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867 results for “repositories”
Repository: The Distribution of Frosts on Mars: Links to Present-Day Gully Activity
<p>This repository contains:</p> <p>1. Global calculated CO2 frost point temperatures (Kelvin) calculated at 1 ppd every 10 Ls using surface pressure from the online version of the Mars Climate Database<br> (http://www-mars.lmd.jussieu.fr/mcd_python/)<br> CO2 Frost Points</p> <p><br> 2. Local Solar Time and Season of THEMIS CO2 Frost Detections at gully locations<br> corr_gully_detections_filenames_meta</p> <p>3. Calculated CO2 frost amounts (kg/m^2) at 30S, 40S, 50S and 60S on pole-facing slopes<br> Frost Amounts</p> <p>4. Calculated CO2 frost amounts (kg/m^2) varying with lower material thermal inertia, slope azimuth, top material thermal inertia, top material thickness and surface albedo<br> Frost Sensitivity</p> <p>5. Predicted H2O frost lifetimes (hours)<br> H2OFrost_Stability</p> <p>6. Global THEMIS CO2 Frost Detections from Mars Year (MY) 26<br> MY26_THEMIS_CO2_Frost_Detections</p> <p>7. THEMIS CO2 Frost Detections at gully locations (Harrison et al. 2015) from MYs 26 - 35<br> MY26_35_THEMIS_GULLY_CO2_Frost_Detections</p> <p>8. H2O frost temperatures (Kelvin) at the Opportunity rover site<br> Opportunity_H2OFrost</p> <p>9. CO2 Frost detections made by Piqueux et al. (2016) using Mars Climate Sounder data<br> Piqueux et al (2016) MCS CO2 Frost Detections</p> <p>10. TES-derived data<br> a) TES_MY26_H2OFrost_Temp_Map<br> b) TES_MY26_H2OFrost_Temp_Seasonal</p> <p>11. The seasonal variation of the CO2 frost point (Kelvin) at the Viking Lander sites<br> Viking_Lander_Data</p>
LabelGit: A dataset for software repositories classification using attributed dependency graphs
<p>A dataset for software repositories classification using attributed dependency graphs</p>
Societies in balance: Monumentality and feasting activities among southern Naga communities, Northeast India (Data repository)
<p>The files provide supplementary information for the paper "Societies in balance: Monumentality and feasting activities among southern Naga communities, Northeast India".</p> <p>In accordance with the content and research questions of the article, this repository includes information on the megalithic monuments, as well as transcripts of the interviews conducted in the village of Rünguzu (Nagaland, India). Therefore, both the quantitative, and the qualitative results presented in the article could be reconstructed and reproduced on the basis of this repository.</p> <p>Information concerning the megalithic monuments of all the villages included in the analyses of the article are given in .csv format. The files include details of the monument type, the orientation of the monuments, the metric measures of the monuments, as well as the social affiliation of the monument builders (if available). These data are the basis for the comparative analyses of the megalithic monuments of the different villages, as well as the detailed analyses of the village Rünguzu. All box plots and bar charts presented in the article are completely based on the data made available here.</p> <p>Secondly, this repository includes transcripts of the interviews which were conducted in the village of Rünguzu. The qualitative descriptions of the village structure itself, the feasting activities, as well as the details of megalithic building activities are based on these interviews. Additionally, social anthropological literature and studies were vital as additional sources of information. The transcripts are, apart from the interviewers, completely anonymised in accordance to ethical standards in the publication of interviews.</p>
PFP data repository
<p>This archive contains the data shown in most Figures of the article:</p> <p>von der Gathen, P., R. Kivi, I. Wohltmann, R. J. Salawitch, M. Rex, Climate change favours large seasonal loss of Arctic ozone, Nat. Commun. <strong>12</strong>, 3886 (2012). https://doi.org/10.1038/s41467-021-24089-6</p> <p>as well as some data sets used in the manuscript. In case of problems, please contact</p> <p>Peter von der Gathen <Peter.von.der.Gathen@awi.de><br> </p>
Support of Creative Commons Licenses in German disciplinary and institutional open access repositories
<p>Which of the following open content licenses can be chosen for the metadata description of the open access full-texts (apart from the deposit license)? n=81* </p> <p>* This survey question is part of the 2014 Census of Open Access Repositories in Germany, Austria and Switzerland, see: http://nbn-resolving.de/urn:nbn:de:kobv:11-100222687 For the research data see: http://doi.org/10.5281/zenodo.10734 </p>
Inbred Strain Variant Database (ISVdb): A repository for probabilistically informed sequence differences among the Collaborative Cross strains and their founders
<p>Data files for the development of a database for storing (and a GUI for retrieving) the imputed variants for 72 Collaborative Cross strains of mice. Files include the inputs for the imputation, as well as the final results. See File_S1_Readme for more details on the included files.</p>
Application Domain of 5,000 GitHub Repositories
<p>We provide a manual classification of the application domain of 5,000 GitHub repositories (the most popular ones, by number of stars, on January, 2017).<br> <br> We classified each system in one of the following application domains:</p> <ul> <li><strong>Application software:</strong> systems that provide functionalities to end-users, like browsers and text editors (e.g., WordPress/WordPress and adobe/brackets).</li> <li><strong>System software:</strong> systems that provide services and infrastructure to other systems, like operating systems, middleware, and databases (e.g., torvalds/linux and mongodb/mongo).</li> <li><strong>Web libraries and frameworks</strong> (e.g., twbs/bootstrap and angular/angular.js).</li> <li><strong>Non-web libraries and frameworks</strong> (e.g., google/guava and facebook/fresco).</li> <li><strong>Software tools:</strong> systems that support development tasks, like IDEs, package managers, and compilers (e.g., Homebrew/homebrew and git/git).</li> <li><strong>Documentation:</strong> repositories with documentation, tutorials, source code examples, etc. (e.g., iluwatar/java-design-patterns).</li> </ul> <p>To cite the dataset, please use the following paper (which proposes and uses a first dataset version):</p> <p>Hudson Borges, Andre Hora, Marco Tulio Valente. <em>Understanding the Factors that Impact the Popularity of GitHub Repositories</em>. In 32nd IEEE International Conference on Software Maintenance and Evolution (ICSME), pages 334-344, 2016.</p>
Supporting dataset for: Reviewing repository discoverability: approaches to improving repository visibility and web impact
<p>This dataset supports the conference poster, "<a href="http://strathprints.strath.ac.uk/61333/">Reviewing repository discoverability: approaches to improving repository visibility and web impact</a>", presented at the Repository Fringe 2017 conference at the University of Edinburgh.</p> <p>The dataset comprises a single OpenDocument Spreadsheet (.ods) format file containing six data sheets of data pertaining to COUNTER compliant usage statistics, search query traffic from Google Search Console, web traffic data for Google Analytics and usage statistics from IRStats2. All data relate to the EPrints repository, <a href="http://strathprints.strath.ac.uk/">Strathprints</a>, based at the University of Strathclyde.</p> <p></p>
Repository of posterior distributions from Bayesian benchmark dose analysis
<p>This repository contains posterior distributions for all model parameters obtained from analysis of continuous dose-response studies.</p>
Data repository for Global wood harvest is sufficient for climate-friendly transitions to timber cities
<p><strong>Supplementary Information S2</strong></p> <p><strong>Global wood harvest is sufficient for climate-friendly transitions to timber cities | <a href="https://doi.org/10.1038/s41893-025-01605-w" target="_blank" rel="noopener">Nature Sustainability</a></strong></p> <p>Alperen Yayla <sup>1,a</sup>; Adam R. Mason <sup>1,b</sup>; Junyang Wang <sup>1,2,c</sup>; Stijn van Ewijk <sup>3,d</sup>; Rupert J. Myers <sup>1,e,*</sup></p> <p><sup>1</sup> Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, United Kingdom</p> <p><sup>2</sup> Department of Mathematics, Imperial College London, London, SW7 2AZ, United Kingdom</p> <p><sup>3</sup> Department of Civil, Environmental & Geomatic Engineering, University College London, London, WC1E 6BT, United Kingdom</p> <p>* Corresponding author</p> <p><sup>a </sup><a href="mailto:a.yayla22@imperial.ac.uk">a.yayla22@imperial.ac.uk</a>, <sup>b</sup> <a href="mailto:a.mason19@imperial.ac.uk">a.mason19@imperial.ac.uk</a>, <sup>c</sup> <a href="mailto:junyang.wang21@imperial.ac.uk">junyang.wang21@imperial.ac.uk</a>, <sup>d</sup> <a href="mailto:s.vanewijk@ucl.ac.uk">s.vanewijk@ucl.ac.uk</a>, <sup>e</sup> <a href="mailto:r.myers@imperial.ac.uk">r.myers@imperial.ac.uk</a>.</p>
Dataset Repository for a Botanical Garden Project: Project Based Learning Assessment from a Blended Approach of PBL with the 5E Model Components
<p><strong>Title:</strong> Botanical Explorers: A Journey Through Our School's Flora - Assessment Data</p><p><strong>Description:</strong> This Excel spreadsheet contains the assessment data for the educational project titled "Botanical Explorers: A Journey Through Our School's Flora", a hands-on science initiative for Grade 9 students at Chalermkwansatree School. The project, conducted under the guidance of Teacher Hasan and aligned with the Additional Science subject focusing on Fuel Energy, is designed to engage students in active learning about local plant life, while developing their research and presentation skills, and fostering environmental appreciation.</p><p>The dataset is part of a comprehensive project contributing 20% to the Term 1, Midterm Score of 50 marks. It encompasses a detailed breakdown of the marks distribution across different tasks such as Data Collection, Book Report, Presentation, and Poster creation, reflecting the multifaceted approach to evaluating student learning and engagement.</p><p><strong>Data Organization:</strong> The spreadsheet is meticulously organized to include:</p><ul><li>A plant list with identifiers like school plant name, location, and space for pictures.</li><li>A marks distribution table indicating the scoring for each project component.</li><li>A timeline for group formation, research, data collection, and submission deadlines.</li><li>Details of the Book Report, Presentation, and Poster requirements.</li></ul><p><strong>Methodology:</strong> Students formed groups to research seven specific plants found within the school premises, examining their identification, classification, ecological roles, growth, and development. The data was collected through a blend of direct observations and scholarly research, ensuring a robust and educational exploration of botany.</p><p><strong>Intended Audience:</strong> The dataset is intended for educational purposes, serving as a valuable resource for educators, students, and researchers interested in project-based learning, botany education, and student assessment methods.</p><p><strong>Usage Notes:</strong> The data provided in this spreadsheet is anonymized, with no personal student information disclosed. It serves as an exemplar model for similar educational initiatives and can be adapted for comparative studies or further educational research.</p><p><strong>Conditions for Use:</strong> The dataset is shared openly with the intention that it will be used for educational and research purposes. Users are requested to cite the dataset appropriately and adhere to any academic and ethical guidelines when utilizing the data for their work.</p>
Repository for adjoint convolution analysis of sea level variations near Charleston and Nantucket
<p>This repository contains adjoint sensitivity, forcing, and example adjoint-convolution script to reconstruct sea level variations near Charleston and Nantucket. </p>
Repositori de dades meteorològiques de les estacions automàtiques de Catalunya
<p>Aauest DataSet conté informació metorològica de la xarxa d'estacions meteorològiques de Catalunya (XEMA) entre els dies 1/10/2023 i 1/11/2023.</p><p>Conté les següents dades:</p><ul><li>Estació : Codi de l'estació meteorològica</li><li>TemperaturaMitjana : Temperatura mitjana diaria</li><li>TemperaturaMaxima : Temperatura màxima diaria</li><li>TemperaturaMinima : Temperatura mínima diaria</li><li>Precipitacio : Precipitació acumulada durant el dia</li><li>Humitat : Humitat relativa mitjana</li><li>Pressio : Pressió atmosfèrica mitjana</li><li>RatxaVent : Ratxa màxima de vent</li><li>DireccioVent : Direcció de la ratxa màxima del vent</li><li>Irradiacio : Irradiació solar global</li><li>Data : Data de la observació</li><li>Estació.y : Nom de l'estació meteorològica</li><li>Latitud : Lcocalització de l'estació meteorològica</li><li>Longitud : Lcocalització de l'estació meteorològica</li><li>Comarca : Comarca on es troba l'estació meteoriloògica</li><li>Municipi : Municipi on es localitza l'estació meteorològica</li><li>Estat : Estat de l'estació (Operativa/Desmantellada)</li><li>link : Link d'accés a les dades de l'estació meteorològica</li></ul>
Data repository accompanying "Flux-tunable Josephson Effect in a Four-Terminal Junction"
Open the record for dataset details and reuse information.
Superadditive Communications with the Green Machine: Online Data Repository
<p>This online repository contains selected datasets and scripts for data processes in the paper "Superadditive Communications with the Green Machine: A Practical Demonstration of Nonlocality without Entanglement". arXiv.2310.05889</p>
Anonymised Dataset Survey of Open Repositories in Ireland
<p>A dataset of survey results of the National Open Research Forum (NORF) Survey of Open Repositories in Ireland. Conducted from April 24 to May 30, 2023, presents a comprehensive overview of the status of open repositories in Ireland. The survey involved respondents from educational, governmental, and research institutions aimed to assess various aspects of repository provision and management across key performance indicators.</p>
VISIONE Feature Repository for VBS: Multi-Modal Features and Detected Objects from VBSLHE Dataset
<p>This repository contains a diverse set of features extracted from the VBSLHE dataset (laparoscopic gynecology) . These features will be utilized in the VISIONE system [Amato et al. 2023, Amato et al. 2022] in the next editions of the Video Browser Showdown (VBS) competition (<a href="https://www.videobrowsershowdown.org/">https://www.videobrowsershowdown.org/</a>). </p> <p>We used a snapshot of the dataset provided by the Medical University of Vienna and Toronto that can be downloaded using the instructions provided at <a href="https://download-dbis.dmi.unibas.ch/mvk/">https://download-dbis.dmi.unibas.ch/mvk/</a>. It comprises 75 video files. We divided each video into video shots with a maximum duration of 5 seconds.</p> <p>This repository is released under a Creative Commons Attribution license. If you use it in any form for your work, please cite the following paper:</p> <blockquote> <p>@inproceedings{amato2023visione, title={VISIONE at Video Browser Showdown 2023}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Falchi, Fabrizio and Gennaro, Claudio and Messina, Nicola and Vadicamo, Lucia and Vairo, Claudio}, booktitle={International Conference on Multimedia Modeling}, pages={615--621}, year={2023}, organization={Springer} } </p> </blockquote> <p> </p> <p>This repository (v2) comprises the following files:</p> <ul> <li><em><strong>msb.tar.gz </strong></em> contains tab-separated files (.tsv) for each video. Each tsv file reports, for each video segment, the timestamp and frame number marking the start/end of the video segment, along with the timestamp of the extracted middle frame and the associated identifier ("id_visione").</li> <li><em><strong>extract-keyframes-from-msb.tar.gz</strong></em> contains a Python script designed to extract the middle frame of each video segment from the MSB files. To run the script successfully, please ensure that you have the original VBSLHE videos available.</li> <li><em><strong>features-aladin.tar.gz†</strong></em><strong> </strong>contains <a href="https://github.com/mesnico/ALADIN">ALADIN</a> [Messina N. et al. 2022] features extracted for all the segment's middle frames.</li> <li><em><strong>features-clip-laion.tar.gz†</strong></em> contains <a href="https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K">CLIP ViT-H/14 - LAION-2B </a>[Schuhmann et al. 2022] features extracted for all the segment's middle frames.</li> <li><em><strong>features-clip-openai.tar.gz† </strong></em>contains <a href="https://huggingface.co/openai/clip-vit-large-patch14">CLIP ViT-L/14</a> [Radford et al. 2021] features extracted for all the segment's middle frames.</li> <li><em><strong>features-clip2video.tar.gz† </strong></em>contains <a href="https://github.com/CryhanFang/CLIP2Video">CLIP2Video</a> [Fang H. et al. 2021] extracted for all the video segments. <strong> </strong></li> <li><em><strong>objects-frcnn-oiv4.tar.gz* </strong></em>contains the objects detected using <a href="http://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1">Faster R-CNN+Inception ResNet</a> (trained on the Open Images V4 [Kuznetsova et al. 2020]).</li> <li><em><strong>objects-mrcnn-lvis.tar.gz*</strong></em> contains the objects detected using Mask R-CNN [He et al. 2017] (trained on LVIS).</li> <li><em><strong>objects-vfnet64-coco.tar.gz*</strong></em> contains the objects detected using VfNet [Zhang et al. 2021] (trained on COCO dataset).</li> </ul> <p>*Please be sure to use the <strong>v2 version </strong>of this repository, since v1 feature files may contain inconsistencies that have now been corrected</p> <p><em><strong>*Note on the object annotations:</strong></em> Within an object archive, there is a jsonl file for each video, where each row contains a record of a video segment (the <em>"_id"</em> corresponds to the <em>"id_visione"</em> used in the msb.tar.gz) . Additionally, there are three arrays representing the objects detected, the corresponding scores, and the bounding boxes. The format of these arrays is as follows:</p> <ul> <li><em>"object_class_names"</em>: vector with the class name of each detected object.</li> <li><em>"object_scores"</em>: scores corresponding to each detected object.</li> <li><em>"object_boxes_yxyx"</em>: bounding boxes of the detected objects in the format <em>(ymin, xmin, ymax, xmax).</em></li> </ul> <p> </p> <p><em><strong>†Note on the cross-modal features: </strong></em>The extracted multi-modal features (ALADIN, CLIPs, CLIP2Video) enable internal searches within the VBSLHE dataset using the query-by-image approach (features can be compared with the dot product). However, to perform searches based on free text, the text needs to be transformed into the joint embedding space according to the specific network being used (see links above). Please be aware that t<strong>he service for transforming text into features is not provided within this repository and should be developed independently using the original feature repositories linked above.</strong></p> <p>We have plans to release the code in the future, allowing the reproduction of the VISIONE system, including the instantiation of all the services to transform text into cross-modal features. However, this work is still in progress, and the code is not currently available.</p> <p> </p> <p><strong>References:</strong></p> <p>[Amato et al. 2023] Amato, G.et al., 2023, January. VISIONE at Video Browser Showdown 2023. In International Conference on Multimedia Modeling (pp. 615-621). Cham: Springer International Publishing.</p> <p>[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. </p> <p>[Fang H. et al. 2021] Fang H. et al., 2021. Clip2video: Mastering video-text retrieval via image clip. arXiv preprint arXiv:2106.11097.</p> <p>[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).</p> <p>[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. International Journal of Computer Vision, 128(7), pp.1956-1981.</p> <p>[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.</p> <p>[Messina et al. 2022] Messina N. et al., 2022, September. Aladin: distilling fine-grained alignment scores for efficient image-text matching and retrieval. In Proceedings of the 19th International Conference on Content-based Multimedia Indexing (pp. 64-70).</p> <p>[Radford et al. 2021] Radford A. et al., 2021, July. Learning transferable visual models from natural language supervision. In International conference on machine learning (pp. 8748-8763). PMLR.</p> <p>[Schuhmann et al. 2022] Schuhmann C. et al., 2022. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35, pp.25278-25294.</p> <p>[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In Proceedings of the IEEE/CV</p>
VISIONE Feature Repository for VBS: Multi-Modal Features and Detected Objects from MVK Dataset
<p>This repository contains a diverse set of features extracted from the marine video (underwater) dataset (MVK) . These features were utilized in the VISIONE system [Amato et al. 2023, Amato et al. 2022] during the latest editions of the Video Browser Showdown (VBS) competition (<a href="https://www.videobrowsershowdown.org/">https://www.videobrowsershowdown.org/</a>). </p> <p>We used a snapshot of the MVK dataset from 2023, that can be downloaded using the instructions provided at <a href="https://download-dbis.dmi.unibas.ch/mvk/">https://download-dbis.dmi.unibas.ch/mvk/</a>. It comprises 1,372 video files. We divided each video into 1 second segments. </p> <p>This repository is released under a Creative Commons Attribution license. If you use it in any form for your work, please cite the following paper:</p> <blockquote> <pre>@inproceedings{amato2023visione, title={VISIONE at Video Browser Showdown 2023}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Falchi, Fabrizio and Gennaro, Claudio and Messina, Nicola and Vadicamo, Lucia and Vairo, Claudio}, booktitle={International Conference on Multimedia Modeling}, pages={615--621}, year={2023}, organization={Springer} }</pre> </blockquote> <p> </p> <p>This repository comprises the following files:</p> <ul> <li><strong><em>msb.tar.gz </em></strong> contains tab-separated files (.tsv) for each video. Each tsv file reports, for each video segment, the timestamp and frame number marking the start/end of the video segment, along with the timestamp of the extracted middle frame and the associated identifier ("id_visione"). </li> <li><em><strong>extract-keyframes-from-msb.tar.gz</strong></em> contains a Python script designed to extract the middle frame of each video segment from the MSB files. To run the script successfully, please ensure that you have the original MVK videos available.</li> <li><strong><em>features-aladin.tar.gz<sup>†</sup></em> </strong>contains <a href="https://github.com/mesnico/ALADIN">ALADIN</a> [Messina N. et al. 2022] features extracted for all the segment's middle frames. </li> <li><em><strong>features-clip-laion.tar.gz<sup>†</sup></strong></em> contains <a href="https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K">CLIP ViT-H/14 - LAION-2B </a>[Schuhmann et al. 2022] features extracted for all the segment's middle frames.</li> <li><em><strong>features-clip-openai.tar.gz<sup>†</sup> </strong></em>contains <a href="https://huggingface.co/openai/clip-vit-large-patch14">CLIP ViT-L/14</a> [Radford et al. 2021] features extracted for all the segment's middle frames. </li> <li><em><strong>features-clip2video.tar.gz<sup>†</sup> </strong></em>contains <a href="https://github.com/CryhanFang/CLIP2Video">CLIP2Video</a> [Fang H. et al. 2021] extracted for all the 1s video segments. <strong> </strong></li> <li><em><strong>objects-frcnn-oiv4.tar.gz<sup>*</sup> </strong></em>contains the objects detected using <a href="http://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1">Faster R-CNN+Inception ResNet</a> (trained on the Open Images V4 [Kuznetsova et al. 2020]). </li> <li><em><strong>objects-mrcnn-lvis.tar.gz<sup>*</sup></strong></em> contains the objects detected using Mask R-CNN [He et al. 2017] (trained on LVIS).</li> <li><em><strong>objects-vfnet64-coco.tar.gz<sup>*</sup></strong></em> contains the objects detected using VfNet [Zhang et al. 2021] (trained on COCO dataset).</li> </ul> <p>*Please be sure to use the <strong>v2 version </strong>of this repository, since v1 feature files may contain inconsistencies that have now been corrected</p> <p><em><strong>*Note on the object annotations:</strong></em> Within an object archive, there is a jsonl file for each video, where each row contains a record of a video segment (the <em>"_id"</em> corresponds to the <em>"id_visione"</em> used in the msb.tar.gz) . Additionally, there are three arrays representing the objects detected, the corresponding scores, and the bounding boxes. The format of these arrays is as follows:</p> <ul> <li><em>"object_class_names"</em>: vector with the class name of each detected object.</li> <li><em>"object_scores"</em>: scores corresponding to each detected object.</li> <li><em>"object_boxes_yxyx"</em>: bounding boxes of the detected objects in the format <em>(ymin, xmin, ymax, xmax).</em></li> </ul> <p> </p> <p><em><strong><sup>†</sup>Note on the cross-modal features: </strong></em>The extracted multi-modal features (ALADIN, CLIPs, CLIP2Video) enable internal searches within the MVK dataset using the query-by-image approach (features can be compared with the dot product). However, to perform searches based on free text, the text needs to be transformed into the joint embedding space according to the specific network being used (see links above). Please be aware that t<strong>he service for transforming text into features is not provided within this repository and should be developed independently using the original feature repositories linked above.</strong></p> <p>We have plans to release the code in the future, allowing the reproduction of the VISIONE system, including the instantiation of all the services to transform text into cross-modal features. However, this work is still in progress, and the code is not currently available.</p> <p> </p> <p><strong>References:</strong></p> <p>[Amato et al. 2023] Amato, G.et al., 2023, January. VISIONE at Video Browser Showdown 2023. In International Conference on Multimedia Modeling (pp. 615-621). Cham: Springer International Publishing.</p> <p>[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. </p> <p>[Fang H. et al. 2021] Fang H. et al., 2021. Clip2video: Mastering video-text retrieval via image clip. arXiv preprint arXiv:2106.11097.</p> <p>[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).</p> <p>[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. International Journal of Computer Vision, 128(7), pp.1956-1981.</p> <p>[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.</p> <p>[Messina et al. 2022] Messina N. et al., 2022, September. Aladin: distilling fine-grained alignment scores for efficient image-text matching and retrieval. In Proceedings of the 19th International Conference on Content-based Multimedia Indexing (pp. 64-70).</p> <p>[Radford et al. 2021] Radford A. et al., 2021, July. Learning transferable visual models from natural language supervision. In International conference on machine learning (pp. 8748-8763). PMLR.</p> <p>[Schuhmann et al. 2022] Schuhmann C. et al., 2022. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35, pp.25278-25294.</p> <p>[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In Proceedings of the IEEE/CV</p>
OneNet project - KPIs repository
<p>This data set is the result of the work carried out in the OneNet project related to the evaluation of demonstrators' results.<br> <br>The data set consists of the KPI values that were achieved in all OneNet demonstrators, as well as their characterization and a brief description of the objectives for calculating each KPI. In the complementary document, the calculation methodologies and formulas of each KPI are reported.</p>
Data and code repository for the "Uncertainties in cloud-radiative heating within an idealized extratropical cyclone"
<p><strong>Author:</strong> Behrooz Keshtgar, behrooz.keshtgar@kit.edu</p> <p>This archive contains the post-processed data used to generate the figures and the code repository for the publication "Uncertainties in cloud-radiative heating within an idealized extratropical cyclone" by Behrooz Keshtgar, Aiko Voigt, Bernhard Mayer and Corinna Hoose.</p> <p>Description of the <strong>data</strong>:</p> <p>figure1.nc: precipitation rate, cloud cover, surface pressure, and cloud classes on day 4.5 of the ICON-NWP baroclinic life cycle simulation.</p> <p>figure2.nc: spatially and temporally averaged profiles of cloud water, ice mass content, and cloud fractions from ICON-LEM simulations.</p> <p>figure4.nc: spatially and temporally averaged cloud-radiative heating profiles from ICON-LEM simulations and offline radiation calculations for each LEM domain.</p> <p>figure5.nc: cross-section of radiative heating rates for 3D and 1D radiative transfer calculations in the shallow cumulus domain.</p> <p>figure6.nc: spatially averaged cloud-radiative heating profiles from 3D and 1D radiation calculations for each LEM domain.</p> <p>figure7.nc: cross-section of cloud-radiative heating calculated with the ice optics of Fu and Baum_ghm in the WCB ascent region.</p> <p>figure8.nc: spatially and temporally averaged profiles of cloud-radiative heating from 1D radiation calculations with different ice optics for each LEM domain.</p> <p>figure9.nc: spatially and temporally averaged profiles of cloud-radiative heating from 1D radiation calculations with LEM and NWP clouds for each LEM domain.</p> <p>figure10.nc: spatially and temporally averaged density and cloud-radiative heating profiles from different offline radiation calculations for each LEM domain.</p> <p>figure11.nc: profiles of the mean absolute difference of cloud-radiative heating from different offline radiation calculations at different resolutions for each LEM domain.</p> <p> </p> <p>The <strong>keshtgar-etal-2024-cyclone-crh-uncertainties-main.zip</strong> is the copy of the published git repository for the model run and analysis scripts. The repository contains</p> <p>- Scripts for the ICON model simulations</p> <p>- Scripts for the offline radiative transfer calculations with LibRadTran and the post-processing routine</p> <p>- Python scripts and Jupyter Notebooks for the analysis in the paper</p>
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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.