrics</li> </ul> <h3>2. Docker Images</h3> <ul> <li><strong>Format</strong>: Compressed Docker image files (.tar.gz) in a .zip</li> <li><strong>Content</strong>: Maven project states after successful repair attempts with patch(es) applied and seperated in the second layer of each image.</li> <li><strong>Purpose</strong>: Allows direct inspection and verification of code changes made during repairs and the successful maven outcome</li> </ul> <h2>Research Context and Data Usage</h2> <p>This dataset corresponds to experiments described in our study investigating the effectiveness of zero-shot prompting and agentic approaches in automating dependency updates. It provides valuable insights into:</p> <ul> <li>Performance variations across different LLMs</li> <li>Influence of various factors on repair success rates</li> <li>Specific code changes made during successful repairs</li> </ul> <p>This dataset can be used for:</p> <ol> <li>Replicating the experimental results presented in the associated study</li> <li>Conducting further analysis on LLM behavior in software engineering tasks</li> <li>Developing and benchmarking new approaches for automated dependency updates</li> <li>Investigating the decision-making processes of LLMs in code modification tasks</li> <li>Verifying and inspecting successful repair attempts through Docker images</li> </ol> <p> </p> <div> <h2>Repair Replication with Docker</h2> To replicate specific repairs using our pre-built Docker images:<br> <div>1. Ensure you have Docker installed on your system.</div> <br>2. Use the included docker-images.zip, which first has to be unzipped <div> <pre><code>unzip docker-images.zip</code></pre> </div> <div>after which the images can be loaded as follows:</div> <div> <pre><code>docker load < image_name.tar.gz</code></pre> </div> <br> <div>3. Run the Docker container:</div> <pre><code>docker run -it [image-name]</code></pre> <br> <div>These Docker images contain the state of projects after repair attempts, allowing for easy inspection and verification of the changes made by our automated repair system.</div> </div>
MoreFixes: Largest CVE dataset with fixes
<p>In our work, we have designed and implemented a novel workflow with several heuristic methods to combine state-of-the-art methods related to CVE fix commits gathering. As a consequence of our improvements, we have been able to gather the largest programming language-independent real-world dataset of CVE vulnerabilities with the associated fix commits.<br> Our dataset containing 29,203 unique CVEs coming from 7,238 unique GitHub projects is, to the best of our knowledge, by far the biggest CVE vulnerability dataset with fix commits available today. These CVEs are associated with 35,276 unique commits as sql and 39,931 patch commit files that fixed those vulnerabilities(some patch files can't be saved as sql due to several techincal reasons)<br> Our larger dataset thus substantially improves over the current real-world vulnerability datasets and enables further progress in research on vulnerability detection and software security. We used <a href="https://nvd.nist.gov/">NVD(nvd.nist.gov)</a> and <a href="https://github.com/github/advisory-database">Github Secuirty advisory Database</a> as the main sources of our pipeline.</p> <p>We release to the community a 16GB PostgreSQL database that contains information on CVEs up to 2024-09-26, CWEs of each CVE, files and methods changed by each commit, and repository metadata.<br> Additionally, patch files related to the fix commits are available as a separate package. Furthermore, we make our dataset collection tool also available to the community.</p> <p>`cvedataset-patches.zip` file contains fix patches, and `postgrescvedumper.sql.zip` contains a postgtesql dump of fixes, together with several other fields such as CVEs, CWEs, repository meta-data, commit data, file changes, method changed, etc.</p> <p>MoreFixes data-storage strategy is based on <a href="https://github.com/secureIT-project/CVEfixes">CVEFixes</a> to store CVE commits fixes from open-source repositories, and uses a modified version of <a href="https://github.com/SAP/project-kb/tree/main/prospector">Porspector(part of ProjectKB from SAP)</a> as a module to detect commit fixes of a CVE. Our full methodology is presented in the paper, with the title of "MoreFixes: A Large-Scale Dataset of CVE Fix Commits Mined through Enhanced Repository Discovery", which will be published in the Promise conference (2024).</p> <p>For more information about usage and sample queries, visit the Github repository: <a href="https://github.com/JafarAkhondali/Morefixes">https://github.com/JafarAkhondali/Morefixes</a></p> <p><strong>If you are using this dataset, please be aware that the repositories that we mined contain different licenses and you are responsible to handle any licesnsing issues. This is also the similar case with CVEFixes.</strong></p> <p><strong>This product uses the NVD API but is not endorsed or certified by the NVD.</strong></p> <p>This research was partially supported by the Dutch Research Council (NWO) under the project NWA.1215.18.008 Cyber Security by Integrated Design (C-SIDe).</p> <p> </p> <p>To restore the dataset, you can use the docker-compose file available at the <a href="https://github.com/JafarAkhondali/morefixes">gitub repository</a>. Dataset default credentials after restoring dump:</p> <pre><code>POSTGRES_USER=postgrescvedumper POSTGRES_DB=postgrescvedumper POSTGRES_PASSWORD=a42a18537d74c3b7e584c769152c3d</code></pre> <p> </p> <p>Please use this for citation:</p> <pre>```<br>@inproceedings{akhoundali2024morefixes, title={MoreFixes: A large-scale dataset of CVE fix commits mined through enhanced repository discovery}, author={Akhoundali, Jafar and Nouri, Sajad Rahim and Rietveld, Kristian and Gadyatskaya, Olga}, booktitle={Proceedings of the 20th International Conference on Predictive Models and Data Analytics in Software Engineering}, pages={42--51}, year={2024} } ```</pre>
"@alex, this fixes #9": Analysis of Referencing Patterns in Pull Request Discussions
<p>This publication consists of a dataset of 7k references manually identified in 450 Pull request (PR) discussion threads sampled from GitHub in CSV format. In addition to the dataset, it also contains R code files which were written to analyze this dataset statistically. This dataset is released under the research, which is accepted for publication at CSCW 2021 conference, titled "@alex, this fixes #9": Analysis of Referencing Patterns in Pull Request Discussions".</p> <p><strong>Paper Abstract</strong></p> <p>Pull Requests (PRs) are a frequently used method for proposing changes to source code repositories. When discussing proposed changes in a PR discussion, stakeholders often reference a wide variety of information objects for establishing shared awareness and common ground. Previous work has not considered how referential behavior impacts collaborative software development via PRs. This knowledge gap is the major barrier in evaluating the current support for referencing in PRs and improving them. We conducted an explorative analysis of ~7K references, collected from 450 public PRs on GitHub, and constructed taxonomies of referent types and expressions. Using our annotated dataset, we identified several patterns in the use of references. Referencing source code elements was prevalent but the authoring interface lacks support for it. Three classes of contextual factors influence referencing behaviors: referent type, discussion thread, and project attributes. Referencing patterns may indicate PR outcomes (e.g., merged PRs frequently reference issues, users, and tests). We conclude with design implications to support more effective referencing in PR discussion interfaces.</p>
Silencing of hippocampal synaptic transmission impairs spatial reward search on a head-fixed tactile treadmill task
<p>Publication data</p> <p>This repository contains the raw data files for the following manuscript:</p> <p>Title: Silencing of hippocampal synaptic transmission impairs spatial reward search on a head-fixed tactile treadmill task<br> Authors: Jake T. Jordan and J. Tiago Gonçalves<br> Pre-print in bioRxiv. doi:10.1101/2021.09.03.458092 (2021)</p> <p>A summary of all experimental groups and data tables and included as two Excel (.xlsx) files: DREADDs_Cued.xlsl and DREADDs_Spatial.xlsl, these correspond to figures 2 and 3 of the publication, respectively.</p> <p>The raw data files were acquired as described in Jordan et al. (2021a) doi:10.1016/j.xpro.2021.100770 <br> Software code for data acquisition and interpretation is available at doi:10.5281/zenodo.5196612</p>
Territoriality drives patterns of fixed space use in Caribbean parrotfishes
<p>Animals often occupy home ranges where they conduct daily activities. In many parrotfishes, large terminal phase (TP) males defend their diurnal (i.e., daytime) home ranges as intraspecific territories occupied by harems of initial phase (IP) females. However, we know relatively little about the exclusivity and spatial stability of these territories. We investigated diurnal home range behavior in several TPs and IPs of five common Caribbean parrotfish species on the fringing coral reefs of Bonaire, Caribbean Netherlands. We computed parrotfish home ranges to investigate differences in space use and then quantified spatial overlap of home ranges between spatially co-occurring TPs to investigate exclusivity. We also quantified spatial overlap of home ranges estimated from repeat tracks of a few TPs to investigate their spatial stability. We then discussed these results in the context of parrotfish social behavior. Home range sizes differed significantly among species. Spatial overlap between home ranges was lower for intraspecific than interspecific pairs of TPs. Focal TPs frequently engaged in agonistic interactions with intraspecific parrotfish and interacted longest with intraspecific TP parrotfish. This behavior suggests that exclusionary agonistic interactions may contribute to the observed patterns of low spatial overlap between home ranges. Spatial overlap of home ranges estimated from repeated tracks of several TPs of three study species was high, suggesting that home ranges were spatially stable for at least one month. Taken together, our results suggest that daytime parrotfish space use is constrained within fixed intraspecific territories in which territory holders have nearly exclusive access to resources. Grazing by parrotfishes maintains benthic reef substrates in early successional states that are conducive to coral larval settlement and recruitment. Behavioral constraints on parrotfish space use may drive spatial heterogeneity in grazing pressure and affect local patterns of benthic community assembly. A thorough understanding of the spatial ecology of parrotfishes is, therefore, necessary to elucidate their functional roles on coral reefs. </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.