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800 results for “Issues”
Data from: A burning issue: Savanna fire management can generate enough carbon revenue to help restore Africa's rangelands and fill Protected Area funding gaps
<p>Many savanna-dependent species in Africa including large herbivores and apex predators are at increasing risk of extinction. Achieving effective management of protected areas (PAs) in Africa where lions live will cost an estimated USD >$1-2 B/year in new funding. We explored the potential for fire management-based carbon-financing programs to fill this funding gap and benefit degrading savanna ecosystems. We demonstrated how introducing early dry season fire management programs could produce potential carbon revenues (PCR) from either a single carbon-financing method (avoided emissions) or from multiple sequestration methods ranging from USD $59.6-$655.9 M/year (at USD $5/ton) or USD $155.0 M–$1.7 B/year (at USD $13/ton). We highlighted variable but significant PCR for savanna PAs from USD $1.5–$44.4 M/year per PA. We suggest investing in fire management programs to jump-start the United Nations Decade of Ecological Restoration to help restore degraded African savannas and conserve imperiled keystone herbivores and apex predators. <br> <br> Open Access article: <a href="https://doi.org/10.1016/j.oneear.2021.11.013">https://doi.org/10.1016/j.oneear.2021.11.013</a></p>
Research Artefact: An Empirical Study of React-Library Related Issues via Stack Overflow
<p>This research artifact accompanies the paper titled "An Empirical Study of React-Library Related Issues via Stack Overflow." It is a comprehensive repository that includes the collected dataset containing 447,542 React-related Stack Overflow question posts, as well as 384 representative samples obtained randomly. The primary objective of this artifact is to facilitate the replication of our dataset for researchers and allow them to utilize it for further investigations and research purposes.</p>
Online Repository of the Study "I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform
<p><strong>Online Repository of the Study </strong><em>“I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform</em></p> <p><strong>Introduction</strong></p> <p>In the Mobility-as-a-Service (MaaS) context, e-bikes are important and environmental-friendly transportation resources providing flexibility, time and cost savings, and reducing traffic congestion. Additional to user satisfaction and marketing advantages, the resolution of user-reported issues is regulated in many cities. In order to efficiently solve the issues, it is essential to quickly identify their types (e.g., software- or hardware-related?) to assign them to the responsible team. But for popular e-mobility services, the manual analysis of the reports is inefficient because of its tediousness, high time requirements, and error-proneness. </p> <p>Our empirical study, carried out in the context of a <em>Mobility as a Service </em>start-up company, proposes an approach for the automated identification of relevant concerns reported by users of e-bike services. The company has more than 20,000 private customers across seven different countries and dedicates considerable effort in analyzing user behavior. However, the current manual process of analyzing and triaging user-reported issues hinders MaaS-company’s ability to grow and expand its services. </p> <p>To help MaaS providers identify relevant user-reported issues, In the study, we (i) manually inspect about 3,000 user-reported issues received by the MaaS company; (ii) design a taxonomy modeling the types of relevant issues reported by users; and (iii) propose MaaS-RIDE, an approach to automatically classify the user-reported issues according to the categories of the devised taxonomy. </p> <p>Our results demonstrate that MaaS-RIDE is able to accurately (F-measure ≥ 93%) identify software and hardware user-reported issues. This result is critical for e-bike sharing companies to address such issues in an agile way and achieve the required user satisfaction.</p> <p><strong>Dataset Overview</strong></p> <p>The dataset is composed of the following different sorts of data: </p> <ul> <li> “<em>Data_and_preprocessing</em>” folder <ul> <li>o the user-reported issues data</li> <li>o the user-reported issues data processed as Bag of Words for Machine Learning training. <ul> <li>For this look at the sub-folder “<em>input_data_for_ML</em>” and the following matrices: <ul> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em></li> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em></li> </ul> </li> <li>Moreover, a sample of selected issues was reported in the replication package: <ul> <li>see file “<em>randomSamples.csv</em>” (due to a non-disclosure agreement with our industrial partner, we are unauthorized to share the whole raw user reports used in our experiments)</li> <li> “RQ1” folder: Types of E-bikes User-reported Issues</li> </ul> </li> </ul> </li> <li> the resulting taxonomy after the analysis of the issues</li> <li> “RQ2” folder: Classifying E-bikes Issue types</li> <li> the trained models </li> <li> the results of the models</li> </ul> </li> </ul> <p>The following sections describe more in detail what each of those folders and files contain.</p> <p><strong>“Data_and_preprocessing” folder</strong></p> <ul> <li><strong>User-reported issues subset.</strong></li> </ul> <p>In an industrial setting, due to privacy reasons, we disclose only an example subset of the user-reported issues, this information is in the file <em>randomSamples.csv</em>.</p> <p>The <em>randomSamples.csv </em>a subset that was generated randomly adding 20 examples using a stratified sampling from the High-level categories and 20 from the Low-level categories. This subset is not exhaustive but serves the purpose of showing the reviewers the kind of issues that this particular industrial set is confronted with. The file contains:</p> <ul> <li> <ul> <li> the Id of the user report; </li> <li> the column "comment_final"<strong> </strong>contains the issue text after the replacement of information that needed anonymization (e.g., vehicle-plates, personal names, addresses and timestamps); </li> <li> the column "High_level_category" contains the selected category from the 5 first level categories of the presented <em>Three-level taxonomy of e-bike user reported issues</em>; </li> <li>• the columns ‘Low_level_category" and "Fine_grained_topic" contain the assigned, if existing, respective category. </li> </ul> </li> <li><strong>Bag of Words Term by Document matrix.</strong></li> </ul> <p>An important input for training the ML models is the Bag of Words representation generated after processing the 2,989 manually-labeled user issues. The result of this process is a Term-by-Document matrix. We share this matrix in the files in the sub-folder <em>input_data_for_ML </em>where they are labeled for High- and Low-level categories. </p> <p>In the <em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em> and <em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em> files, the first column refers to the issue “Id”, the last column “oracle” is the labeled category, the rest of the columns represent the terms contained in the 2,989 user-reported issues and in each row the weight of the i−𝑡ℎ term contained in the j−𝑡ℎ user issue by using the tf-idf score.</p> <p><strong>“RQ1” folder</strong></p> <ul> <li><strong>“Three-level taxonomy of e-bike user-reported issues.pdf<em>” file</em></strong></li> </ul> <p>The taxonomy derives from the manual analysis of the 2,989 user issues. We found that a three-level taxonomy provides significant granularity to the MaaS-company. The taxonomy encompasses 5 High-level categories, 16 Low-level categories, and 15 Low-level subcategories of e-bike user-reported issues. The file <em>Three-level taxonomy of e-bike user-reported issues.pdf</em> presents the taxonomy categories and in the columns “Nr.” and “%” it shows the number of occurrences within the analyzed dataset, and the corresponding percentages.</p> <p><strong>“RQ2” folder</strong></p> <ul> <li><strong>“Trained Models” folder</strong></li> </ul> <p>We provide the trained machine and deep learning models in the sub-folder <em>ML_DL_models</em>. Our approach experimented with classic machine learning models based on the Bag-of-Words approach using SVM, on Word Embeddings using FastText, and Language models leveraging BERT. The SVM and BERT models were trained using the open source low-code data analytics platform KNIME and were used to classify issues corresponding to the first and second levels of the taxonomy from the “RQ1” folder. A 10-fold cross validation strategy was used to assess the classification performance. </p> <p>The fastText model was trained by using default values of parameters (https://fasttext.cc/docs/en/options.html) and a 10-fold cross-validation strategy. With fastText, we classified issues corresponding only to the first level of the taxonomy from “RQ1” folder, since fastText is more effective when more data points are available in the training set (i.e., lower levels in the taxonomy have fewer well-represented issue types).</p> <ul> <li><strong>“Model results” folder</strong></li> </ul> <p>In the sub-folder model_results we provide the tables summarizing the results of using the proposed MaaS-RIDE approach, with which we automatically identify and categorize user-reported issues according to the High-level and Low-level categories of the taxonomy devised in RQ1, which are relevant for the MaaS-company. </p>
Dataset and Puppet Issues Mining
<p>Mining Puppet and Dataset</p>
Data used in the paper A Taxonomy of Testable HTML5 Canvas Issues
<p>This repository contains data used in the paper <code>A Taxonomy of Testable HTML5 Canvas Issues</code>, submitted to IEEE Transactions on Software Engineering.</p> <p><strong>Data description:</strong></p> <table> <thead> <tr> <th>file name</th> <th>description</th> <th>fields</th> </tr> </thead> <tbody> <tr> <td>extracted_projects.csv</td> <td>180 open-source <canvas> projects (extracted from GitHub)</td> <td>nameWithOwner</td> </tr> <tr> <td>extracted_bug_reports.csv</td> <td>2,403 <canvas> issue reports (extracted from the 180 GitHub projects)</td> <td>url</td> </tr> <tr> <td>classified_bug_reports.csv</td> <td>332 classified issue reports (sampled from extracted_bug_reports.csv)</td> <td>url, type</td> </tr> </tbody> </table>
Technical Debt Classification in Issue Trackers using Natural Language Processing based on Transformers
<p>In order to ensure transparency and reproducibility, we have made everything available publicly here, including the Code, Models, Datasets and more. All the files and their functionality used in this paper are explained clearly in the <strong>README.md</strong> file.</p> <p>Background: Technical Debt (TD) needs to be controlled and tracked during software development. Support to automatically track TD in issue trackers is limited. </p> <p>Aim: We explore the usage of a large dataset of developer-labeled TD issues in combination with cutting-edge Natural Language Processing (NLP) approaches to automatically classify TD in issue trackers.</p> <p>Method: We mine and analyze more than 160GB of textual data from GitHub projects, collecting over 55,600 TD issues and consolidating them into a large dataset (GTD dataset). We use such datasets to train and test Transformer ML models. Then we test the model's generalization ability by testing them on six unseen projects. Finally, we re-train the models including part of the TD issues from the target project to test their adaptability. </p> <p>Results and Conclusion: (i) We create and release the GTD dataset, a comprehensive dataset including TD issues from 6,401 public repositories with various contexts; (ii) By training Transformers using the GTD dataset, we achieve performance metrics that are promising; (iii) Our results are a significant step forward towards supporting the automatic classification of TD in issue trackers, especially when the models are adapted to the context of unseen projects after fine-tuning.</p>
Data sets for the Simulated AMPI (SAMPI) load balancing simulation workflow and Ondes3D performance analysis (Companion to CCPE - Euro-Par 2017 special issue)
<p>This package contains data sets and scripts (in an Org-mode file) related to our submission to the special Euro-Par 2017 issue of the journal "Concurrency and Computation: Practice and Experience", under the title "Performance Modeling of a Geophysics Application to Accelerate Over-decomposition Parameter Tuning through Simulation".</p>
State of Practice and Challenges of Issue Management for Component-based and Service-Oriented Systems: An Empirical Study
<p>Modern software systems often consist of independently developed components, e.g., libraries or (micro-)services, that can be combined to form a larger architecture. Many of these components are developed externally, i.e., in other projects by other teams, which is particularly the case in service-oriented architectures. Even though the components are independently developed, bugs may arise that propagate along the call chains, which sometimes negatively affect the calling components. However, as such components are typically managed in separate issue management systems (IMS), identifying that a downstream component is affected by an upstream component's issues is complex and time-consuming. Even though some scientific works and IMS forums discuss how to manage such cross-component issues, there is no systematic study on the state of practice and the challenges of issue management when different components are affected. Therefore, this study aims to determine which types of components are usually included in modern software projects, how developers identify and manage issue propagation between components, and how often they face such propagations. To get these insights, we conducted an empirical study through a questionnaire with 64 industry experts and open-source contributors and interviewed seven of them. Our key takeaways help researchers and software engineers to understand possible impacts and to improve cross-component issue management. We identified, for example, that related issues currently are non-semantically linked via URLs in issue comments.</p>
Code4Lib Journal, Issue 55
The lastest issue of Code4Lib Journal came out yesterday, and I wanted to see how quickly I could garner insights regarding the issue's themes, topics, and questions addressed. I was able to satisfy my curiosity about these self-imposed challenges, but ironically, it took me longer to write this blog posting than it did for me to do the analysis.
Job-Related Health Issues That Affect Employees Working in Pharmaceutical Marketing
<p><strong><span>Abstract</span></strong></p> <p><span>Marketing representatives play a critical role in a pharmaceutical organization in the development and sustainability of their business through helping in selling of products and services. It is not an easy job. The Medical Marketing representative is a high-risk job with immense stress and negative consequences for individuals. It requires more skills, wider knowledge and emotional stability than the other profession. Due to extensive traveling, wandering and waiting time, target issues, work-life balancing problems and lack of job security the medical representative feels exhausted.</span></p> <p><span>In India, the pharmaceutical industry is growing tremendously for the past few years. This industry is highly competitive in nature. It increases the need for the marketing representatives and also their roles and responsibilities simultaneously. The cut-throat competitive scenario in the market increases the pressure of achieving targets to pharmaceutical sales which ultimately induced their job stress and other health issues. So, the pharmacy institution must provide more attention to prevent and reduce the burnout of the marketing representatives, otherwise the institution will lose its reputation.</span></p> <p><strong><span>Keywords: </span></strong><span>occupational risks; pharmaceutical marketing force; road traffic accidents; violence; workplace stress; Burnout; Frustrating; Medical Representatives; Stress and Work-Life Balancing Problems; stress management Violence; Well-being.</span></p>
Environmental issues of software & its labelling: questionnaire and survey data
<p>Questionnaire (original one in German, translated into English) and the survey data of my survey on environmental issues of software and its labelling, conducted in August to October 2016 (doctoral studies).</p>
Copper-plates issued by Govinda Prabhūtavarṣa of the Rāṣṭrakūṭa dynasty, Saka year 726.
<p>Inked impression of the copper-plates issued by Govinda Prabhūtavarṣa of the Rāṣṭrakūṭa dynasty, Saka year 726. British Museum 1969, 0401, 0.3. The original plates in the British Library.</p>
Copper-plates issued by Govinda Prabhūtavarṣa of the Rāṣṭrakūṭa dynasty, Saka year 726
<p>Inked impression of the copper-plates issued by Govinda Prabhūtavarṣa of the Rāṣṭrakūṭa dynasty, Saka year 726. British Museum 1969, 0401, 0.3. The original plates in the British Library.</p>
API Database of Python frameworks & Labeled Issues
<p>PyLibAPIs.7z : contains public API data (MongoDB dump) for these frameworks:</p> <ul> <li>TensorFlow</li> <li>Keras</li> <li>Scikit-learn</li> <li>Pandas</li> <li>Flask</li> <li>Django</li> </ul> <p>Label.xlsx: contains issues and their labels</p> <p>Breaking Changes for All Frameworks.pdf: contains the breaking change distributions of all six frameworks</p>
FIGURE 4 in Overcoming sampling issues in dental tribology: Insights from an experimentation on sheep
FIGURE 4. Mean and standard error of the mean for complexity (Asfc) and anisotropy (epLsar) of the dental microwear textures for each ewe sample and for each molar of the tooth row.
FIGURE 3 in Overcoming sampling issues in dental tribology: Insights from an experimentation on sheep
FIGURE 3. Mean and standard error of the mean for complexity (Asfc) and anisotropy (epLsar) of the dental microwear textures for dental facets from upper and lower molars and for each dietary sample. 1, Comparison of the disto-labial protoconid facet of the second lower molar and the mesio-lingual paracone facet of the second upper molar. 2, Comparison of the disto-labial protoconid facet of the second lower molar and mesio-lingual protocone facet of the second upper molar.
FIGURE 5 in Overcoming sampling issues in dental tribology: Insights from an experimentation on sheep
FIGURE 5. Mean and standard error of the mean for complexity (Asfc) and anisotropy (epLsar) of the dental microwear textures for the simulations of fossil data using random sampling of the ewe dataset. Ellipses represent 95% of the means for each iteration. Mean and standard error of the mean for each sample (lower second molars) are also represented for comparison. 1, Simulation with 10 individuals per dietary category and 1000 iterations. 2, Simulation with 20 individuals per dietary category and 1000 iterations. 3, Simulation with 30 individuals per dietary category and 1000 iterations.
Replication Package for "PyTraceBERT: Python Traceback-based Language Model for Detecting Compatibility Issues in Deep Learning Systems"
<p>This package contains the traceback data, pre-trained models, and static word embeddings used in the paper, PyTraceBERT: Python Traceback-based Language Model for Detecting Compatibility Issues in Deep Learning Systems.</p>
ISSUES OF FINANCIAL PROVISION OF PRISON INSTITUTIONS IN RUSSIA IN THE EARLY XX CENTURY
<p><span>The article deals with the issues of material provision of prisons in the early XX century. It is an interesting historical fact that prisoners brought a certain income to the treasury by performing various types of socially useful work, which was encouraged by the authorities. A similar practice existed in the early twentieth century abroad, for example, in Switzerland, Great Britain, and the USA, where prisoners' labor was also actively used, bringing money to the state. In fact, these financial resources were used partly for the needs of places of deprivation of liberty, which contributed to their self-sufficiency. The Tsarist government in Russia rightly believed that, from a financial point of view, it was more profitable to force prisoners to work and bring income than to simply keep the entire prison population "under lock and key". In addition, later, after 1917 in Russia, labor re-education of prisoners was actively used during the years of Soviet power. At present, the Russian government is attempting to organize the so-called "public institute of probation" in order to activate labor employment of convicts. </span></p>
Data underlying the research paper "Articulating Social Issues with Open Data: Exploring a Game Jam Approach"
<p>Contains research data underlying the following research paper:</p> <blockquote> <p>Davide Di Staso, Lærke Christiansen, Fernando Kleiman, and Marijn Janssen. 2024. Articulating Social Issues with Open Data: Exploring a Game Jam Approach. In Proceedings of the 8th International Conference on Game Jams, Hackathons and Game Creation Events (ICGJ ’24), October 11, 2024, Copenhagen, Denmark. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3697789.3697798</p> </blockquote> <p>The authors acknowledge the financial support from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 955569, "Towards a sustainable Open Data ECOsystem" (ODECO).</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.