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Dataset results
2,025 results for “AIS”
AI results complementing the Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Romania
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Italy
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Iceland
<p>This dataset contains the results of the surveillance activities conducted in 2019, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund</li> </ul>
AI results complementing the Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Switzerland
<p>This dataset contains the results of the surveillance activities conducted in 2019, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund</li> </ul>
AI results complementing the Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Croatia
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Luxembourg
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Belgium
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
Dataset supporting the manuscript dedicated to lignin precursor incorporation analysis, bioorthogonal labeling, parametric and AI based segmentation.
<p>This dataset aims to test the algorithms presented in an article submitted by the authors and untitled:</p> <p><strong>The combination of chemical reporter-, segmentation- and ratiometric-methods enables high-quality mapping of lignification dynamics in plant cell walls</strong></p> <p><strong>Are available:</strong></p> <p>-the algorithm with graphical user interface for imageJ and its installation procedure ("Cell_Wall_Segmentation " and "Tutorial Cell_Wall_Segmentation")</p> <p>-a folder comprising a classifier and a data set compatible with the machine learning part of the algorithm "data and classifier for weka"</p> <p>- representative images adapted for testing “representative images”</p> <p>- the macro corresponding to the parametric segmentation procedure (see imageJ documentation for installation instructions) “parametric_segmentation”</p>
Data from: Using satellite AIS to improve our understanding of shipping and fill gaps in ocean observation data to support marine spatial planning
1. A key stage underpinning marine spatial planning (MSP) involves mapping the spatial distribution of ecological processes and biological features, as well the social and economic interests of different user groups. One sector, merchant shipping (vessels that transport cargo or passengers), however, is often poorly represented in MSP due to a perceived lack of fine-scale spatially explicit data to support decision making processes. 2. Here, using the Republic of Congo as an example, we show how publicly accessible satellite derived Automatic Identification System (S-AIS) data can address gaps in ocean observation data for shipping at a national scale. We also demonstrate how fine-scale (0.05 km2 resolution) spatial data layers derived from S-AIS (intensity, occupancy) can be used to generate maps of vessel pressure to provide an indication of patterns of impact on the marine environment and potential for conflict with other ocean-user groups. 3. We reveal that passenger vessels, offshore service vessels, bulk carrier and cargo vessels and tankers account for 93.7% of all vessels and vessel traffic annually, and that these sectors operate in a combined area equivalent to 92% of Congo's exclusive economic zone(EEZ) – far exceeding the areas allocated for other user-groups (conservation, fisheries and petrochemicals). We also show that the shallow coastal waters and habitats of the continental shelf are subject to more persistent pressure associated with shipping; and that the potential for conflict among user groups is likely to be greater with fisheries, whose zones are subject to the highest vessel pressure scores than with conservation or petrochemical sectors. 4. Synthesis and applications. Shipping dominates ocean use, and so excluding this sector from decision making could lead to increased conflict among user groups, poor compliance and negative environmental impacts. This study demonstrates how Satellite derived Automatic Identification System data can provide a comprehensive mechanism to fill gaps in ocean observation data and visualise patterns of vessel behaviour and potential threats to better support marine spatial planning at national scales.13-Feb-2018
Expert and AI-generated annotations of the tissue types for the RMS-Mutation-Prediction microscopy images
<div> <p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations">https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations</a>.. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> </div> <div> <div> <p>This dataset contains 2 components:</p> <ol> <li>Annotations of multiple regions of interest performed by an expert pathologist with eight years of experience for a subset of hematoxylin and eosin (H&E) stained images from the RMS-Mutation-Prediction image collection [1,2]. Annotations were generated manually, using the Aperio ImageScope tool, to delineate regions of alveolar rhabdomyosarcoma (ARMS), embryonal rhabdomyosarcoma (ERMS), stroma, and necrosis [3]. The resulting planar contour annotations were originally stored in ImageScope-specific XML format, and subsequently converted into Digital Imaging and Communications in Medicine (DICOM) Structured Report (SR) representation using the open source conversion tool [4].</li> <li>AI-generated annotations stored as probabilistic segmentations.</li> </ol> <p><strong>WARNING</strong>: After the release of IDC v20 (v2 of this data record), it was discovered that a mistake had been made during data conversion that affected the newly-released segmentations accompanying the "RMS-Mutation-Prediction" collection. Segmentations released in v20 for this collection have the segment labels for alveolar rhabdomyosarcoma (ARMS) and embryonal rhabdomyosarcoma (ERMS) switched in the metadata relative to the correct labels. Thus segment 3 in the released files is labelled in the metadata (the SegmentSequence) as ARMS but should correctly be interpreted as ERMS, and conversely segment 4 in the released files is labelled as ERMS but should be correctly interpreted as ARMS. This mistake was fixed in the version v3 of this record (IDC data release v21).</p> <p>Many pixels from the whole slide images annotated by this dataset are not contained inside any annotation contours and are considered to belong to the background class. Other pixels are contained inside only one annotation contour and are assigned to a single class. However, cases also exist in this dataset where annotation contours overlap. In these cases, the pixels contained in multiple contours could be assigned membership in multiple classes. One example is a necrotic tissue contour overlapping an internal subregion of an area designated by a larger ARMS or ERMS annotation. The ordering of annotations in this DICOM dataset preserves the order in the original XML generated using ImageScope. These annotations were converted, in sequence, into segmentation masks and used in the training of several machine learning models. Details on the training methods and model results are presented in [1]. In the case of overlapping contours, the order in which annotations are processed may affect the generated segmentation mask if prior contours are overwritten by later contours in the sequence. It is up to the application consuming this data to decide how to interpret tissues regions annotated with multiple classes. The annotations included in this dataset are available for visualization and exploration from the National Cancer Institute Imaging Data Commons (IDC) [5] (also see IDC Portal at <a href="https://imaging.datacommons.cancer.gov/">https://imaging.datacommons.cancer.gov</a>) as of data release v18. Direct link to open the collection in IDC Portal: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations">https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=RMS-Mutation-Prediction-Expert-Annotations</a>.</p> </div> <div> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>pan_cancer_nuclei_seg_dicom-collection_id-idc_v19-aws.s5cmd</code> corresponds to the annotations for th eimages in the <code>collection_id</code> collection introduced in IDC data release v19. DICOM Binary segmentations were introduced in IDC v20. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <p>For each of the collections, the following manifest files are provided:</p> <ol> <li><code>rms_mutation_prediction_expert_annotations-idc_v20-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>rms_mutation_prediction_expert_annotations-idc_v20-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>rms_mutation_prediction_expert_annotations-idc_v20-dcf.dcf</code>: Gen3 manifest (for details see <a href="../records/Gen3%20manifest%20documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/imagingdatacommons/idc-index" target="_blank" rel="noopener">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code></li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <p>If you use the files referenced in the attached manifests, we ask you to cite this dataset, as well as the publication describing the original dataset <a href="https://paperpile.com/c/NHiBXI/njdR">[2]</a> and publication acknowledging IDC <a href="https://paperpile.com/c/NHiBXI/uJJZ">[5]</a>.</p> <h3>References</h3> </div> </div> <div> <p>[1] D. Milewski et al., "Predicting molecular subtype and survival of rhabdomyosarcoma patients using deep learning of H&E images: A report from the Children's Oncology Group," Clin. Cancer Res., vol. 29, no. 2, pp. 364–378, Jan. 2023, doi: 10.1158/1078-0432.CCR-22-1663.</p> <p>[2] Clunie, D., Khan, J., Milewski, D., Jung, H., Bowen, J., Lisle, C., Brown, T., Liu, Y., Collins, J., Linardic, C. M., Hawkins, D. S., Venkatramani, R., Clifford, W., Pot, D., Wagner, U., Farahani, K., Kim, E., & Fedorov, A. (2023). DICOM converted whole slide hematoxylin and eosin images of rhabdomyosarcoma from Children's Oncology Group trials [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8225132" rel="noopener">https://doi.org/10.5281/zenodo.8225132</a></p> <p>[3] Agaram NP. Evolving classification of rhabdomyosarcoma. Histopathology. 2022 Jan;80(1):98-108. doi: 10.1111/his.14449. PMID: 34958505; PMCID: PMC9425116,https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9425116/</p> <p>[4] Chris Bridge. (2024). ImagingDataCommons/idc-sm-annotations-conversion: v1.0.0 (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.10632182" rel="noopener">https://doi.org/10.5281/zenodo.10632182</a></p> <p>[5] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. Radiographics 43, (2023).</p> </div>
Does Co-Development with AI Assistants Lead to More Maintainable Code? Replication Package
<p>This is the replication package for the study "Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability" preregistered aat ICSME 2024 as “Does Co-Development with AI Assistants Lead to More Maintainable Code?”</p> <p>Abstract from the registered report:</p> <p>[Background/Context] AI assistants like GitHub Copilot are transforming software engineering, with several studies highlighting productivity improvements. However, their impact on code quality, particularly in terms of maintainability, requires further investigation.<br>[Objective/Aim] This study aims to examine the influence of AI assistants on software maintainability, specifically assessing how these tools affect the ability of developers to evolve code.<br>[Method] We will conduct a two-phased controlled experiment involving professional developers. In Phase 1, developers will add a new feature to a Java project, with or without the aid of an AI assistant. Phase 2, a randomized controlled trial, will involve a different set of developers evolving random Phase 1 projects - working without AI assistants. We will employ Bayesian analysis to evaluate differences in completion time, perceived productivity, code quality, and test coverage.</p> <p>Note: To maintain the integrity of the study, i.e., preventing any leakage to AI assistants' training data, we choose not to host the code in a public git repository. Instead, all relevant documents and code are shared through a replication package on Zenodo, available as PDF documents generated by repo2pdf (https://github.com/BankkRoll/repo2pdf). We have deliberately used settings to obfuscate the code (e.g., line numbers) to ensure it will not be scraped by any large language models before the study has been completed.</p> <p>Contents:</p> <ul> <li>Task 1 instructions.</li> <li>Task 2 instructions.</li> <li>The source code that the participants received.</li> <li>A causal graph with analysis details.</li> <li>Archives containing anonymized experimental data and analysis scripts (in .zip and .tar.gz for convenience). </li> </ul>
Exploring Generative AI Tools for Software Quality: Insights from a Rapid Multivocal Literature Review
Open the record for dataset details and reuse information.
Dataset of ICITS'24 - article "Ethics and AI in Higher Education: A Study on Students' Perceptions"
<p>This dataset encompasses responses from first-year undergraduate students enrolled in an ICT program at Fluminense Federal University. The survey was completed by 61 students via an online platform between June 28th and July 4th, 2023. The findings were subsequently published in an article titled "<strong>Ethics and AI in Higher Education: A Study on Students' Perceptions</strong>" presented at the ICITS'24 conference.</p> <p> </p> <p><strong>Article abstract</strong>:This initial study investigates the complex ethical concerns surrounding the integration of artificial intelligence (AI) in education, with a particular focus on undergraduate students' perceptions of AI tools in their academic efforts. A notable issue is the educational application of these tools, coupled with a pressing need for comprehensive public policies that ensure ethics and privacy are protected. Through qualitative research involving 61 students in an Information and Communication Technology (ICT) course, we explored the teaching of ethics related to the use of AI tools in an academic context. Our findings reveal a tendency among students to favor commercial AI tools that are not tailored for educational use, pointing to a potential shortfall in the educational AI domain. This study underscores the importance of promoting critical thinking and the responsible use of AI tools in academic settings. However, it is crucial to conduct further research with university students and instructors to refine and improve educational frameworks utilizing AI tools.</p>
The Piraeus AIS data set expressed under the vesselAI ontology
<p>See also <a href="https://github.com/gsantipantakis/dataExtraction/releases/">https://github.com/gsantipantakis/dataExtraction/releases/</a> for data extraction to tab separated values (TSV) files.</p>
AGIMUS Interviews with experts in AI ethics
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Recommender Systems and AI Techniques in E-commerce: An Analysis of Trends and the Research Agenda
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AI and Political Disinformation
Open the record for dataset details and reuse information.
Dataset for "In-IDE Human-AI Experience in the Era of Large Language Models; A Literature Review" paper
Open the record for dataset details and reuse information.
DRAGON-AI ChromaDB database
<p>ChromaDB databases used for analysis in [10.48550/arXiv.2312.10904<br>](https://arxiv.org/abs/2312.10904)</p>
Supplementary Material for "Automated Detection of AI-Obfuscated Plagiarism in Modeling Assignments"
<p>This repository contains additional material supporting the paper titled "Automated Detection of AI-Obfuscated Plagiarism in Modeling Assignments", presented at ICSE 2024 (SEET track).</p> <p>The paper presents a token-based approach for detecting modeling plagiarism. It leverages a novel normalization technique to achieve resilience against common obfuscation attacks.</p> <p>The approach was also integrated into the software plagiarism detector <a title="JPlag Repository on GitHub" href="https://github.com/jplag/JPlag">JPlag</a>, thus providing a widely accessible solution.</p> <p><strong>Contents Overview:</strong></p> <ul> <li><strong>Source Code:</strong> The implementation of our approach (contribution 1) based on the software plagiarism detector <a title="JPlag Repository on GitHub" href="https://github.com/jplag/JPlag">JPlag</a> (v4.0.0). Note that JPlag is licensed under the GPL-3.0 license.</li> <li><strong>ChatGPT Study:</strong> An exploration of how ChatGPT can be exploited for cheating in modeling assignments.</li> <li><strong>Datasets:</strong> The three datasets of our evaluation based on EMF modeling assignments.</li> <li><strong>Raw Results:</strong> All raw data of our evaluation results, as used in our plots and tables.</li> <li><strong>Demo:</strong> A packaged JAR of our approach's implementation alongside an instruction on how to use it.</li> </ul>
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.