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725 results for “recommendation”
Exploring the Impact of Negative Sampling on Patent Citation Recommendation
<ul> <li> <p><strong>pcr_patents.csv </strong>is the dataset which is generated by collecting samples randomly from Google Patents by exploiting a <a href="https://pypi.org/project/google-patent-scraper/">Python library</a>. The dataset comprises around 250,000 US patents and their titles, abstracts, and citations. Each patent has roughly on average 27 citations.</p> </li> </ul> <p>The zip file contains 3 different datasets for training and testing patent citation recommendation systems. These datasets were generated by utilizing the main dataset. They consist of around 1 million instances which are positive as well as negative samples. </p> <ul> <li> <p><strong>pcr_cpc_negative_sample_data.csv</strong> consists of negative samples that were generated based on CPC subclass codes. </p> </li> <li> <p><strong>pcr_random_negative_sample_data.csv</strong> consists of negative samples that were generated randomly. </p> </li> <li> <p><strong>pcr_sem_sim_negative_sample_data_2.csv</strong> consists of negative samples that were generated based on nearest neighbor relation.</p> </li> </ul>
Consensus recommendations for opioid agonist treatment following the introduction of emergency clinical guidelines in Ireland during the COVID-19 pandemic: A national Delphi study
<p>Anonymous Delphi survey likert scale responses (round 1 (S1-S32) and 2 (R2S1-R2S15)) <a href="https://zenodo.org/api/files/92371fe3-6af6-4044-ba3f-83a26b2fe471/Delphi_likert_responses_ano.csv?versionId=7c3aaa7b-8b43-43da-bfeb-fe644f33d3e2">Delphi_likert_responses_ano.csv</a> and corresponding statements in <a href="https://zenodo.org/api/files/92371fe3-6af6-4044-ba3f-83a26b2fe471/STATEMENTS_REPO.csv?versionId=ada85e7c-ba48-4f61-8e1a-d90dab9b4f05">STATEMENTS_REPO.csv</a>.</p>
Interactive recommendations and action list for technical report from the UKRI Net Zero DRI Scoping Project
<p>The UKRI Net Zero DRI Scoping Project produced evidence-based recommendations. This dataset provides an interactive way to list all of the recommendations and actions from the project. These files have been provided to show the mapping and links between the specific recommendations and the different strategic themes and roadmap actions proposed by the project. </p> <p>Three file formats have been provided for ease of use: Excel, PDF, CSV. The recommendations, if opened in a spreadsheet, can be sorted or filtered based on a range of interest areas e.g. type of evidence, delivery pathway, strategic theme, delivery year. </p> <p>There are two tabs in the spreadsheet. The first tab shows the recommendations discussed in the 'toolkit' section of the project's final technical report. The second tab shows actions discussed in the 'roadmap' section of the report.</p> <p>For full details about the evidence that underpins these recommendations, please see technical report found here: <a href="https://doi.org/10.5281/zenodo.8199984">https://doi.org/10.5281/zenodo.8199984</a> </p> <p>The authors cited here are those who produced the files uploaded. Many other contributors are named in the technical report and is a synthesis of their work. Please also cite the technical report if you use this dataset. </p>
Benchmark Data Repositories: Lessons and Recommendations
<p>Our dataset "repository_survey" summarizes a comprehensive survey of over 150 data repositories, characterizing their metadata documentation and standardization, data curation and validation, and tracking of dataset use in the literature. In addition, "survey_model_evaluation" includes our findings on model evaluation for five benchmark repositories. Column descriptions and further details can be found in "README.pdf." The data are associated with our paper "Benchmark Data Repositories: Lessons and Recommendations." </p>
JusBrasilRec: A large-scale dataset of user sessions for recommendations on the legal domain
<p><strong>JusBrasilRec: A large-scale dataset of user sessions for recommendations on the legal domain</strong></p> <p>The proliferation of legal documents in various formats and their dispersion across multiple courts present a significant challenge for users seeking precise matches to their information requirements. Despite notable advancements in legal information retrieval systems, research into legal recommender systems remains limited. A plausible factor contributing to this scarcity could be the absence of extensive publicly accessible datasets or benchmarks.</p> <p>Jusbrasil (<a href="https://www.jusbrasil.com.br">https://www.jusbrasil.com.br</a>) is known as the largest legal search portal in Brazil. It provides an online environment where users can find the legal documents that best match their information needs. With millions of user interactions to billions of documents containing different artifacts related to law in Brazil, Jusbrasil appears as a large-scale test bed for advancing research on the still scarce area of legal recommender systems. </p> <p>Therefore, we collected and made available the <strong>JusBrasilRec</strong>, a dataset containing user sessions from Jusbrasil for recommendations on the legal domain. Additionally, we also computed and made available a TF-IDF matrix from the textual content of the documents in Jusbrasil. The following files are available for download from JusBrasilRec:</p> <ul> <li><strong>jusbrasilrec_dataset.zip:</strong> a compacted file containing the user sessions;</li> <li><strong>jusbrasilrec_tfidf_matrix.zip:</strong> a compacted file containing the TF-IDF matrix;</li> <li><strong>readme.txt:</strong> a text file explaining the content and format of the previous files.</li> </ul> <p><strong>How to cite the dataset:</strong> Marcos Aurélio Domingues, Edleno Silva de Moura, Leandro Balby Marinho and Altigran da Silva. A Large Scale Benchmark for Session-based Recommendations on the Legal Domain. Artificial Intelligence and Law. 2023.</p>
Data from: Recommendations for assessing earthworm populations in Brazilian ecosystems
Open the record for dataset details and reuse information.
Dataset and R code: Genetic diversity of lion populations in Kenya: evaluating past management practices and recommendations for future conservation actions by Chege M et.al
Open the record for dataset details and reuse information.
Recommendations for quantifying and reducing uncertainty in climate projections of species distributions
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IPBES Data Management Tutorials - Session 4.2: Recommendations for workflow establishment and data management
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>Data management of active research data </em>chapter provides an introduction for IPBES experts on how to manage data while actively being used, analyzed, and produced to fulfill the criteria of the IPBES data management policy.</p> <p>This session,<em> Recommendations for workflow establishment and data management</em>, provides recommendations for data management including the generation of workflows and data storage. </p>
Cooking chicken at home: common or recommended approaches to judge doneness may not assure sufficient inactivation of pathogens
<p>About one third of foodborne illness outbreaks in Europe are acquired in the home and eating undercooked poultry is among consumption practices associated with illness. The aim of this study was to investigate whether actual and recommended practices for monitoring chicken doneness are safe. Seventy-five European households from five European countries were interviewed and videoed while cooking chicken in their private kitchens, including young single men, families with infants/in pregnancy and elderly over seventy years. A cross-national web-survey collected cooking practices for chicken from 3969 households. In a laboratory kitchen, chicken breast fillets were injected with cocktails of Salmonella and Campylobacter and cooked to core temperatures between 55 and 70 °C. Microbial survival in the core and surface of the meat were determined. In a parallel experiment, core colour, colour of juice and texture were recorded. Finally, a range of cooking thermometers from the consumer market were evaluated. The field study identified nine practical approaches for deciding if the chicken was properly cooked. Among these, checking the colour of the meat was commonly used and perceived as a way of mitigating risks among the consumers. Meanwhile, chicken was perceived as hedonically vulnerable to long cooking time. The quantitative survey revealed that households prevalently check cooking status from the inside colour (49.6%) and/or inside texture (39.2%) of the meat. Young men rely more often on the outside colour of the meat (34.7%) and less often on the juices (16.5%) than the elderly (>65 years old; 25.8% and 24.6%, respectively). The lab study showed that colour change of chicken meat happened below 60 °C, corresponding to less than 3 log reduction of Salmonella and Campylobacter. At a core temperature of 70 oC, pathogens survived on the fillet surface not in contact with the frying pan. No correlation between meat texture and microbial inactivation was found. A minority of respondents used a food thermometer, and a challenge with cooking thermometers for home use was long response time. In conclusion, the recommendations from the authorities on monitoring doneness of chicken and current consumer practices do not ensure reduction of pathogens to safe levels. For the domestic cook, determining doneness is both a question of avoiding potential harm and achieving a pleasurable meal. It is discussed how lack of an easy "rule-of-thumb" or tools to check safe cooking at consumer level, as well as national differences in contamination levels, food culture and economy make it difficult to develop international recommendations that are both safe and easily implemented.</p>
Journal recommendations for 4 abstracts
<p>Journal recommendations prepared on results from JANE and whichjournal.com based on 4 abstracts from the disciplines dentistry, psychology and aerosol chemistry.</p> <p>The factsheets with data for each journal should help to decide for the best journal.</p> <p>The data is provided as spreadsheet (xls) and factsheet (pdf).</p>
GUI evaluation data for an IDE command recommender system
<p>This dataset contains results of the study conducted among the participants of the XP 2016 (a scientific conference with a strong participation of practitioners from the industry). The objective of the study was to evaluate the acceptance and usability of the proposed Graphical User Interface (GUI) for an Integrated Development Environment (IDE) command recommender system (RS). The data was collected by the questionnaire and the interviews. The data is anonymized.</p> <p>Content:</p> <ul> <li>README.txt</li> <li>./Survey answers.csv - the questionnaire answers</li> <li>./Interviews <ul> <li>./interviewXXX.txt - a file with a transcribed interview</li> <li>./mapping-codes-to-primary-documents.csv - a binary table summarizing interviews</li> </ul> </li> </ul>
Supplementary Material for the Paper "Design Recommendations for Self-Monitoring in the Workplace: Studies in Software Development"
<p>Contains the supplementary material for the paper "Design Recommendations for Self-Monitoring in the Workplace: Studies in Software Development" submitted to CSCW'18. All contents are explained in the file README.txt.</p> <p>Abstract:<br> One way to improve the productivity of knowledge workers is to increase their self-awareness about productivity at work through self-monitoring. Yet, little is known about expectations of, the experience with and the impact of self-monitoring in the workplace. To address this gap, we studied software developers, as one community of knowledge workers. We used an iterative, feedback-driven development approach (N=20) and a survey (N=413) to infer design elements for workplace self-monitoring, which we then implemented as a technology probe called WorkAnalytics. We field-tested these design elements during a three-week study with software development professionals (N=43). Based on the results of the field study, we present design recommendations for self-monitoring in the workplace, such as using experience sampling to increase the awareness about work and to create richer insights, the need for a large variety of different metrics to retrospect about work, and that actionable insights, enriched with benchmarking data from co-workers, are likely needed to foster productive behavior change at work.</p>
APIBench: A Benchmark Dataset for Evaluating API Recommendation Approaches in Python and Java
<p>APIBench is the benchmark dataset APIBench released in the paper "<a href="https://yunpeng.site/files/apirec.pdf"><em>Revisiting, Benchmarking and Exploring APIRecommendation: How Far Are We?</em></a>". </p> <p>APIBench contains two sub-dataset for evaluating the performance of query-based and code-based API recommendation approaches, namely APIBench-Q and APIBench-C. Each sub-dataset has a Java version and a Python version.</p> <p>APIBench-Q contains 4,309 Python queries and 6,563 Java queries collected from Stack Overflow posts generated from Aug 2008 to Feb 2021 and tutorial websites Geeks4Geeks, Java2s, and Kode Java in April 2021.</p> <p>APIBench-C contains 2,361 Python projects and 1,477 Java projects mined from GitHub in April 2021.</p> <p>Please read the <strong>README.md</strong> file for detailed information about the benchmark.</p> <p>The evaluation results of existing API recommendation approaches can be found in <a href="https://github.com/JohnnyPeng18/APIBench">this GitHub repository</a>.</p>
Educator Perceptions of DevOps Teaching Recommendations and Their Alignment with Common Challenges
<p>DevOps education presents unique pedagogical challenges due to the diversity of tools, rapid technological change, and the multidisciplinary nature of the field. Although previous work has proposed recommendations to address these challenges, it is unclear how educators perceive these recommendations and whether they align with the challenges encountered in practice. In this paper, we present a mixed methods study involving 11 DevOps educators who interacted with Improve, a tool that presents a curated set of educational challenges and recommendations derived from previous literature. Educators indicated which recommendations they already use, which they intend to use, and which challenges they experience and are motivated to address. Our findings show that 22.6% of the recommendations were new to educators and considered potentially useful, while 59.2% were already in use. Additionally, 66.3% of the challenges were considered relevant, with most of them having linked recommendations that educators were already using or willing to adopt. This study provides empirical insights into the perceived usefulness of existing recommendations, identifies gaps in challenge-recommendation mappings, and supports future efforts to design and disseminate more targeted educational guidance for DevOps teaching.</p>
Product Recommendations through Neo4j by Analyzing Patterns in Customer Purchases
<p><span>Recommendation system grows more important each day as user interaction on the internet grows in size and complexity. To achieve better user experience and personalized choice of products for each user, it is important to create a recommendation system that takes all the interaction of a user on the internet and analyzes it thoroughly to get a better understanding of the user. Understanding the user will benefit the business more as each user will get a personalized experience based on how they act. This study focuses on utilizing the graph database to gain insight into the user behavior and develop a recommendation system based on how users act on the internet. The recommender system will use the Neo4j database as it provides much functionality to work with, such as the Graph Data Science library and the Jaccard Similarity method. Using all the graph technologies that exist today, this study will enable businesses to give a personalized experience to user by providing a detailed, accurate, effective, and efficient recommendation to user.</span></p>
MyAnimeList recommendation network
<p>MAL recommendation network extracted in March 2024.</p> <ul> <li>top_animes.jsonl contains the top 2499 animes.</li> <li>recommendations.jsonl contains the recommendations. Note that it is an undirected graph. If (u,v) exists, then (v,u) exits as well.</li> </ul> <p> </p>
Multi-modal User Interactions for Recommendations
<p>This repository contains the data for <a href="https://doi.org/10.1145/3626772.3657881">Dataset and Models for Item Recommendation Using Multi-Modal User Interactions</a>.</p> <p>We publish a real-world dataset from the insurance domain with multi-modal user interactions that can be used in recommendation models. The dataset is anonymized.</p> <p>There are 6 different datasets:</p> <div> <h3><strong>data_users.csv</strong></h3> </div> <p>This data contains the users. Each user has had one or more purchase events with conversations and/or web sessions prior to that purchase. The data contains 5 columns:</p> <ul> <li>user_id. The ID of a user.</li> <li>purchase_event_id. The ID of a purchase event.</li> <li>conversation_id. The ID of a conversation.</li> <li>session_id. The ID of a web session.</li> <li>event_number. A number specifying the order of conversations/web sessions.</li> </ul> <div> <h3><strong>data_conversations_keyword.csv</strong></h3> </div> <p>This data contains the conversations that the user had prior to the user's purchase event. Each conversation consists of multiple sentences represented with keywords. The data contains 4 columns:</p> <ul> <li>conversation_id. The ID of a conversation.</li> <li>sentence_number. A number specifying the order of sentences.</li> <li>sentence_speaker. The speaker of the sentence (user or agent).</li> <li>keywords. List with the IDs of the keywords in the sentence.</li> </ul> <div> <h3><strong>data_conversations_embedding.csv</strong></h3> </div> <p>The data contains the conversations that the user had prior to the user's purchase event. Each conversation consists of multiple sentences represented with text embeddings. The data contains 771 columns:</p> <ul> <li>conversation_id. The ID of a conversation.</li> <li>sentence_number. A number specifying the order of sentences.</li> <li>sentence_speaker. The speaker of the sentence (user or agent).</li> <li>embedding_1 - embedding_768. Text embeddings computed with a pre-trained language-specific BERT model.</li> </ul> <div> <h3><strong>data_sessions.csv</strong></h3> </div> <p>This data contains the web sessions that the user made prior to the user's purchase event. Each web session consists of multiple actions. The data contains 3 columns:</p> <ul> <li>session_id. The ID of a web session.</li> <li>action_number. A number specifying the order of actions.</li> <li>action_tags. List with the IDs of the section, object and type of an action.</li> </ul> <div> <h3><strong>data_purchase_events.csv</strong></h3> </div> <p>This data contains the purchase events. Each event consists of one or more item purchases made by the same user. The data contains 2 columns:</p> <ul> <li>purchase_event_id. The ID of a purchase event.</li> <li>item_id. The ID of an item.</li> </ul> <div> <h3><strong>data_post_filter.csv</strong></h3> </div> <p>This data contains the items that were possible for the user to buy at the time of the user's purchase event. The data contains 2 columns:</p> <ul> <li>purchase_event_id. The ID of a purchase event.</li> <li>item_id. The ID of an item.</li> </ul>
Considering Time and Feature Entropy in Calibrated Recommendations
<p>Dataset and results from the paper "Considering Time and Feature Entropy in Calibrated Recommendations" authored by Diego Corrêa da Silva, Dietmar Jannach and Frederico Araújo Durão.</p>
ICRP Recommendations May Be Fit-For-Purpose, But Without Adequate Human Resources, We Just Won't Get There
<p>In many countries in the world radiation protection faces an acute shortage of human resources. In particular, the medical-physics and radiation-protection professions face an acute shortage of entrants owing to the irregular number of physics/engineering graduates and low popularity of two year masters programmes. Under such conditions ICRP recommendations may of themselves be very much fit-for-purpose, however their implementation in practice is fraught with difficulties and sometimes of a dubious level. A formula needs to be found to: (a) ensure that the potential stock of entrants to the professions would be independent of erratic student numbers in physics/engineering (b) address the paradox of having to reduce the masters programme to one year at a time when the knowledge-skills-competences required for modern medical-physics / radiation-protection practice are expanding rapidly owing to the increasing complexity of medical device technology and clinical protocols. It was considered that the best way forward would be to opt for an undergraduate inter-faculty programme that combined physics and medical physics/radiation protection. The resulting four year programme consists of 5 parallel strands namely physics/mathematics/statistics, medical-physics/radiation-protection, basic-medical-sciences, research and hospital placements. The physics/mathematics/statistics component is sufficiently strong to ensure a strong scientific foundation whilst the medical-physics/radiation-protection component is sufficiently comprehensive to permit the reduction of the Masters in Medical Physics from two years to one. We are pleased to report that the innovative curricular experiment has been a great success. The combination of pure and applied physics, the inter-faculty nature of the programme (where students share lectures with both physics and healthcare professions students) together with the element of clinical practice have been found to be the most attractive features of the programme. The programme has provided a welcome boost for both the medical-physics/radiation-protection professions and indeed even physics itself.</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.