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406 results for “API”
Replication Package for Understanding the Impact of APIs Behavioral Breaking Changes on Client Applications
<p>This repository contains the replication package for the paper Understanding the Impact of APIs Behavioral Breaking Changes on Client Applications.<br>This paper will be published in the Proceedings of the ACM on Software Engineering journal. The replication package includes the scripts and data we extracted, leading us to our findings.</p>
[Supplementary-material] Validation of Inter-Parameter Dependencies in API Gateways
<p>This is the supplementary material for the paper Validation of Inter-Parameter Dependencies in API Gateways.</p>
Figure 2 in The association between ClaI polymorphism and hygienic behavior in Apis mellifera
Figure 2. PCR and restriction products picture of the ClaI polymorphism.
Whole genome data of British and Irish Apis mellifera mellifera
<p>The data here was analysed in <a href="https://doi.org/10.1080/00218839.2024.2411483" target="_blank" rel="noopener">Buswell et al 2024 Whole genome analyses of introgression in British and Irish Apis mellifera mellifera, <em>Journal Of Apicultural Research</em></a> </p> <p>Included here are two files, a vcf of the 140 individuals across ~9 million SNPs and an allele frequences file used in the analysis to perform the ABBA BABA analysis across the 183,94 SNPs that were found in the outgroup. The raw data used to create these data can be found Read Archive BioProject accession: PRJNA1069874 and the code used to produce these data can be found at https://github.com/Vicbuz/When_ones_not_enough and in the directory https://github.com/Vicbuz/When_ones_not_enough/tree/main/WGS_files. </p> <p>For more details, please see the paper.</p>
Dataset of Empirical Evidence of Large-Scale Diversity in API Usage of Object-Oriented Software
<p>Dataset of Empirical Evidence of Large-Scale Diversity in API Usage of Object-Oriented Software, SCAM'13.</p> <p>https://www.monperrus.net/martin/companion-diversity-api-usages</p>
Energy Consumption of IO APIS (ESEM'2019 paper)
<p>## About the experiments</p> <p>### Acronyms used in the figures</p> <p>- BufferedReader (BR).<br> - LineNumberReader (LNR).<br> - CharArrayReader (CAR).<br> - PushbackReader (PBR).<br> - FileReader (FR).<br> - FileInputStream (FIS).<br> - BufferedInputStream (BIS).<br> - StringReader (SR).<br> - PushbackInputStream (PBIS).<br> - StringBufferInputStream (SBIS).<br> - ByteArrayInputStream (BAIS).<br> - LineNumberInputStream (LNIS).<br> - Scanner (SCN).<br> - O método readAllLines da classe Files, e um Stream de String (RFAL).<br> - O método lines da classe Files, e um Stream de String (RFL).<br> - O método newBufferedReader da classe Files, e um Stream de String (BRFL)<br> - BufferedWriter (BW).<br> - FileWriter (FW).<br> - StringWriter (SW).<br> - PrintWriter (PW).<br> - CharArrayWriter (CAW).<br> - FileOutputStream (FOS).<br> - ByteArrayOutputStream (BAOS).<br> - BufferedOutputStream (BOS).<br> - PrintStream (POS).</p> <p>### Settings</p> <p>For each setting, we experimented with three files of different sizes: 1 Mb, 10 Mb e 20 Mb.</p> <p>The experiments were ran in the following machine</p> <p>- Hardware: Intel® Core™ i7-2670QM CPU @ 2.20GHz, with 4 processors, 16GB DDR3 1600MHz, Ubuntu 16.04 LTS (kernel 4.4.0-112-generic).<br> - Java: Java(TM) SE Runtime Environment, version 1.8.0-151</p>
Identification data for discrimination between honey bee (Apis mellifera) lineages
<p><span>The data in dw.xml file can be used for discriminate between 4 lineages of honey bee </span><em>Apis mellifera</em><span>: A, C, M, O. </span></p> <p>The discrimination is based on 19 landmarks of a forewing. The data were obtained from:</p> <p>Nawrocka, A., Kandemir, İ., Fuchs, S., & Tofilski, A. (2018). Computer software for identification of honey bee subspecies and evolutionary lineages. Apidologie, 49(2), 172-184. <a href="https://doi.org/10.1007/s13592-017-0538-y" rel="noopener">https://doi.org/10.1007/s13592-017-0538-y</a></p> <p>The dw.xml file can be used in <a href="http://drawwing.org/identifly" target="_blank" rel="noopener">IdentiFly</a> software or in R package <a href="https://github.com/DrawWing/IdentiFlyR" target="_blank" rel="noopener">IdentiFlyR</a> https://github.com/DrawWing/IdentiFlyR. </p> <p><span> </span></p>
Identification data for discrimination between regions in lineage C of honey bee (Apis mellifera)
<p>The data in dw.xml file can be used for discriminate between 5 regions in lineage C of honey bee <em>Apis mellifera</em>: Greece, Croatia and Slovenia, Italy, Romania and Moldova and European part of Turkey. </p> <p>The discrimination is based on 19 landmarks of a forewing. The data were obtained from:</p> <p>Oleksa, A., Căuia, E., Siceanu, A., Puškadija, Z., Kovačić, M., Pinto, M. A., Rodrigues, P. J., Hatjina, F., Charistos, L., Bouga, M., Prešern, J., Kandemir, İ., Rašić, S., Kusza, S., Tofilski, A. (2023). Honey bee (<em>Apis mellifera</em>) wing images: a tool for identification and conservation. GigaScience, 12, giad019. <a href="https://doi.org/10.1093/gigascience/giad019" target="_blank" rel="noopener">https://doi.org/10.1093/gigascience/giad019</a></p> <p>The dw.xml file can be used in <a href="http://drawwing.org/identifly" target="_blank" rel="noopener">IdentiFly</a> or in R package <a href="https://github.com/DrawWing/IdentiFlyR" target="_blank" rel="noopener">IdentiFlyR</a>. </p>
Supplementary data for "Enhancing Resource-based Test Case Generation For RESTful APIs with SQL Handling"
<p>Supplement to: <em>Enhancing Resource-based Test Case Generation For RESTful APIs with SQL Handling</em></p> <p>In this repository, we provide <em>tests</em> and their <em>coverage reports</em> (conducted by Intellij coverage reports) that are used in the Discussion section of the paper.</p>
Replication Package for "Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis"
<p>This repository provides the data sets and scripts used in the paper "Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis" by Sebastian Nielebock, Robert Heumüller, Kevin Michael Schott, and Frank Ortmeier from the Faculty of Computer Science of the Otto-von-Guericke University Magdeburg, Germany. This paper is published in Springer's "Automated Software Engineering - An International Journal" in August 2021. The article is available as open access at <a href="https://dx.doi.org/10.1007/s10515-021-00294-x">https://dx.doi.org/10.1007/s10515-021-00294-x</a>. A preprint is available under <a href="https://arxiv.org/abs/2008.00277">https://arxiv.org/abs/2008.00277</a>.</p> <p>All scripts and data sets are provided by the authors and come without any guarantee. For any issues regarding replication do not hesitate to contact us ({sebastian.nielebock,robert.heumueller, kevin.schott, frank.ortmeier} <at> ovgu.de)</p> <p>If you use or refer to these datasets, please cite our paper using the following BibTex entry.</p> <pre>@article{NielebockAPIFilterSearch2021, title = {Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis}, author = {Sebastian Nielebock and Robert Heum\"{u}ller and Kevin Michael Schott and Frank Ortmeier}, editor = {Springer}, journal = {Springer Automated Software Engineering - An International Journal}, number = {15}, pages = {1-48}, volume = {28}, url = {https://arxiv.org/abs/2008.00277}, doi = {10.1007/s10515-021-00294-x}, year = {2021}, } </pre>
Which RESTful API Design Rules are Important and How Do They Improve Software Quality? A Delphi Study with Industry Experts
<p>The dataset of a Delphi study with 8 industry experts who reached consensus on the perceived importance and positive software quality impact of 82 RESTful API design rules from the catalogue by Mark Massé ("REST API Design Rulebook", O’Reilly Media, 2011). The replication package contains:</p> <ul> <li><strong>rules.csv:</strong> the final consensus results for the 82 rules in CSV format</li> <li><strong>rule-importance.xlsx</strong>: the detailed results and analysis for rule importance as an Excel spreadsheet</li> <li><strong>rule-sw-quality-impact.xlsx</strong>: the detailed results and analysis for rule impact on software quality as an Excel spreadsheet</li> </ul> <p>In this version, we updated the final numbers for the software quality mapping with the results from the synchronous meeting.</p>
Data from: Evaluating the strength of western honey bees (Apis mellifera L.) colonies fed pollen subsitutes over winter
<p>We fed managed honey bee colonies one of two commercially available pollen substitutes (MegaBee, AP23) or no diet over late fall and winter in Florida, U.S.A. We measured the change in mass of adult bees, brood, and full colonies after nine weeks of feeding. We also measured the average mass per pupa, amount of pollen substitute patty consumed, the average total mass of natural pollen collected, and the proportion of pollen foragers.</p>
Conformance Assessment of Architectural Design Decisions on the Mapping of Domain Model Elements to APIs and API Endpoints: Dataset and Code
<p>This is the dataset and related code artifact for the article "Conformance Assessment of Architectural Design Decisions on the Mapping of Domain Model Elements to APIs and API Endpoints".</p> <p><strong>Abstract of the article:</strong></p> <p> Microservice APIs are often identified and designed based on Domain-Driven Design (DDD). To help in the continuous analysis of mappings of domain model elements to APIs and API endpoints, we aim to automate the assessment of conformance to Architectural Design Decision (ADD) options. The ADDs, their decision options, and relevant decision drivers studied in this paper originate from an empirical study on the mapping of domain model elements to APIs and API endpoints in practice. We propose automated detectors to detect the decision options of the ADDs taken in a given microservice API model, and an assessment scoring scheme based on the empirical knowledge codified in the ADDs. We evaluate our work, by first manually creating a ground truth for 14 cases in a multi-case study, and then comparing the results of our automated detectors to the ground truth for each of the 14 cases. In the cases we were able to identify 86\% of the decision points correctly, and a statistical analysis of our data shows only a negligible effect size for differences to the ground truth.</p>
APIzation: Generating Reusable APIs from StackOverflow Code Snippets - Replication Package
<p>This repository represents the replication package for the paper <em>APIzation: Generating Reusable APIs from StackOverflow Code Snippets</em>.</p> <p>The paper is published in the proceeding of the <em>36th IEEE/ACM International Conference on Automated Software Engineering (ASE)</em>.</p> <p>In this replication package, we provide all the <em>APIzations</em> we produced with our tool. Also, we include the data we used for our evaluation.</p>
Processed data for the "Property-Based Testing of Web APIs" paper
<p>Processed data for the "Property-Based Testing of Web APIs" paper. Each directory in the archive consists of:</p> <p>- metadata.json. Metadata about a test run - tested fuzzer name, run duration, etc</p> <p>- fuzzer.json - Structured fuzzer output</p> <p>- deduplicated_cases.json - Deduplicated reported failures, when fuzzers provide it</p> <p>- sentry.json - Cleaned Sentry events for this run</p> <p>- target.json - Parsed stdout for Gitlab & Disease.sh targets that were tested without Sentry integration</p>
An Exploratory Study on Faults in Web API Integration in a Large-Scale Payment Company: Appendix
<p>Appendix of our "An Exploratory Study on Faults in Web API Integration in a Large-Scale Payment Company: Appendix" paper.</p>
Energy Consumption Estimation of API-usage in Smartphone Apps via Static Analysis
<p>OPEN CALL FOR COLLECTING ENERGY PROFILES @ <a href="https://github.com/AbdulAli/replication-kit-msr-2023">https://github.com/AbdulAli/replication-kit-msr-2023</a></p> <p>Cite this work as:</p> <p>@inproceedings{bangash2023msr,<br> title={Energy Consumption Estimation of API-usage in Mobile Apps via Static Analysis},<br> author={Bangash, Abdul Ali and Jamal, Qasim and Eng, Kalvin and Ali, Karim and Hindle, Abram},<br> booktitle={2023 20th International Conference on Mining Software Repositories (MSR)},<br> pages={5721--5730},<br> year={2023},<br> organization={IEEE}<br> }</p> <p>This is the replication-kit of the paper published at MSR 2023.</p> <p>It includes:</p> <ul> <li>SQLite operations' benchmarks</li> <li>SQLite benchmarks' energy profiles</li> <li>The E-Factor Calculation program</li> </ul>
Complex urban environments provide Apis mellifera with a richer plant forage than suburban and more rural landscapes
<p>Growth in the global development of cities, and increasing public interest in beekeeping, has led to rises in the numbers of urban apiaries. Towns and cities can provide an excellent diet for managed bees, with a diverse range of nectar and pollen available throughout a long flowering season and are often more ecologically diverse than the surrounding rural environments. Accessible urban honeybee hives are a valuable research resource to gain insights into the diet and ecology of wild pollinators in urban settings. We used DNA metabarcoding of the rbcL and ITS2 gene regions to characterise the pollen community in Apis mellifera honey, inferring the floral diet, from 14 hives across an urban gradient around Greater Manchester, UK. We found that the proportion of urban land around a hive is significantly associated with an increase in the diversity of plants foraged, and that invasive and non-native plants appear to play a critical role in the sustenance of urban bees, alongside native plant species. The proportion of improved grassland, typical of suburban lawns and livestock farms, is significantly associated with decreases in the diversity of plant pollen found in honey samples. These findings are relevant to urban landscape developers motivated to encourage biodiversity and bee persistence, in line with global bio-food security agendas.</p>
A Versatile and Secure Data Management System API facilitating the Interconnection of any software component and Easy Data Access.
<p>In the context of the EU-funded project PILOTING (No. 871542), a versatile Data Management System (DMS) was deployed and facilitated the easy integration of nine different robotic systems and various payloads and the storing of all the data observations produced during the inspections. The DMS is designed under the DMS-Data Model (DMS-DM) and operates on top of a representational state transfer (REST) application programming interface (API) that provides a simplified way to exchange data through HTTP(S) requests from a client to the server. One of its key advantages is that it provides a great deal of flexibility so the model could accommodate extensions if needed. Data is not tied to resources or methods, so REST can handle multiple types of calls, return different data formats and even change structurally with the correct implementation of hypermedia. <br> It supports CRUD operations via GET, POST, PUT, PATCH, and DELETE HTTP methods and stores the data on a PostgreSQL object-relational database system. The REST API has been created with Python's Django (web framework) and Django REST Framework, a powerful and flexible toolkit for building Web APIs. <br> The constructed document presents the communication endpoints (DMS API’s Uniform Resource Identifiers (URIs)) under which an authorized user can have access to the DMS API and subsequently to the collected data. </p>
Spectrogram of vibrations from inside an active beehive (Apis mellifera)
<p>A piezoelectric transducer is inserted inside an active beehive. We record the vibrations within the hive every 5 minutes for about 2 months. We extract the spectrogram from each recording. The spectrogram is a time-frequency representation that shows how the frequencies of the recording evolve over time. All spectrograms from 3865 recordings are stacked and their corresponding timestamp appears on the upper left corner. The video demonstrates the vibrational activity of the bees and any other factor that can vibrate the beehive for two months.</p>
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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.