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Adopting Micro-Frontends: Impacts and Challenges – A Systematic Mapping Study
<p><strong>Context: </strong>Incorporating new technologies into monolithic frontend projects has been challenging for software development teams, due to issues like code redundancy, inconsistency, and scalability. To address these challenges, the micro-frontend architecture emerged, offering a more modular and independent solution.</p> <p><strong>Goal:</strong>This study aims to investigate the impacts and challenges of adopting the micro-frontend architecture. <br><strong>Method:</strong> We conducted a Systematic Mapping Study (SMS) to identify the architectural patterns and approaches used in micro-frontends. Additionally, we carried out a survey to validate the strategies and challenges of adopting this architecture.</p> <p><strong>Results:</strong> The findings highlighted the relevance of the micro-frontend architecture in modernizing the frontend layer. However, its adoption is closely tied to the complexity and size of the project and team. Therefore, implementing the micro-frontend architecture with a well-defined set of methodological and technological decisions increases the likelihood of successful adoption.</p> <p><em>Keywords:</em> <em>Micro-frontend, Systematic Mapping Study, Challenges, Survey</em></p> <p> </p> <p> </p>
Test Dataset for the ICASSP-2024 3D-CBCT challenge (Part 2)
<p>Test Dataset for the ICASSP-2024 3D-CBCT challenge (Part 2)</p> <p>https://sites.google.com/view/icassp2024-spgc-3dcbct/home</p> <p>Please download all files (of all parts, 1-2) into one folder, and then merge them together with:</p> <p><code>$ zip -s 0 test.zip --out test_unsplit.zip</code></p> <p> </p> <p>This will create a new zip file, "train_unsplit.zip" that then you can unzip with your favorite tool, e.g.</p> <p><code>$ unzip test_unsplit.zip</code></p> <p> </p> <p><br>The CBCT geometry required to be used</p> <blockquote> <p>image size : [300 300 300] mm<br>image shape : [256 256 256] voxels<br>voxel size : [1.171875 1.171875 1.171875] mm<br>detector shape : [256 256] pixels<br>detector size : [600, 600] mm<br>pixel size : [2.34375, 2.34375] mm<br>distance source origin (axis of rotation, center of image) : 575 mm<br>distance source to detector : 1050 mm</p> </blockquote> <p> </p> <p>We recommend zenodo_get to download the files:<br><a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a></p>
Test dataset for the ICASSP-2024 3D-CBCT challenge (Part 1)
<p>Test Dataset for the ICASSP-2024 3D-CBCT challenge (Part 1)</p> <p>https://sites.google.com/view/icassp2024-spgc-3dcbct/home</p> <p>Please download all files (of all parts, 1-2) into one folder, and then merge them together with:</p> <p><code>$ zip -s 0 test.zip --out test_unsplit.zip</code></p> <p> </p> <p>This will create a new zip file, "train_unsplit.zip" that then you can unzip with your favorite tool, e.g.</p> <p><code>$ unzip test_unsplit.zip</code></p> <p> </p> <p><br>The CBCT geometry required to be used</p> <blockquote> <p>image size : [300 300 300] mm<br>image shape : [256 256 256] voxels<br>voxel size : [1.171875 1.171875 1.171875] mm<br>detector shape : [256 256] pixels<br>detector size : [600, 600] mm<br>pixel size : [2.34375, 2.34375] mm<br>distance source origin (axis of rotation, center of image) : 575 mm<br>distance source to detector : 1050 mm</p> </blockquote> <p> </p> <p>We recommend zenodo_get to download the files:<br><a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a></p> <p><br><br></p> <p> </p> <h2>Files</h2>
SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge (CtrSVDD Track, Test Set)
<p>For more information about SVDD Challenge 2024, please refer to https://challenge.singfake.org/.<br><br>We have released the test set here.</p> <p>The training and development set is at https://zenodo.org/records/10467648.</p> <p>The Interspeech paper that describes the dataset details and baseline analysis is https://arxiv.org/abs/2406.02438.</p>
A non-invasive BCG skin challenge model for assessing tuberculosis vaccine efficacy
<p>Flow cytometry FCS files for Figure 3 days 7 and 14.</p>
ADD 2023 Challenge Track 1.2 Evaluation Dataset (Round 2)
<div> <div>Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.</div> <br> <div>The ADD 2023 dataset is publicly available.</div> <br> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> <br> <div>If you use this dataset, please cite the following paper:</div> <blockquote> <div>Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130</div> </blockquote> </div>
ADD 2023 Challenge Track 3 Training/Development Dataset
<div> <div>Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.</div> <br> <div>The ADD 2023 dataset is publicly available.</div> <br> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> <br> <div>If you use this dataset, please cite the following paper:</div> <blockquote> <div>Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130</div> </blockquote> </div>
ADD 2023 Challenge Track 1.2 Evaluation Dataset (Round 1)
<div> <div>Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.</div> <br> <div>The ADD 2023 dataset is publicly available.</div> <br> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> <br> <div>If you use this dataset, please cite the following paper:</div> <blockquote> <div>Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130</div> </blockquote> </div>
ADD 2023 Challenge Track 3 Evaluation Dataset
<div> <div>Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.</div> <br> <div>The ADD 2023 dataset is publicly available.</div> <br> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> <br> <div>If you use this dataset, please cite the following paper:</div> <blockquote> <div>Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130</div> </blockquote> </div>
ADD 2023 Challenge Track 2 Training/Development Dataset
<div> <div>Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.</div> <br> <div>The ADD 2023 dataset is publicly available.</div> <br> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> <br> <div>If you use this dataset, please cite the following paper:</div> <blockquote> <div>Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130</div> </blockquote> </div>
ADD 2023 Challenge Track 1.1 Evaluation Dataset
<div> <div>Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.</div> <br> <div>The ADD 2023 dataset is publicly available.</div> <br> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> <br> <div>If you use this dataset, please cite the following paper:</div> <blockquote> <div>Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130</div> </blockquote> </div>
ADD 2023 Challenge Track 2 Evaluation Dataset
<div> <div>Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.</div> <br> <div>The ADD 2023 dataset is publicly available.</div> <br> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> <br> <div>If you use this dataset, please cite the following paper:</div> <blockquote> <div>Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130</div> </blockquote> </div>
ADD 2023 Challenge Track 1.2 Training/Development Dataset
<div> <div>Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge (http://addchallenge.cn/add2023) includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks.</div> <br> <div>The ADD 2023 dataset is publicly available.</div> <br> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> <br> <div>If you use this dataset, please cite the following paper:</div> <blockquote> <div>Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan, Chenglong Wang, Tao Wang, Chu Yuan Zhang, Xiaohui Zhang, Yan Zhao, Yong Ren, Le Xu, Junzuo Zhou, Hao Gu, Zhengqi Wen, Shan Liang, Zheng Lian, Shuai Nie, Haizhou Li: ADD 2023: the Second Audio Deepfake Detection Challenge. DADA@IJCAI 2023: 125-130</div> </blockquote> </div>
HAND IN HAND: Empowering Teachers Across Europe to Deal with Social, Emotional and Diversity Related Career Challenges - International Dataset
<p><span>HAND IN HAND: Empowering teachers across Europe to deal with social, emotional and diversity related career challenges (HAND:ET) is a European (Erasmus K3) policy experimentation project that brings together 11 partners and 13 associated partners from seven countries (Austria, Croatia, Denmark, Germany, Portugal, Slovenia, and Sweden).</span></p> <p><span>The project focuses on teachers by supporting their development of social and emotional competencies as well as their diversity awareness (SEDA) to empower them for the complexity of everyday working life with increasingly diverse classrooms and enable them to deal flexibly with new challenges. It also puts the teachers' well-being at the centre by highlighting how developing SEDA competencies simultaneously fosters self-care for teachers, giving a central role to the voices of teachers.</span></p> <p><span>Information on the project can be accessed at: <a href="https://www.handinhand.si/en/" target="_blank" rel="noopener">https://www.handinhand.si/en/</a></span></p> <p><span>Further information on the project and the evaluation results can be found in the following publication: Kozina, A. (2024). Empowering Teachers Across Europe to Deal with Social, Emotional and Diversity Related Challenges, Volume 1: Experimentation Perspectives. Waxman.</span></p> <p><span>Information on the instruments used in the evaluation is provided in the attached document "HAND_ET_Scale_Documentation.pdf".</span></p> <p><span>Design: All countries implementing the experiment (Austria, Croatia, Portugal, Slovenia and Sweden) invited schools to participate. The schools participating in the experiment were randomly allocated to either the experimental or the control group. Central to the experiment’s design, schools had to agree to their participation in either condition (experimental or control). The intention was to test the effectiveness of the HAND:ET system in promoting SEDA competencies: to compare the changes in the same competencies from before implementing the HAND:ET system (pre-test) to after implementing the system (post-test) in a group of teachers (and other school staff) who took part in the experiment (experimental group) with a group of teachers (and other school staff) who did not participate (the control group). </span></p> <p><span> </span><span>Data collection period: T1 (pre-test) data was collected August to September 2022, T2 (post-test) data was collected Mai to July 2023.</span></p> <p><span> </span><span>Sample: The HAND_ET datafile includes primary and/or secondary teachers from Austria, Croatia, Portugal, Slovenia, and Sweden, who responded to the questionnaire either at first time point or second time point or both. In Austria, teachers teaching students in grades 1 to 4, in Croatia those teaching students in grades 1 to 8, in Portugal those teaching students in grades 1 to 12, in Slovenia those teaching students in grades 1 to 9, while in Sweden teachers teaching students in grades 4 to 9 participated.</span></p> <p><span> </span><span>The HAND_ET datafile is structured as follows: </span></p> <p><span>·</span><span> </span><span>Variables from 'IDS' to 'Gender' include general information about the participant (e.g group, participation in pre- or post-test, gender)</span></p> <p><span>·</span><span> </span><span>Variables from 'TC06exp' to 'TCN22_TCN2204' capture responses from questionnaire items administered during the pre-test.</span></p> <p><span>·</span><span> </span><span>Variables from 'TC02_2' to 'TCN30_TCN30Q04_TCN30G03_2' contain responses to questionnaire items used in the post-test data collection.</span></p> <p><span>·</span><span> </span><span>Variables from 'observe' to 'coopteach' include scale scores for the scales administered in pre-test.</span></p> <p><span>·</span><span> </span><span>Variables from 'observe_2' to 'coopteach_2' include scale scores for the scales administered in post-test.</span></p> <p><span>·</span><span> </span><span>Variables from 'observe_D' to 'CloseC_D' include scale scores calculated as differences between measurements at Time 1 (T1) and Time 2 (T2) for all questionnaire scales.</span></p> <p><span>·</span><span> </span><span>Variables from 'observe_D.1' to 'CloseS_D.5' contain scale score differences (T1-T2) with missing values imputed. As we did 5 imputations, there are five variables for each scale (as indicated at the end of the variable name).</span></p> <p><span> </span><span>The scale score for each participant at each point in time was computed as the arithmetic mean of responses to the items of a scale measuring a SEDA construct. A scale value was only computed if responses for at least half of the items of a scale were available. No overall scale score was computed for multidimensional constructs. Subscales were treated as separate scales in the analysis. The difference score was computed as the T2 minus T1 scale score. A positive value corresponds to an increase of the scale score and a negative value indicates a decrease in the scale score of T2 compared to T1.</span></p> <p><span> </span><span>The dataset includes variables with imputed scale scores. We only imputed the difference score for each of the outcome variables five times. The imputation was carried out separately for each of the scales based on the three variables: group, T1, and T2 scale score. We used predictive mean matching with the R package mice (Buuren & Groothuis-Oudshoorn, 2011) for imputation. The teachers that have a missing in the group variable have no imputed values.</span></p>
Challenge tests Salmonella in BSF larvae rearing
Open the record for dataset details and reuse information.
Apache POI pre-processed data for the first DocGen challenge at DySDoc 3
<p>Apache POI pre-processed data for the first DocGen challenge</p> <p>The pre-processed data for First Software Documentation Generation Challenge (DocGen), hosted at the <a href="https://dysdoc.github.io/">Third International Workshop on Dynamic Software Documentation (DySDoc 3)</a>, includes the following datasets for <a href="https://poi.apache.org/">Apache POI 3.17</a>:</p> <p><strong>Call graph between method and classes.</strong></p> <p>File: call-graph-poi-3.17-all.zip</p> <p>CSV file with the call graph between methods and between classes. Class A calls class B if there exists a call between amethod of class A and a method of class B. The call graph was produced by the tool <a href="https://github.com/gousiosg/java-callgraph/">java-callgraph</a>. </p> <p>The CSV file contains the following columns:</p> <ul> <li>call_type: call between (C)lasses or (M)ethods</li> <li>caller: the Fully Qualified Name (FQN) of the caller</li> <li>method_call_type: the type of method call: <ul> <li>M for invokevirtual calls</li> <li>I for invokeinterface calls</li> <li>O for invokespecial calls</li> <li>S for invokestatic calls</li> <li>D for invokedynamic calls</li> </ul> </li> <li>callee: the FQN of the callee</li> </ul> <p>For more details about the format and each type of method call, check the tool <a href="https://github.com/gousiosg/java-callgraph/">README</a>.</p> <p><strong>Inheritance hierarchy</strong></p> <p>File: poi-3.17-inheritance.zip</p> <p>A CSV file with the inheritance hierarchy of POI, which was extracted using bcel 6.2</p> <p>The CSV file contains the following columns:</p> <ul> <li>record_id: sequential number</li> <li>parent_class: the parent class</li> <li>child_class: the child class</li> <li>relationship_type: the type of relationship between classes, i.e., the child class 'extends' or 'implements' the parent class</li> </ul> <p><strong>Issues</strong></p> <p>File: bugzilla-poi-dump.zip</p> <p>CSV file with the list of issues of <a href="https://bz.apache.org/bugzilla/buglist.cgi?product=POI">Apache POI</a> (timestamp: Tue Feb 27, 2018, 18.41.40 UTC)</p> <p>The CSV file contains the following columns:</p> <ul> <li>record_id: sequential number</li> <li>issue_id: the ID that identifies the issue in the issue tracker</li> <li>issue_url: the URL of the issue in the issue tracker</li> <li>issue_title: the title of the issue</li> <li>xml_path: the path to the XML of the issue, which contains all the issue information provided by the issue tracker</li> </ul> <p>All the issues in XML format can be found in the "poi" folder in the ZIP file</p> <p><strong>Commits</strong></p> <p>File: poi-commits.zip</p> <p>A JSON file with commit information for POI 3.17 (until revision 219dff00e6, on Sept. 8, 2017). The information was extracted using the tools <a href="https://dl.acm.org/citation.cfm?doid=2024445.2024463">Historage</a> and <a href="https://dl.acm.org/citation.cfm?doid=2597073.2597125">Kataribe</a>.</p> <p>For each commit, we provide:</p> <ul> <li>Commit hash</li> <li>Parent commit hash (if exists)</li> <li>Commit message</li> <li>Commit time</li> <li>Committer name</li> <li>Method-level changes (addition/deletion/modification/renaming and method FQN). <ul> <li>The FQN contains information about the class (CN) and method (MT) or constructor (CS)</li> </ul> </li> </ul> <p><strong>StackOverflow posts</strong></p> <p>File: apache-poi-SO.zip</p> <p>JSON file with all 6,299 Stack Overflow threads with the <code>apache-poi</code> tag,</p>
Supporting dataset for: "Technical note: Laboratory modelling of urban flooding: strengths and challenges of distorted scale models"
<p>Li, X., Erpicum, S., Bruwier, M., Mignot, E., Finaud-Guyot, P., Archambeau, P., Pirotton, M., Dewals, B., (2019). Laboratory modelling of urban flooding: strengths and challenges of distorted scale models. Hydrology and Earth System Sciences, 2019.</p> <p>Included in this repository the data used in:</p> <p>- Figure 3: Upscaled water depth profiles in prototype in street 4 of Araud (2012).</p> <p>- Figure 4: Spatial distributions and scatter plot of the differences between the upscaled water depths</p> <p>- Figure 6: Upscaled outflow discharge in each street compared to the corresponding value deduced from the observations in the less distorted model.</p>
VOT19 Challenge RGBD
<p>Dataset for the VOT19 RGBD Challenge.</p>
Supporting data: Guzzo et al., (2019) Effects of repeated daily acute heat challenge on the growth and metabolism of a cold water stenothermal fish, Journal of Experimental Biology
<p>Supporting data for Guzzo et al. (2019) Effects of repeated daily acute heat challenge on the growth and metabolism of a cold water stenothermal fish. Journal of Experimental Biology, jeb.198143 doi: 10.1242/jeb.198143</p>
FIGURE 1. A in On the taxonomic status of Teyuwasu barberenai Kischlat, 1999 (Archosauria Dinosauriformes), a challenging taxon from the Upper Triassic of southern Brazil
FIGURE 1. A—Stratigraphic column [after Langer et al. (2018)] of the type-locality of both 'Teyuwasu' and Staurikosaurus. Main column depicting the main outcrop of the Alemoa complex with auxiliary column depicting the approximate level that yielded the abovementioned specimens. B—skeletal reconstructions of 'Teyuwasu' and Staurikosaurus depicting known bones in white and unknown in gray. C—Proximal portion of the right femur of 'Teyuwasu' (BSPG AS XXV 53), in cranial view. D—Proximal portion of the left (reversed) femur of Staurikosaurus (MCZ 1669), in cranial view. E—Proximal portion of the left (reversed) femur of Saturnalia (MCP 3944-PV), in cranial view. F—Proximal portion of the right femur of Buriolestes (ULBRA-PVT280), in cranial view. G—Distal end of the right tibia of 'Teyuwasu barberenai' (BSPG AS XXV 54), in distal view. H—Distal end of the left (reversed) tibia of Staurikosaurus (MCZ 1669), in distal view. I—Distal end of the left tibia of Panphagia (PVSJ 874), in distal view. J—Distal end of the right tibia of Macrocollum (CAPPA/UFSM 0001c), in distal view. Abbreviations: at, anterior trochanter; dlt, dorsolateral trochanter; fh, femoral head; plf, posterolateral flange; ts, trochanteric shelf.
ScienceDex guides
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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.