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997 results for “AWARENESS”
Evaluation Data of a Trust-Aware Decentralized Social Network
<p>This dataset includes the evaluation data for the Paper "Trusting Decentralized Web Data in a Solid-based Social Network".<br>Within the ZIP File the following files are included in the dataset:</p> <ul> <li><strong>calculations.csv:</strong> All calculated numbers based on the raw data of the conducted empircal user study.</li> <li><strong>questions_translation.csv:</strong> A translation of all German questions asked in the survey to English, including a mapping of the question codes to the questions.</li> <li><strong>raw_data.csv:</strong> The raw data exported from the used survey tool of the conducted empircal user study.</li> <li><strong>survey.pdf:</strong> The survey as PDF print. IFrames of TrADS used during the survey are hidden in the PDF.</li> </ul>
Figure 12 in Increasing awareness for soil biodiversity and protection
Figure 12. Hands-on element: A smelling post with olfactory samples of various defense secretions of soil organisms.
Extended Evaluation Data of TrADS: a Trust-Aware Decentralized Social Network
<p>This dataset includes the evaluation data for the Paper "TrADS: a Trust-Aware Decentralized Social Network".<br>Within the ZIP File the following files are included in the dataset:</p> <ul> <li><strong>survey.pdf:</strong> The survey as PDF print. IFrames of TrADS used during the survey are hidden in the PDF.</li> <li><strong>all.xlsx:</strong> An Excelfile containing all the following .CSV files as worksheets.</li> <li><strong>raw_data.csv:</strong> The raw data exported from the used survey tool of the conducted empircal user study.</li> <li><strong>group1_unfiltered.csv:</strong> All participants' data of group 1.</li> <li><strong>group2_unfiltered.csv:</strong> All participants' data of group 2.</li> <li><strong>group1.csv:</strong> All filtered participants' data of group 1, who correctly answered the control questions.</li> <li><strong>group1_ueq_data.csv:</strong> All filtered participants's ueq+ question responses of group 1. Only including the questions 1 - 4 but not the question about dimension importance (q5).</li> <li><strong>group1_ueq_importance.csv:</strong> All filtered participants's ueq+ question responses about personal importance of group 1. Only including the question 5 about dimension importance.</li> <li><strong>group1_ueq_kpis:</strong> Including the ueq+ KPI values of all participants in group 1.</li> <li><strong>group2.csv:</strong> All filtered participants' data of group 2, who correctly answered the control questions.</li> <li><strong>group2_ueq_data.csv:</strong> All filtered participants's ueq+ question responses of group 2. Only including the questions 1 - 4 but not the question about dimension importance (q5).</li> <li><strong>group2_ueq_importance.csv:</strong> All filtered participants's ueq+ question responses about personal importance of group 2. Only including the question 5 about dimension importance.</li> <li><strong>group2_ueq_kpis:</strong> Including the ueq+ KPI values of all participants in group 2.</li> <li><strong>participants.csv:</strong> General information about participants grouped by both groups and joint.</li> <li><strong>likert_questions.csv:</strong> Mean Values and Standard Deviations (Std) of all statements rated on a 5-point Likert scale. It includes Means and Stds for Group 1, Group 2, Group 1 + Group 2 concatinated, and the values of the first user study published in the previous paper on TrADS <a href="https://zenodo.org/records/10641724" target="_blank" rel="noopener">(also available in previous dataset on Zenodo)</a>.</li> </ul>
Awareness of FAIR and FAIR4RS among international research software funders (Dataset)
<p><span>This research employed a mixed methods online survey to investigate research software funders’ perspectives. </span></p> <p><span>All participants gave informed consent at the start of the online survey. The University of Illinois Urbana-Champaign Institutional Review Board (no. 24374) reviewed the study and determined it exempt.</span></p> <p><span>Data collection took place from December 2023 to May 2024. The mean completion time for the detailed survey was 28 minutes and 13 seconds. The data were cleaned and prepared for analysis by removing any identifiable respondent details. </span></p> <h2><span>Survey design</span></h2> <p><span>The survey began by collecting profile information, including institutional affiliation and job title. The survey primarily gathered detailed information about initiatives, policies, or programs to support research software but also included a much smaller set of questions about additional topics, such as strategic funding priorities and awareness of key concepts. The data generated from this survey are too extensive to report in a single manuscript. Here, we focus on the results generated via the set of questions asking about FAIR and FAIR4RS, specifically, the following survey items: </span></p> <table> <tbody> <tr> <td> <p><strong><span>Variable</span></strong></p> </td> <td> <p><strong><span>Survey item</span></strong></p> </td> <td> <p><strong><span>Response options</span></strong></p> </td> </tr> <tr> <td> <p><span>Awareness of FAIR principles</span></p> </td> <td> <p><span>“Have you ever heard of the FAIR (findable, accessible, interoperable, and reusable) principles for data?”</span></p> </td> <td> <p><span>Yes, No, Unsure</span></p> <p><span>(If ‘Yes’, then the next question was asked)</span></p> </td> </tr> <tr> <td> <p><span>“How familiar are you with the FAIR principles for data?”</span></p> </td> <td> <p><span>Not at all Familiar, Slightly Familiar, Somewhat Familiar, Moderately Familiar, Extremely Familiar</span></p> </td> </tr> <tr> <td> <p><span>Awareness of FAIR4RS principles</span></p> </td> <td> <p><span>“Have you ever heard of the FAIR4RS principles for research software?”</span></p> </td> <td> <p><span>Yes, No, Unsure</span></p> <p><span>(If ‘Yes’, then the next question was asked)</span></p> </td> </tr> <tr> <td> <p><span>“How familiar are you with the FAIR4RS principles for research software?”</span></p> </td> <td> <p><span>Not at all Familiar, Slightly Familiar, Somewhat Familiar, Moderately Familiar, Extremely Familiar</span></p> </td> </tr> </tbody> </table> <p><span> </span></p> <p><span>In addition, an open-ended question asked for further detail about the respondents’ assessments of FAIR4RS’s relevance to their work.</span></p> <h2><span>Sampling</span></h2> <p><span>The survey targeted international research funders, including governmental and non-governmental (e.g., philanthropic) organizations. An initial contact list was created based on participation in the Research Software Association (ReSA) and known responsibilities for research software funding among the authors' networks. This list was refined by removing individuals who had moved to unrelated professional roles or were unavailable long-term due to personal issues.</span></p> <p><span>The final contact list comprised 71 people at 37 funding organizations. After excluding individuals when a member of their organization had already provided a complete response or when the person was no longer working on a relevant topic or was otherwise unavailable (total of n=30), 41 people remained. Of these, five did not complete the survey, while 36 individuals (representing 30 research funding organizations) did, yielding a response rate of 87.8% (and representing 81% of the original organizations). Fully completed survey responses were not required for inclusion in the sample, resulting in varied sample sizes across different survey questions.</span></p> <p><span>The respondents represented governmental (n=26), philanthropic (n=6), and corporate (n=1) research funders.</span></p> <p><span>Respondents’ job titles spanned the following categories: Senior Leadership and Executive (e.g., Vice President of Strategy); Program and Project Management (e.g., Senior Program Manager); Planning and Business Development; and Scientific, Technical, and IT roles (e.g., Scientific Information Lead).</span></p> <p><span>Most respondents, 72.7% (n=24), answered “Yes” to the question, “Has your organization established any policies, initiatives, or programs aimed at supporting research software?” Meanwhile, 18.2% (n=6) said “No,” and 9.1% (n=3) were “Unsure.”</span></p> <p><span>Regarding geographic distribution in the achieved sample, most survey respondents were from North America and Europe, with 15 and 12 participants, respectively. The sample also comprised 4 participants from South America, 3 from Oceania, and 1 from Asia, reflecting a global but uneven representation across continents. Some participating funders covered a broad spectrum of disciplines, while others focused on specific domains such as social sciences, health, environment, physical sciences, or humanities.</span></p>
Preventing technical debt with the TAP framework for Technical Debt Aware Management
<p><br> This zip file contains twelve files and one folder to replicate and verify the study evaluating the TAP Framework (for Technical debt Aware Project management).</p> <p> * TDframework_ticket_rawdata_revised_anonymised.xlsx: <br> revised and anonymized raw data for tickets statistic containing two sheets for TD ticket and maintenance ticket</p> <p> * TDframework_survey_questions_and_SPSSvariable_definition.xlsx: <br> excel sheet with all survey questions in German and English language, SPSS variable names, and short names<br> <br> <br> * TDframework_survey_results.sav:<br> survey results already in SPSS format, variables with suffix "D" are dichotomized versions of the variables as explained in the paper.</p> <p> * TDframework_survey_results.xslx:<br> Excel-Export of TDframework_survey_results.sav<br> <br> <br> * MannWhitneyUTest.spv:<br> results of the Mann-Whitney U-Test, which show the significances of the hypothesis mentioned in the paper (Section 5.2.2)<br> <br> * MannWhitneyUTest.xslx:<br> Excel-Export of MannWhitneyUTest.sav<br> <br> * Correlations.spv<br> results of the Chi-square test for correlations and the significances mentioned in the paper (Section 5.2.3)<br> <br> * Correlations.xslx:<br> Excel-Export of Correlations.sav<br> <br> <br> * Management-Survey_2021_en.pdf:<br> - original survey with englisch translation of the questions<br> <br> * Management-Survey_2021_XXX.pdf:<br> results of the management survey from <br> - team manager of the observed unit (TMO), participant A<br> - team manager of the comparison unit (TMC), participant B<br> - unit manager of the observed unit (UMO), participant C<br> - unit manager of the comparison unit (UMC), participant C<br> UMC => no survey, but email conversation between author and UMC to get the answers for the comparison unit<br> <br> * Fig6_Cause-Effect-Diagram_arrow_details.xslx<br> details about sources for the arrows in this figure<br> <br> * folder "Case Study Protocol"<br> contains the case study protocol according to Runeson's guidelines.<br> </p>
Compositional discovery of architecture-aware and sound process models from event logs of multi-agent systems: experimental data.
<p>This repository contains the experimental data used for the evaluation of the compositional approach to the discovery of process models from event logs of multi-agent systems, where agents interact according to specific patterns of synchronous and asynchronous interactions.</p> <p>According to the experiment plan, there is the folder for each interface pattern containing:</p> <ol> <li>The reference model (Petri net encoded in PNML-file)</li> <li>The event log obtained by simulating the behavior of the reference model (XES-file)</li> <li>The model discovered directly from the generated event log (Petri net encoded in PNML-file)</li> <li>The model discovered by composing the agent model w.r.t. the interface pattern (Petri net encoded in PNML-file)</li> </ol>
TReNCo: Topologically associating domain (TAD) aware regulatory network construction (extended data)
<p>The enclosed files contain all of the extended data from: TReNCo: Topologically associating domain (TAD) aware regulatory network construction</p>
Replication data for: Enhancing user awareness on inferences obtained from fitness trackers data
<p>Survey results and fitness trackers datasets used for the evaluation of PrivacyEnhAction application.</p>
Data and analysis scripts of "Predictability awareness rather than mere predictability enhances the perceptual benefits for targets in auditory rhythms over targets following temporal cues"
<p>These are the data and the analysis scripts accompanying the publication </p> <p>"Predictability awareness rather than mere predictability enhances the perceptual benefits for targets in auditory rhythms over targets following temporal cues". </p> <p>Check the readMe for an instruction.</p>
Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes
<p>Supplementary files of Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes by Borges, Boussau, Höhna, Pereira and Kosiol<br> </p>
Source Location Privacy Aware Routing Protocols Selection Results
<p>This is the dataset used to generate the results for the journal paper " A Decision Theoretic Framework for Selecting Source Location Privacy Aware Routing Protocols in Wireless Sensor Networks" at Future Generation Computer Systems (FGCS) 2018.</p> <p> </p> <p> </p>
[SELFY] Multi-Agent Situational Awareness Database
<p>This dataset has been created in the context of SELFY project. It includes ROS1 rosbags comprising both real-world measurements and synthetic data generated with the CARLA simulator, as well as the definitions of ROS messages. Each rosbag contains the following type of data:</p> <ul> <li>Images captured by the cameras;</li> <li>Cloud points captured by the LiDARs;</li> <li>Ground truth regarding the position of objects involved in the scene (vehicles, pedestrians).</li> </ul>
FedscGen: privacy-aware federated batch effect correction of single-cell RNA sequencing data -- Preprocessed datasets
<div> <div> <div> <div> <p>This dataset accompanies the publication "FedscGen: Privacy-Aware Federated Batch Effect Correction of Single-Cell RNA Sequencing Data" and includes eight single-cell RNA sequencing (scRNA-seq) datasets used to benchmark the FedscGen and scGen methods. The datasets are provided in <code>.h5ad</code> format and include comprehensive metadata necessary for replication and further analysis.</p> <h3>Datasets</h3> <p>We analyze various datasets to compare FedscGen against scGen (centralized) in terms of batch correction. For simplicity, we refer to the dataset by abbreviations:</p> <ol> <li> <p><strong>Cell Line (CL)</strong>:</p> <ul> <li>Derived from the 293t_jurkat experiment with three batches: Zheng et al., 2017.</li> </ul> </li> <li> <p><strong>Human Dendritic Cells (HDC)</strong>:</p> <ul> <li>scRNA-seq data of human dendritic cells across two batches: Villani et al., 2017.</li> </ul> </li> <li> <p><strong>Human Pancreas (HP)</strong>:</p> <ul> <li>Consolidated data from five sources with 14,767 cells each: Baron et al., 2016; Muraro et al., 2016; Segerstolpe et al., 2016; Wang et al., 2016; Xin et al., 2016.</li> </ul> </li> <li> <p><strong>Mouse Brain (MB)</strong>:</p> <ul> <li>Merged datasets with 691,600 and 141,606 cells: Saunders et al., 2018; Rosenberg et al., 2018.</li> </ul> </li> <li> <p><strong>Mouse Cell Atlas (MCA)</strong>:</p> <ul> <li>Data focusing on 11 cell types from various organs: Han et al., 2018; The Tabula Muris Consortium, 2018.</li> </ul> </li> <li> <p><strong>Mouse Hematopoietic Stem and Progenitor Cells (MHSPC)</strong>:</p> <ul> <li>Data from SMART-seq2 and MARS-seq protocols: Nestorowa et al., 2016; Paul et al., 2015.</li> </ul> </li> <li> <p><strong>Mouse Retina (MR)</strong>:</p> <ul> <li>Data from two unassociated laboratories with 26,830 and 44,808 cells: Macosko et al., 2015; Shekhar et al., 2016.</li> </ul> </li> <li> <p><strong>PBMC (human Peripheral Blood Mononuclear Cell)</strong>:</p> <ul> <li>scRNA-seq data with two batches: Zheng et al., 2017.</li> </ul> </li> </ol> <p><strong>Usage Notes</strong>: Each dataset is provided in <code>.h5ad</code> format, compatible with common single-cell analysis tools such as Scanpy. Detailed metadata is included within each file.</p> <p><strong>Keywords</strong>: Single-cell RNA sequencing, scRNA-seq, Batch effect correction, Privacy-aware, Federated learning, scGen, FedscGen, Clinical multi-center studies, Genomics, Bioinformatics</p> <p><strong>Contact</strong>: For questions or further information, please contact Mohammad Bakhtiari at <a href="mailto:mohammad.bakhtiari@uni-hamburg.de.">mohammad.bakhtiari@uni-hamburg.de.</a></p> <p><strong>License</strong>: Creative Commons Attribution 4.0 International (CC BY 4.0)</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Dataset of "Raising Awareness for Inertial Sensors-based Keylogging on Smartphones" scientific research
<p>Dataset for the article</p> <p>Federico Montori, Luca Sciullo, and Luca Bedogni. 2024. Raising Awareness for Inertial Sensors-based Keylogging on Smartphones. In Proceedings of the 2024 International Conference on Information Technology for Social Good (GoodIT '24). Association for Computing Machinery, New York, NY, USA, 14–21. https://doi.org/10.1145/3677525.3678634</p> <p>Please cite the above paper if you are using this dataset.</p>
Dataset: iShares ESG Aware MSCI USA ETF (ESGU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares ESG Aware MSCI EAFE ETF (ESGD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Ishares Environmentally Aware Real Estate ETF (ERET) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Aware, Inc. (AWRE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Tissue-aware interpretation of genetic variants advances the etiology of rare diseases
<p>Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a<br>few tissues and organs. However, variant effect prediction tools that aim to identify<br>pathogenic variants are typically oblivious to tissue contexts. Here we report a machine-<br>learning framework, denoted ‘Tissue Risk Assessment of Causality by Expression for<br>variants’ (TRACEvar, https://netbio.bgu.ac.il/TRACEvar/), that offers two advancements.<br>First, TRACEvar predicts pathogenic variants that disrupt the normal physiology of specific<br>tissues. This was achieved by creating 14 tissue-specific models that were trained on over<br>14,000 variants and combined 84 attributes of genetic variants with 495 attributes derived<br>from tissue omics. TRACEvar outperformed 10 well-established and tissue-oblivious variant<br>effect prediction tools. Second, the resulting models are interpretable, thereby illuminating<br>variants' mode-of-action. Application of TRACEvar to variants of 52 rare-disease patients<br>highlighted pathogenicity mechanisms and relevant disease processes. Lastly, interpretation<br>of large-scale models revealed that top-ranking determinants of pathogenicity included<br>attributes of disease-affected tissues, particularly cellular process activities. Hence, tissue<br>contexts and interpretable machine-learning models can greatly enhance the etiology of rare<br>diseases.</p> <p>Article link: https://www.embopress.org/doi/full/10.1038/s44320-024-00061-6</p> <p> </p>
Dataset of "What Should Developers Be Aware Of? An Empirical Study on the Directives of API Documentation"
<p>Dataset of <em>What Should Developers Be Aware Of? An Empirical Study on the Directives of API Documentation</em> (Martin Monperrus, Michael Eichberg, Elif Tekes, Mira Mezini), In Empirical Software Engineering, Springer, 2011.</p> <p><br> * dataset-src.tar.bz2 contains the source code of the Java libraries used as raw data.<br> * dataset.xml.bz2 contains the API documentation extracted from source code.<br> * directives.xml.bz2 contains the API directives found during the exploratory case study.<br> * directive-appendix.pdf is a human-readable PDF version of directives.xml.bz2.<br> <br> All datasets are published under the Creative Commons Attribution License: if you use them, please cite:<br> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.