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725 results for “Recommendation”
Replication Package for MORCoRA: Multi-Objective Refactoring Recommendation Considering Review Availability
<div> <div>This is the replication package for <em>MORCoRA: Multi-Objective Refactoring Recommendation Considering Review Availability</em></div> <br><br> <div> <p><strong>Refactoring sequences searched by MORCoRA</strong></p> </div> <div>The 6 directories contain the refactoring sequences searched by MORCoRA.</div> <div>The name of each directory is the name of the repository introduced in <em>Table 2. Dataset</em>.</div> <br> <div>Each directory includes 6 CSV files.</div> <div>The name of the CSV file represents the search algorithm used to search refactoring sequences.</div> <br> <div>Note that <em>NsgaiiN</em> represents using the NSGA-II algorithm without considering the review availability objective, which is the RA- in <em>Section 4.5</em>.</div> <br> <div>In each CSV file, a single row is the refactoring sequences searched and the recommended reviewers for it. The sequence with no appropriate reviewers will be noted as <em>No appropriate expertise reviewer</em>.</div> <br><br> <div>In each CSV file, a row consists of multiple columns, the last column represents the recommended reviewer, and the rest columns represent elements in the refactoring sequences.</div> <div>Each element consists of:</div> <div> <ul> <li>ROType: refactoring operation type</li> </ul> </div> <div> <ul> <li>class1info: <em>class1</em> in the <em>Table 1. Refactoring Operations</em> in the paper. It is the information of the source class where the number before <em>#</em> represents the access modifiers according to <a href="https://docs.oracle.com/javase/7/docs/api/constant-values.html#java.lang.reflect.Modifier.PROTECTED">modifiers and their corresponding int values</a>, the name after <em>#</em> represents the name for the class</li> </ul> </div> <div> <ul> <li>class1path: the path to the file containing the class</li> </ul> </div> <div> <ul> <li>class2info: <em>class2</em> in the <em>Table 1. Refactoring Operations</em> the formation of the target class</li> </ul> </div> <div> <ul> <li>class2path: the path to the file containing the class</li> </ul> </div> <div> <ul> <li>target: it can be "class" or "method" or "field" according to the refactoring type. The number before <em>#</em> represents the access modifier. The name after <em>#</em> is the name of the "class" or "method" or "field", and its type is revealed after the <em>@</em> symbol.</li> </ul> </div> <br><br> <div><strong>Manually review results</strong></div> <div>The manual review results of the 60 solutions introduced in <em>Section 4.3</em> is recorded in the <em>manually_review_60_solutions.csv</em></div> <br> <div>It includes 6 columns:</div> <div> <ul> <li>Repository: The name of the repository.</li> </ul> </div> <div> <ul> <li>Recommended Refactoring Operations: the searched refactoring sequence.</li> </ul> </div> <div> <ul> <li>Recommended Reviewer: the reviewer recommended to review the refactoring sequence.</li> </ul> </div> <div> <ul> <li>Reviewable: If no appropriate expertise reviewer is found for the sequence, the value is "0", otherwise "1".</li> </ul> </div> <div> <ul> <li>Code smell Eliminated: The code smell type detected by JDeodorant that the recommended refactoring can eliminate. If the refactoring sequence cannot eliminate any code smell, then it is "No".</li> </ul> </div> <div> <ul> <li>Valid: If the refactoring sequence is recommended with appropriate reviewer (value in the column "Reviewable" is "1") and meaningful, and eliminates at least one code smell, the value is "1", otherwise "0".</li> </ul> </div> </div>
Data Science Tasks used in "AI Support for Data Scientists: An Empirical Study on Workflow and Alternative Code Recommendation"
<p>This entry contains the supplementary files for a scientific article. </p> <p>The dataset contains the necessary files for the two data science tasks used in the experiment study from scientific article "AI Support for Data Scientists: An Empirical Study on Workflow and Alternative Code Recommendation"</p> <p> </p>
Resources for BMF CP 83: Information seeking, recommendation mechanism, and space tourism intention
<p><span>The current study is conducted to examine the following research questions:</span></p> <ul> <li><span>What information sources on social media are associated with the general intention to try space tourism?</span></li> <li><span>Does the automatically recommended information moderate the associations between multiple sources of information and the intention to try space tourism?</span></li> </ul>
Data set for "EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study"
<p>Dataset has been created for "EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study" paper.</p> <p>We have created a smartphone based behaviour monitoring and recommendation system to aid patients recruited in a weight loss intervention programme.<br>The main interaction element for the patients is <strong>EghiFit</strong> application which was used in context of this dataset for data acquisition and secure transmission to our servers.</p> <p>The data consists of application usage per patient, steps achieved, nutritional information of meals logged, heart rate data, interaction data and more.<br>For more information about the dataset, please take a look at <strong>readme.md</strong> file and our paper.</p>
Mentor Recommendation
<h2>Dataset Description: IT Mentor-Mentee Matching Dataset</h2> <h3>Overview</h3> <p>The <strong>IT Mentor-Mentee Matching Dataset</strong> is a comprehensive collection of profiles designed to facilitate mentor-mentee relationships within the information technology sector. This dataset is based on information gathered from a nonprofit organization in the U.S. that focuses on mentorship initiatives aimed at empowering individuals in the IT field.</p> <h3>Structure</h3> <p>The dataset comprises <strong>5,000 rows</strong> of profiles, each characterized by the following columns:</p> <ul> <li><strong>User_ID</strong>: Unique identifier for each mentor or mentee.</li> <li><strong>Position</strong>: The job title (e.g., Front-End Developer, Data Scientist) relevant to the user.</li> <li><strong>Experience_Level</strong>: A categorical variable indicating the user’s level of experience (e.g., Entry, Mid, Senior).</li> <li><strong>Years_of_Experience</strong>: Numeric representation of the years spent in the industry.</li> <li><strong>Primary_Language</strong>: The primary programming or scripting language the user is proficient in (e.g., Python, JavaScript).</li> <li><strong>Secondary_Languages</strong>: A list of additional programming languages the user is familiar with.</li> <li><strong>Expert_Roles</strong>: Key areas of expertise within the user’s field (e.g., React, Machine Learning).</li> <li><strong>Industry</strong>: The industry context in which the user operates (e.g., Finance, Healthcare, Education).</li> <li><strong>Education</strong>: The highest level of education attained by the user.</li> <li><strong>Availability</strong>: The user’s availability for mentoring or mentoring sessions (e.g., Part-time, Full-time).</li> </ul> <h3>Purpose</h3> <p>This dataset aims to support research and development in mentor-mentee matching systems, enabling organizations, educational institutions, and individual professionals to foster mentorship opportunities that enhance skills and career development within the IT industry. The dataset serves as a valuable resource for various stakeholders seeking to promote growth and knowledge sharing in the tech community.</p> <h3>Use Cases</h3> <ul> <li><strong>Mentor-Mentee Matching</strong>: Create algorithms to effectively match mentors with mentees based on skills, experience, and availability.</li> <li><strong>Collaborative Filtering</strong>: Implement machine learning models that predict compatibility scores between potential mentors and mentees.</li> <li><strong>Data Analysis</strong>: Conduct exploratory data analysis to uncover trends in skills and experience levels within the IT workforce.</li> </ul>
Datensatz für BA: Entwicklung eines Recommender-Systems für die Zuordnung von Anforderungen zu IT-Services
<ul> <li> <p><strong>afos_03_promise_nrf.csv</strong>: Diese Datei enthält funktionale Anforderungen aus dem PROMISE-Datensatz. Die Abkürzung „nrf“ könnte sich auf spezifische Notwendigkeiten oder Funktionen innerhalb der PROMISE-Daten beziehen. Sie dient als Quelle für Anforderungen, die im Recommender-System genutzt werden können.</p> </li> <li> <p><strong>afos_tcs_sampled.csv</strong>: Diese Datei enthält eine Stichprobe von funktionalen Anforderungen aus einem zusätzlichen Quellenbestand oder einem Datensatz mit technischen Anforderungen. Diese Anforderungen ergänzen den PROMISE-Datensatz und erweitern die Datenbasis für die Entwicklung und das Training des Recommender-Systems.</p> </li> <li> <p><strong>Goldstandard_256.csv</strong>: Diese Datei bildet den Goldstandard für das Recommender-System und enthält 256 funktionale Anforderungen, die manuell den entsprechenden IT-Services zugeordnet wurden. Sie dient als Referenz zur Bewertung der Qualität des Recommender-Systems und ermöglicht die Validierung und Optimierung des Modells.</p> </li> <li> <p><strong>ITSM_Set_with_Descriptions.xlsx</strong>: Diese Datei enthält eine Sammlung von IT-Services mit detaillierten Beschreibungen. Die Tabelle umfasst Kategorien und Unterkategorien der IT-Services, die als Basis für die Empfehlungen des Recommender-Systems dienen. Diese Beschreibungen ermöglichen eine semantische Analyse und unterstützen das Modell bei der Zuordnung zu passenden Anforderungen.</p> </li> <li> <p><strong>Recommender_System.ipynb</strong>: Dies ist ein Jupyter-Notebook, das den Code zur Entwicklung, Training und Evaluierung des Recommender-Systems enthält. Das Notebook fasst die Implementierung der Empfehlungslogik zusammen und stellt die Grundlage für die experimentellen Auswertungen dar.</p> </li> <li> <p><strong>requirements.txt</strong>: Diese Datei listet die Python-Bibliotheken und deren Versionen auf, die für das Recommender-System benötigt werden. Sie stellt sicher, dass alle benötigten Abhängigkeiten für die erfolgreiche Ausführung des Codes installiert sind.</p> </li> </ul>
Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Open Access, Open Data, and Open Science
<p>This Table sets out findings from the mapping exercise conducted as part of the objectives of subtask 6.1 of the RRING project.</p> <p>Aim: Alignment of RRI to advance the UN SDGs.</p> <p>Objectives:</p> <ul> <li>Mapping the RSSR to the SDGs </li> </ul> <p>Mapping the RSSR to the SDGs is aimed at providing new perspectives, ideas and approaches that can help to improve the operationalization and implementation of each SDG, <em>by facilitating the integration of RRI (or RRI-like) practices in the SDGs, to make them more achievable.</em> The impact of the new perspectives, ideas and approaches in SDG operationalization and implementation will be aimed at the level of <em>national and international policy (making); future research and innovation projects (in industry and academia); as well as education and training of researchers, policy makers and other stakeholders.</em></p> <p>Two documents were used for this task:</p> <ul> <li>2017 Recommendation on Science and Scientific Researchers ([RSSR], UNESCO), and</li> <li>the United Nations 2030 Agenda for Sustainable Development with the 17 Sustainable Development Goals (SDGs).</li> </ul>
Dataset used for "A Recommender System of Buggy App Checkers for App Store Moderators"
<p>This is the dataset used for paper: "A Recommender System of Buggy App Checkers for App Store Moderators", published on the <em>International Conference on Mobile Software Engineering and Systems (MOBILESoft)</em> in 2015.<br> <br> <strong>Dataset Collection</strong><br> We built a dataset that consists of a random sample of <em><strong>Android app metadata</strong></em> and <em><strong>user reviews</strong></em> available on the <em>Google Play Store</em> on January and March 2014.<br> Since the Google Play Store is continuously evolving (adding, removing and/or updating apps), we updated the dataset twice.<br> The dataset D1 contains available apps in the Google Play Store in January 2014.<br> Then, we created a new snapshot (D2) of the Google Play Store in March 2014.<br> <br> The apps belong to the 27 different categories defined by Google (at the time of writing the paper), and the 4 predefined subcategories (free, paid, new_free, and new_paid). For each category-subcategory pair (e.g. tools-free, tools-paid, sports-new_free, etc.), we collected a maximum of 500 samples, resulting in a median number of 1.978 apps per category.</p> <p>For each app, we retrieved the following metadata: <em>name, package, creator, version code, version name, number of downloads, size, upload date, star rating, star counting</em>, and the set of <em>permission requests</em>.<br> <br> In addition, for each app, we collected up to a maximum of the latest 500 reviews posted by users in the Google Play Store. For each review, we retrieved its metadata:<em> title, description, device,</em> and <em>version</em> of the app. None of these fields were mandatory, thus<br> several reviews lack some of these details.<br> From all the reviews attached to an app, we only considered the reviews associated with the latest version of the app —i.e., we discarded unversioned and old-versioned reviews. Thus, resulting in a corpus of 1,402,717 reviews (2014 Jan.).</p> <p> </p> <p><strong>Dataset Stats</strong><br> Some stats about the datasets:</p> <p>- <strong>D1</strong> (<em>Jan. 2014</em>) contains 38,781 apps requesting 7,826 different permissions, and 1,402,717 user reviews.</p> <p>- <strong>D2</strong> (<em>Mar. 2014</em>) contains 46,644 apps and 9,319 different permission requests, and 1,361,319 user reviews.</p> <p>Additional stats about the datasets are available <a href="https://sites.google.com/site/androidbuggyappcheckers">here</a>.<br> <br> <br> <strong>Dataset Description</strong><br> To store the dataset, we created a graph database with <a href="https://neo4j.com/">Neo4j</a>. This dataset therefore consists of a graph describing the apps as nodes and edges. We chose a graph database because the graph visualization helps to identify connections among data (e.g.,<br> clusters of apps sharing similar sets of permission requests).<br> <br> In particular, our dataset graph contains six <em>types of nodes</em>:<br> - <em>APP</em> nodes containing metadata of each app,<br> - <em>PERMISSION</em> nodes describing permission types,<br> - <em>CATEGORY</em> nodes describing app categories,<br> - <em>SUBCATEGORY</em> nodes describing app subcategories,<br> - <em>USER_REVIEW</em> nodes storing user reviews.<br> - <em>TOPIC</em> topics mined from user reviews (using LDA).<br> <br> Furthermore, there are five <em>types of relationships</em> between APP nodes and each of the remaining nodes:</p> <p>- <em>USES_PERMISSION</em> relationships between APP and PERMISSION nodes<br> - <em>HAS_REVIEW</em> between APP and USER_REVIEW nodes<br> - <em>HAS_TOPIC</em> between USER_REVIEW and TOPIC nodes<br> - <em>BELONGS_TO_CATEGORY</em> between APP and CATEGORY nodes<br> - <em>BELONGS_TO_SUBCATEGORY</em> between APP and SUBCATEGORY nodes</p> <p><br> <strong>Dataset Files Info</strong></p> <ul> <li><strong>Neo4j 2.0 Databases</strong> <ul> <li><em><strong>googlePlayDB1-Jan2014_neo4j_2_0.rar</strong></em></li> <li><em><strong>googlePlayDB2-Mar2014_neo4j_2_0.rar</strong></em><br> We provide two Neo4j databases containing the 2 snapshots of the Google Play Store (January and March 2014). These are the original databases created for the paper. The databases were created with <strong>Neo4j 2.0. </strong>In particular with the tool version <em>'Neo4j 2.0.0-M06 Community Edition' (latest version available at the time of implementing the paper in 2014).</em><br> </li> </ul> </li> <li><strong>Neo4j 3.5 Databases</strong> <ul> <li><strong>googlePlayDB1-Jan2014_neo4j_3_5_28.rar</strong></li> <li><strong>googlePlayDB2-Mar2014_neo4j_3_5_28.rar</strong><br> Currently, the version Neo4j 2.0 is deprecated and it is not available for download in the official <em>Neo4j Download Center</em>. We have migrated the original databases (Neo4j 2.0) to Neo4j 3.5.28.<br> The databases can be opened with the tool version: <em>'Neo4j Community Edition 3.5.28'.<br> The tool can be downloaded from the official </em><a href="https://neo4j.com/download-center/#community">Neo4j Donwload</a> page.<br> <br> In order to open the databases with more recent versions of Neo4j, the databases must be first migrated to the corresponding version. Instructions about the migration process can be found in the <a href="https://neo4j.com/docs/upgrade-migration-guide/current/understanding-upgrades-migration/">Neo4j Migration Guide</a>.<br> <br> First time the Neo4j database is connected, it could request credentials. The username and pasword are: neo4j/neo4j <br> <br> </li> </ul> </li> </ul> <p> </p>
Recommendations for molecules of interest in the Taurus Molecular Cloud-1
<p>This dataset contains 1510 molecules of interest to the chemical inventory of TMC-1, recommended based on grounds of chemical similarity. These molecules were identified as part of work currently under review:</p> <p>Lee et al., "Machine Learning of Interstellar Chemical Inventories" (2021)</p> <p>Structures were generated from their SMILES strings using OpenBabel and rdkit, and geometry optimization carried out using the geomeTRIC program:</p> <p><br> Wang, L.‑P.; Song, C.C. (2016), J. Chem, Phys. 144, 214108. http://dx.doi.org/10.1063/1.4952956</p> <p><br> Electronic structure calculations were performed using psi4, with both geometry optimization and dipole moments calculated at at the 𝜔B97X‑D/6‑31+G(d) level of theory. Equilibrium dipole moments<br> and rotational constants are reported in unsigned debye and MHz respectively; for the latter, we provide effective scaled parameters as well that empirically correct for vibration‑rotation interactions. Please refer to “Lee, K. L. K. and McCarthy, M. 2020, J Phys Chem A, 5, 898” for information regarding their uncertainties. For molecules where SCF/geometry optimizations failed to converge, we provide their dipole moments based on the molecular mechanics structures. These molecules will be indicated by “Is DFT optimized?: False”.</p> <p><br> The predicted column densities and uncertainties are given with a simple Gaussian Process with rational quadratic and white noise kernels. Simply put, the predicted column densities of unseen molecules are given as functions of distance in chemical space that decays naturally to zero for infinite distance from other data points. The reader is encouraged to look at the distances between recommendations and TMC‑1 molecules to develop an intuition for how the predicted column density behaves roughly with distance, and interpret them with the uncertainties accordingly: as a guide but not to rule out molecules specifically. Molecules with particularly large uncertainties are<br> likely to be impactful in constraining the chemistry of the source, even if we provide just an upper limit.</p> <p><br> Finally, there is no real ordering of which the molecules are given. This is quasi‑random, although there are pockets of similar molecules based on how similar the TMC‑1 molecules are between searches.</p> <p> </p> <p>The included tarball contains the OpenBabel generated XYZ coordinates for each recommended species. This <a href="https://github.com/laserkelvin/umda">github repository</a> contains the notebooks and code used to generate this data.</p>
PProx: Efficient Privacy for Recommendation-as-a-Service (datasets)
<p>Result datasets for the Middleware'21 paper PProx: Efficient Privacy for Recommendation-as-a-Service. Also available on <a href="https://github.com/CloudLargeScale-UCLouvain/PProx">https://github.com/CloudLargeScale-UCLouvain/PProx</a> (paper directory).</p>
Replication Package for the Paper: "Code Reviewer Recommendation for Architecture Violations: An Exploratory Study"
<p>This is the replication package for the paper: "Code Reviewer Recommendation for Architecture Violations: An Exploratory Study".</p> <p><strong>1) scripts.zip </strong>includes the Python scripts used to run the experiments in this work. Experimental details (e.g., parameters) are described in the Python files. Choose the relevant experimental settings and run "Experiment.py" to start the experiments.</p> <p><strong>2) dataset.xlsx </strong>is the dataset used in the experiments on code reviewer recommendation, which includes the code review comments (from the four OSS projects) related to architecture violations and the file paths of code changes.</p>
Country uptake of WHO recommendations on differentiated HIV testing services approaches: A global policy review
<p><strong>Objectives </strong></p> <p>In 2015 and 2016, the World Health Organization (WHO) issued guidelines on HIV testing services (HTS) highlighting recommendations for a strategic mix of differentiated HTS approaches. The Policy review examines the uptake of differentiated HIV Testing service (HTS) approaches recommendations.</p> <p><strong>Methods </strong></p> <p>Data were extracted from all available national policies published between January 2015 and June 2019 and stored in WHO's national policy repository. WHO recommended as HTS approaches facility-based testing, community-based testing, HIV self-testing and provider-assisted referral (or assisted partner notification); in terms of testing components: pre-test information, lay provider testing and rapid testing. Descriptive analyses were conducted to examine availability of policies and adherence to WHO-differentiated HTS recommendations.</p> <p><strong>Results</strong></p> <p>Of 194 countries worldwide, 65 published policies were identified in the review period. 24 were from the African region (51% of African countries, 24/47), six from the Eastern Mediterranean region (29%, 6/21), 21 from the European region (40%, 21/53), five from the American region (14%, 5/35), four from the South East Asia region (36%, 4/11) and five from the Western Pacific Region (19%, 5/27). Only five countries were compliant with recommendations and 63 included at least one. 85% (n=55) included facility-based testing for pregnant women, 75% (n=49) facility-based testing for key populations, 74% (n=48) community-based testing for key populations, 38% (n=25) HIV self-testing, 25% (n=16) provider-assisted referral, 69% (n=45) rapid testing, 57% (n=37) post-test counselling, 45% (n=29) lay provider testing and 29% (n=19) pre-test information. The highest uptake of WHO recommendations was seen in countries from the African and Eastern Mediterranean region.</p> <p><strong>Conclusions</strong></p> <p>There was substantial variability in the uptake of WHO HTS recommendations, ranging from 25% to 85%. Uptake was above 50% for facility-based testing for pregnant women and key populations, community-based testing, rapid diagnostic testing and post-test counselling; uptake was between 25% and 45% for all the other recommendations.</p>
Data for: Defining the relationship between phylogeny, clinical manifestation and phenotype for Trichophyton mentagrophytes/interdigitale complex; a literature review and taxonomic recommendations
<p><span>This study looked for correlations between molecular identification, clinical manifestation and morphology for <em>Trichophyton interdigitale</em> and <em>T. mentagrophytes</em>. For this purpose, a total of 110 isolates were obtained from Czech patients with various clinical manifestations of dermatophytosis. Micro- and macromorphology and physiology were analysed, and the strains were characterized using multilocus sequence typing. Among the 12 measured/</span><span>scored phenotypic features, statistically significant differences between species were found only in growth rates at 37°C and in</span><span> the</span><span> production of spiral hyphae but none of these features was diagnostic. </span><span>Correlations were found between <em>T. interdigitale</em> and higher age of patients and between clinical manifestations such as tinea pedis or onychomychosis.</span><span> </span><span>The MLST approach showed that ITS genotyping of <em>T. mentagrophytes</em> isolates has limited practical benefits because of extensive gene flow between sublineages. Based on our results and previous studies, there are few taxonomic arguments for preserving both species' names. The species show a lack of monophyly and unique morphology. On the other hand, some genotypes are associated with predominant clinical </span><span>manifestations and sources</span><span> of infections</span><span>,</span><span> which keep those names alive. This practice is questionable because the use of both names confuses identification</span><span>,</span><span> leading to difficulty in comparing epidemiological studies. The current identification method using ITS genotyping is ambiguous for some isolates and is not user-friendly. Additionally, identification tools such as MALDI-TOF MS fail to distinguish these species. To avoid further confusion and simplify identification in practice, we recommend using the name <em>T. mentagrophytes</em> for the entire complex. When clear differentiation of populations corresponding to <em>T. interdigitale</em> and <em>T. indotineae</em> is possible based on molecular data, we recommend optionally using a variety rank:<em> T. mentagrophytes</em> var. <em>interdigitale</em> and <em>T. mentagrophytes</em> var. <em>indotineae</em>.</span></p>
User Unfairness Mitigation by Graph Data Augmentation in Recommendation
<p>Dataset for the paper submission `User Unfairness Mitigation by Graph Data Augmentation in Recommendation`.</p> <p>The included datasets are: MovieLens-1M, Last.FM 1K.</p> <p> </p>
[DATASET] Supporting Open Science Hardware in Academia: Policy Recommendations for Science Funders and University Managers
<p>Raw data from a real-time Delphi exercise used to write the policy brief: <a href="https://zenodo.org/record/8030029">Supporting Open Science Hardware in Academia: Policy Recommendations for Science Funders and University Managers</a></p> <p>Access to the Real-Time Delphi platform was kindly provided by <a href="https://4strat.de">4strat</a>. See tab "meta - start here" for description of the dataset.</p>
Ethical recommendations for working with photovoice and gender related violence
<p>In this video, Barbara Biglia (URV), researcher and lecturer at the Pedagogy Department, shares some essential recommendations for working with visual methodologies like photovoice and gender related violence. Presentation elaborated by Rocío Garrido (US) and Alejandra Araiza (UAEH) in the framework of the Project Mainstreaming Sexual and Gender-Related Violence sensibilities into university courses through Photovoice experiences (2020 INDOV 00003).</p> <p>Project link: http://www.innovaciondocentegenero.eu/photovoice/</p>
AAPM TG 236 Report 236: Recommendations on volume-image-based treatment planning, dosimetry, and quality management for HDR intracavitary brachytherapy: Part I breast
<p>This is a zipped file for Appendix A.1 DVH validation using reference DCIOM datasets for AAPM TG report 236: Part I. Intracavitary breast brachytherapy. One can download and unzip the file. These are CT images and a structure file in DICOM format to validate the DVH information in your HDR brachytherapy treatment planning system either Varian BrachyVision or Elekta Oncentra. One can import all the DICOM files into your brachytherapy TPS and compute the dose. One can follow the instruction described in detail in Appendix A.1 of the AAPM TG 236: Part I report.</p>
Genotype data of Philippine native pigs, Duroc, Landrace, Large White and Berkshire, using 20 ISAG-FAO recommended microsatellite markers
<p>Microsatellite genotyping is a cost-effective method for the genetic diversity analysis of under-studied populations, such as the Philippine native pigs. We genotyped <em>n</em> = 196 pigs representing 7 Philippine native pig populations (<em>n </em>= 20 to 27 for each population) and 4 commercial transboundary breeds (<em>n</em> = 9 to 11 for each population). Twenty microsatellite markers, recommended by the International Society of Animal Genetics (ISAG)-FAO, were used to generate the dataset for population analysis (S0005, S0155, S0026, S0355, Sw830, Sw2410, Swr1941, Sw632, Sw24, S0228, Sw936, S0097, Sw857, Sw122, Sw2406, IGF1, Sw240, S0090, S0226, Sw72). S0218 was used as a sex marker (data not shown). All loci, except Sw24, did not deviate from Hardy Weinberg equilibrium. Each marker showed an average <em>PIC </em>of 0.779. A total of 260 alleles of length 86 to 272 bp were obtained. Using this dataset, we determined population structure and conservation priorities in the Philippine native pigs. This dataset contains both the raw files (.fsa) and the processed file (.txt). This dataset can be used by colleagues to increase their research coverage and achieve multi-population and multi-country comparisons, especially among Asian indigenous pigs.</p>
Current sheet intervals for "Solar wind current sheets: MVA inaccuracy and recommended single-spacecraft methodology"
Open the record for dataset details and reuse information.
Personalized Recommendations for Acute Kidney Injury (AKI) Care
ClinicalTrials.gov study NCT04040296. IPD Sharing: YES. Countries: 1. Publications: 3.
ScienceDex guides
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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.