Recent Torrents https://academictorrents.com/ Dataset 04c55531c1617744bd76a3b126a19c4ba48cb2a2 https://academictorrents.com/details/04c55531c1617744bd76a3b126a19c4ba48cb2a2 https://academictorrents.com/details/04c55531c1617744bd76a3b126a19c4ba48cb2a2 Wikipedia database dumps of European language wikis of 10k articles or more. enwiki excluded. Wikipedia Multistream 2026-06-01. These 67 languages are included: Albanian, Alemannic, Aragonese, Asturian, Basque, Bavarian, Belarusian, Benetian, Bosnian, Breton, Bulgarian, Catalan, Croatian, Czech, Danish, Dutch, Emilian-Romagnol, Esperanto, Estonian, Faroese, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Ladin, Latin, Latvian, Ligurian, Limburgish, Lithuanian, Lombard, Low German, Macedonian, Maltese, Neapolitan, North Frisian, Norwegian, Nynorsk, Occitan, Piedmontese, Polish, Portuguese, Romanian, Romansh, Rusyn, Samogitian, Scots, Scottish Gaelic, Serbian, Serbo-Croatian, Sicilian, Silesian, Slovak, Slovenian, Spanish, Swedish, Ukrainian, Upper Sorbian, Walloon, Welsh, West Frisian, Yiddish. 53381860475 Dataset 55199eff9368cde1f5c1262dd7c1af09f7503ea5 https://academictorrents.com/details/55199eff9368cde1f5c1262dd7c1af09f7503ea5 https://academictorrents.com/details/55199eff9368cde1f5c1262dd7c1af09f7503ea5 Reddit comments and submisReddit comments and submissions from 2026-05 Documentation, json schemas and more can be found at https://github.com/ArthurHeitmann/arctic_shift Helper scripts for processing files can be found at https://github.com/Watchful1/PushshiftDumpssions 71668069069 Dataset 16396475b640d8487a6b723eada8a440fb33d3ce https://academictorrents.com/details/16396475b640d8487a6b723eada8a440fb33d3ce https://academictorrents.com/details/16396475b640d8487a6b723eada8a440fb33d3ce # Crossref Event Data archive This is an archive of all events collected by selected Crossref Event Data agents between its launch on 2017/02/17 and its deprecation on 2026/04/23. The DOI of this dataset is https://doi.org/10.13003/wjyr-rv9j ## File format The data are provided in [JSONL](https://jsonlines.org/) format. Each data file has a .jsonl file extension and contains up to 5000 entries (lines). Filenames follow this pattern: agent-nnnn.jsonl ## Structure of the archive The data are hierarchically grouped in directories by agent, year, month, day and a 4-digit directory ID. For example: shell $ tree -L 6 data | head -n 18 data ├── crossref │ ├── 2021 │ │ ├── 01 │ │ │ └── 01 │ │ │ └── 0000 │ │ │ └── crossref-0001.jsonl │ │ ├── 04 │ │ │ ├── 28 │ │ │ │ └── 0000 │ │ │ │ └── crossref-0001.jsonl │ │ │ ├── 29 │ │ │ │ └── 0000 │ │ │ │ ├── crossref-0001.jsonl │ │ │ │ └── crossref-0002.jsonl │ │ │ └── 30 │ │ │ └── 0000 │ │ │ └── crossref-0001.jsonl Each daily directory contains one or more 4 digit directories containing JSONL data files. There can be at most 1000 files within each 4 digit directory. ## Agents The export contains data for the following agents: | Agent name | Description | | ———————- | ————————————————————————————————————————————————————— | | crossref | Relationships and references to datasets and DOI registration agencies other than Crossref (e.g. DataCite) | | f1000 | Recommendations of research publications | | facultyopinions | Recommendations of research publications (formerly F1000) | | hypothesis | Annotations in Hypothes\.is | | newsfeed | Discussed in blogs and media | | reddit | Discussed on Reddit | | reddit-links | Discussed on sites linked to in subreddits | | stackexchange | Discussed on StackExchange sites | | web | Discussed on selected webpages | | wikipedia | References on Wikipedia pages | | wordpressdotcom | Discussed on Wordpress\.com sites | ## Event data structure The main purpose of each event is to capture a relationship between a subject and an object, a triplet of [subject, relationship, object]. The event data structure has a few properties but the most important ones are: | Property | Description | | ——————————- | ——————————————————————————————————————————————————————————————————————————— | | license | The license per event. Different agents may have a different license and that may also change over time. It is best to consider the license per event. | | obj_id | The id of the object | | subj_id | The id of the subject | | occurred_at | When was the event observed? | | id | A unique id, can be helpful when processing the archive | | subj:pid | Same as subj_id | | obj:pid | Same as obj_id | | subj:url | The source of the event | | obj:url | Same as obj:pid | | subj:title | Some agents (for example Wikipedia) capture a title for the subject s url | | subj/obj:work_type_id | The type of the identified pid | | source_id | The agent that captured this event | | relation_type_id | The relation type of the relationship between the subject and the object | One thing to note: Depending on the agent the subj:url may or may not be equal to the subj:pid . In any case the subj:url should be treated as an independent value. An example of a Crossref agent event: json "license": "https://creativecommons.org/publicdomain/zero/1.0/", "obj_id": "https://doi.org/10.14383/cri.2017.12.2.149", "source_token": "36c35e23-8757-4a9d-aacf-345e9b7eb50d", "occurred_at": "2025-01-01T11:10:07.000Z", "subj_id": "https://doi.org/10.3390/vetsci11020090", "id": "9d5be3e3-3141-4965-836a-8831178726b2", "action": "add", "subj": "pid": "https://doi.org/10.3390/vetsci11020090", "url": "https://doi.org/10.3390/vetsci11020090", "work_type_id": "journal-article" , "source_id": "crossref", "obj": "pid": "https://doi.org/10.14383/cri.2017.12.2.149", "url": "https://doi.org/10.14383/cri.2017.12.2.149", "method": "doi-literal", "verification": "literal" , "relation_type_id": "references" An example of a Wikipedia agent event: json "license": "https://creativecommons.org/publicdomain/zero/1.0/", "obj_id": "https://doi.org/10.2307/2128863", "source_token": "36c35e23-8757-4a9d-aacf-345e9b7eb50d", "occurred_at": "2025-01-01T17:02:25Z", "subj_id": "https://en.wikipedia.org/api/rest_v1/page/html/Mohammad_Reza_Pahlavi/1266654138", "id": "6618b137-da03-4f15-ac86-ca3283c28cb6", "action": "add", "subj": "pid": "https://en.wikipedia.org/wiki/Mohammad_Reza_Pahlavi" 107590238836 Dataset d4056fe87d24aaed9d366453f17abb08f7c4c62d https://academictorrents.com/details/d4056fe87d24aaed9d366453f17abb08f7c4c62d https://academictorrents.com/details/d4056fe87d24aaed9d366453f17abb08f7c4c62d Release 09-RS220 (24th April 2024) of the Genome Taxonomy Database (GTDB), an initiative to establish a standardised microbial taxonomy based on genome phylogeny. 410496532480 Dataset 13e25c59c31920abce5599a071991a8d8ca94e89 https://academictorrents.com/details/13e25c59c31920abce5599a071991a8d8ca94e89 https://academictorrents.com/details/13e25c59c31920abce5599a071991a8d8ca94e89 Release 07-RS207 (8th April 2022) of the Genome Taxonomy Database (GTDB), an initiative to establish a standardised microbial taxonomy based on genome phylogeny. 317416538112 Dataset 4ad45a3bcd78a36f700530060ae7839638b09840 https://academictorrents.com/details/4ad45a3bcd78a36f700530060ae7839638b09840 https://academictorrents.com/details/4ad45a3bcd78a36f700530060ae7839638b09840 Release 06-RS202 (27th April 2021) of the Genome Taxonomy Database (GTDB), an initiative to establish a standardised microbial taxonomy based on genome phylogeny. 191990071296 Dataset fe8c256fd07464c365c2403b6f34be3ef510aae1 https://academictorrents.com/details/fe8c256fd07464c365c2403b6f34be3ef510aae1 https://academictorrents.com/details/fe8c256fd07464c365c2403b6f34be3ef510aae1 Release 04-RS89 (19th June 2019) of the Genome Taxonomy Database (GTDB), an initiative to establish a standardised microbial taxonomy based on genome phylogeny. 103494451200 Dataset 937539d2e0c9d774c63625af5106c0e6d4bc5a7a https://academictorrents.com/details/937539d2e0c9d774c63625af5106c0e6d4bc5a7a https://academictorrents.com/details/937539d2e0c9d774c63625af5106c0e6d4bc5a7a Release 3-RS86.2 (15th January 2019) of the Genome Taxonomy Database (GTDB), an initiative to establish a standardised microbial taxonomy based on genome phylogeny. 27277656064 Dataset 4793809ff9e07b0217baed7a4fe6980a0444ac20 https://academictorrents.com/details/4793809ff9e07b0217baed7a4fe6980a0444ac20 https://academictorrents.com/details/4793809ff9e07b0217baed7a4fe6980a0444ac20 Release 05-RS95 (17th July 2020) of the Genome Taxonomy Database (GTDB), an initiative to establish a standardised microbial taxonomy based on genome phylogeny. 132191879168 Dataset 7ced89299d243e10b37664a5ee1799444eee8c5b https://academictorrents.com/details/7ced89299d243e10b37664a5ee1799444eee8c5b https://academictorrents.com/details/7ced89299d243e10b37664a5ee1799444eee8c5b Release 3-RS86 (19th August 2018) of the Genome Taxonomy Database (GTDB), an initiative to establish a standardised microbial taxonomy based on genome phylogeny. 29144121344 Dataset 8020b6f824e38c9937d669c52cd7a577dca63f65 https://academictorrents.com/details/8020b6f824e38c9937d669c52cd7a577dca63f65 https://academictorrents.com/details/8020b6f824e38c9937d669c52cd7a577dca63f65 Release 1-RS80 (1st November 2017) of the Genome Taxonomy Database (GTDB), an initiative to establish a standardised microbial taxonomy based on genome phylogeny. 3929014272 Dataset 3b2c4748e377147559c0a9fc6b2e30c40aed166f https://academictorrents.com/details/3b2c4748e377147559c0a9fc6b2e30c40aed166f https://academictorrents.com/details/3b2c4748e377147559c0a9fc6b2e30c40aed166f Release 2-RS83 (8th March 2018) of the Genome Taxonomy Database (GTDB), an initiative to establish a standardised microbial taxonomy based on genome phylogeny. 28099739648 Dataset 642f971d1229cbf0cbe1de5903c969fb82cc4365 https://academictorrents.com/details/642f971d1229cbf0cbe1de5903c969fb82cc4365 https://academictorrents.com/details/642f971d1229cbf0cbe1de5903c969fb82cc4365 Release 11-RS232 (15th April 2026) of the Genome Taxonomy Database (GTDB), an initiative to establish a standardised microbial taxonomy based on genome phylogeny. 611596632064 Dataset bac5df1f39fd83fc87826a8dc546e56db34f2322 https://academictorrents.com/details/bac5df1f39fd83fc87826a8dc546e56db34f2322 https://academictorrents.com/details/bac5df1f39fd83fc87826a8dc546e56db34f2322 English Wikipedia Multistream 2026-06-01 https://en.wikipedia.org/wiki/Wikipedia:Database_download Corresponding index file: https://academictorrents.com/details/c1236c4d35b6d2adcba502e3271d6a3c5261b1ab 26437250146 Dataset c1236c4d35b6d2adcba502e3271d6a3c5261b1ab https://academictorrents.com/details/c1236c4d35b6d2adcba502e3271d6a3c5261b1ab https://academictorrents.com/details/c1236c4d35b6d2adcba502e3271d6a3c5261b1ab English Wikipedia Multistream Index 2026-06-01 https://en.wikipedia.org/wiki/Wikipedia:Database_download Corresponding multistream file: https://academictorrents.com/details/bac5df1f39fd83fc87826a8dc546e56db34f2322 281979710 Dataset a1d810fbb54c21dfe8a68538d917c668e48663de https://academictorrents.com/details/a1d810fbb54c21dfe8a68538d917c668e48663de https://academictorrents.com/details/a1d810fbb54c21dfe8a68538d917c668e48663de Places in the Wild comprises over 67,000 RAW-format images, each captured with a 45-megapixel Canon EOS R5 full-frame mirrorless camera at 5-degree intervals, providing 360-degree coverage across over 800 unique locations. These locations span 260 basic-level scene categories, including both indoor and outdoor environments such as bedrooms, train stations, forests, and parking garages. 2618655068160 Dataset e59a4244be98b0123c47f4205c94c95123318935 https://academictorrents.com/details/e59a4244be98b0123c47f4205c94c95123318935 https://academictorrents.com/details/e59a4244be98b0123c47f4205c94c95123318935 Ultrasound Videos Database: Collection of 32 medical ultrasound video files for simulations, case discussions, and training. Includes cardiac normal, tamponade, FAST exams (RUQ free fluid), AAA, and Edus2 open-source set. Free for non-commercial educational use. This license applies to all video in this directory. Copyright 2011,2012 Paul Kulyk and Paul Olszynski All videos made available under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. http://creativecommons.org/licenses/by-nc-sa/3.0/ 121597065 Dataset 85d017ddd06920534187e7d45f21c7cec90c9bca https://academictorrents.com/details/85d017ddd06920534187e7d45f21c7cec90c9bca https://academictorrents.com/details/85d017ddd06920534187e7d45f21c7cec90c9bca Reddit comments and submisReddit comments and submissions from 2026-04 Documentation, json schemas and more can be found at https://github.com/ArthurHeitmann/arctic_shift Helper scripts for processing files can be found at https://github.com/Watchful1/PushshiftDumpssions 68544608390 Dataset 0d6c1cb68beb572c88f302048be4f2917d226168 https://academictorrents.com/details/0d6c1cb68beb572c88f302048be4f2917d226168 https://academictorrents.com/details/0d6c1cb68beb572c88f302048be4f2917d226168 Wikipedia database dumps of Asian language wikis of 10k articles or more. Wikipedia Multistream 2026-05-01. These 85 languages are included: Acehnese, Armenian, Assamese, Azerbaijani, Balinese, Bangla, Banjar, Banyumasan, Bashkir, Bishnupriya, Buginese, Burmese, Cantonese, Cebuno, Central Bikol, Central Kurdhish, Chechen, Chinese, Chuvash, Classical Chinese, Dimli, Eastern Mari, Georgian, Gilaki, Gorontalo, Gujarati, Hakka, Hebrew, Hindi, Iloko, Indonesian, Japanese, Javanese, Kannada, Kara-Kalpak, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Maithili, Malay, Malayalam, Manipuri, Marathi, Mazanderani, Minangkabau, Mindong, Mingrelian, Minnan, Mongolian, Nepali, Newari, Odia, Ossetic, Pampangan, Pashto, Persian, Punjabi, Russian, Sanskrit, Santali, Saraiki, Shan, Sindhi, Sinhala, South Azerbaijani, Sundanese, Tagalog, Tajik, Talysh, Tamil, Tatar, Telugu, Thai, Turkish, Urdu, Uzbek, Vietnamese, Waray, Western Armenian, Western Mari, Western Punjabi, Wu, Yakut. 31820494896 Dataset 95d3cd024872ccb240b867afb7ba4ea275a9d7a8 https://academictorrents.com/details/95d3cd024872ccb240b867afb7ba4ea275a9d7a8 https://academictorrents.com/details/95d3cd024872ccb240b867afb7ba4ea275a9d7a8 This data dump is sourced from the various sites in the Stack Exchange network of Q&A sites. This dump contains data up to and including 2026-03-31. The exact licenses for each bit of content is embedded in each entry. For license date ranges, see the root-level license.txt, or https://stackoverflow.com/help/licensing. For the schema, see the sede-and-data-dump-schema.md file within each .7z This torrent has also been archived at https://archive.org/details/stackexchange_20260331 98446659547 Dataset f7b5259dfeadf9869d919276006f53c1969c74cd https://academictorrents.com/details/f7b5259dfeadf9869d919276006f53c1969c74cd https://academictorrents.com/details/f7b5259dfeadf9869d919276006f53c1969c74cd In 2018, the World Health Organization (WHO) published 56 recommendations to improve the quality of intrapartum care and enhance women’s childbirth experiences. In response, the WHO developed the Labour Care Guide (LCG) in 2020, a next-generation tool designed to promote evidence-based, respectful, and woman-centered care during labor and delivery. The LCG was created through expert consultations, primary research with maternity healthcare providers, and usability studies across multiple countries. It serves as a practical tool for monitoring labor progress and maternal and fetal well-being by recording key clinical parameters. When deviations from normal labor progression are detected, the LCG highlights these issues, prompting timely interventions to ensure safe and effective care. Intrapartum ultrasound for labor progression analysis is a crucial examination in labor management. The core operation in this analysis is the identification of landmarks from intrapartum ultrasound images. These landmarks serve as the basis for subsequent qualitative evaluations of angles and distances, which offer valuable diagnostic information regarding labor arrest and influence decisions about the timing and type of intervention. However, obtaining reliable landmark annotations typically demands experienced physicians, and even for proficient obstetricians, manual landmark identification is a time-consuming and labor-intensive endeavor. Consequently, the development of fully automatic and precise landmark localization techniques has been an area of significant and persistent need. The Intrapartum Ultrasound Grand Challenge (IUGC) 2025 is a collaborative initiative involving the "Deep Learning in Intrapartum Ultrasound Image Analysis" cooperative group and prominent clinical societies such as the International Society of Ultrasound in Obstetrics & Gynecology (ISUOG), the World Association of Perinatal Medicine (WAPM), the Perinatal Medicine Foundation (PMF), and the National Institute for Health and Care-Excellence (NICE). The objective of this partnership is to formulate and promote clinically relevant challenges, thereby maximizing the potential clinical impact of innovative algorithmic contributions from participating teams. Since its inception at MICCAI 2023, the IUGC has advanced the Pubic Symphysis - Fetal Head Segmentation (PSFHS) by facilitating and benchmarking algorithmic progress and providing high-quality annotated image datasets. In MICCAI 2024, the IUGC expanded to incorporate multiple benchmarks: (1) The analysis objects were extended from images to videos; (2) The tasks were augmented from image segmentation to classification, segmentation, and biometric parameter measurement; (3) The quantitative parameters were increased from one (i.e., Angle of Progression (AOP)) to two (i.e., AOP and head - symphysis distance (HSD)); and (4) The data sources were broadened from being solely from Asia to include Asia, Europe, and Africa. This novel and inventive design has established a benchmarking ecosystem for the systematic comparison of algorithms across diverse tasks and clinical challenges. The significance of the IUGC 2025 lies in its concentration on addressing the actual clinical assessment of labor progress, covering (1) end-to-end measurements (which are currently indirect measurements based on segmentation results); (2) all fetal descent stations during the childbirth process (comprising five “minus”, one “zero", and three “plus” stations for reliable longitudinal assessment of labor progress); (3) computational tasks (such as regression, detection); and (4) learning methods (semi-supervised, weakly-supervised, and barely-supervised learning). In line with the IUGC s goal of addressing clinical requirements, authoritative and leading clinical organizations have allied with the IUGC. We have extended the IUGC 2024 Challenge from an indirect ultrasound measurement based on segmentation results to an end-to-end measurement based on landmarks. Specifically, we provide 300 labeled cases and 31,421 unlabeled cases in the training set, 100 visible cases for validation, and 501 hidden cases for testing. The targets are the coordinates of three landmarks and the corresponding biometric parameter. In addition to the typical Mean Radial Error (MRE) and the absolute difference between predicted and manually measured parameters, our evaluation metrics also emphasize inference speed. In summary, the IUGC 2025 challenge exhibits three primary characteristics: (1) Task: Employing semi-supervised landmark detection. (2) Dataset: Curating a large-scale and diverse fetal ultrasound dataset that accounts for all fetal descent stations during the childbirth process. It comprises 28,919 ultrasound images from over 20 medical groups. (3) Evaluation measures: Focusing on detection accuracy. (4) Multiple raters independently annotate a subset of test cases to compare algorithmic performance against human expert inter-rater variability. https://pubmed.ncbi.nlm.nih.gov/41604894/ Bai J, Tang Y, Liu X, Hu J, Li Y, Chen X, Wang Y, Ma C, Li Y, Guo B, Jiao J, Huang Y, Wang K, Li L, Ma Y, Han X, Shao H, Yang Z, Liu Q, Hu Y, Kuang J, Song S, Krishna A, Khan ZA, Li Z, Zhang Z, Zhang H, Cheng Y, Zhang X, Chen X, Yan H, Tong L, Du B, Deng B, Chen Y, Peng Z, Rezaei S, Gan J, Cai W, Wang F, Curran KM, Silvestre G, Khobo I, Lu Y, Ni D, Huang Y, Yaqub M, Ma J, Lekadir K, Li S. IUGC: A benchmark of landmark detection in end-to-end intrapartum ultrasound biometry. Med Image Anal. 2026 May;110:103960. doi: 10.1016/j.media.2026.103960. Epub 2026 Jan 23. PMID: 41604894. 1204118461 Dataset cf97c52651867d2e78d234aebb1fa45432ddbe9a https://academictorrents.com/details/cf97c52651867d2e78d234aebb1fa45432ddbe9a https://academictorrents.com/details/cf97c52651867d2e78d234aebb1fa45432ddbe9a We public the ultrasound liver images, which were annotated to show the outlines, livers, and liver mass regions. Xu Yiming, Zheng Bowen, Liu Xiaohong, Wu Tao, Ju Jinxiu, Wang Shijie, Lian Yufan, Zhang Hongjun, Liang Tong, Sang Ye, Jiang Rui, Wang Guangyu, Ren Jie, & Chen Ting. (2022). Annotated Ultrasound Liver images [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7272660 70369237 Dataset aff393b0688d0bf396407cb8d02b4284879c1cf7 https://academictorrents.com/details/aff393b0688d0bf396407cb8d02b4284879c1cf7 https://academictorrents.com/details/aff393b0688d0bf396407cb8d02b4284879c1cf7 We propose TRUSTED (the Tridimensional Renal Ultra Sound Tomod Ensitometrie Dataset), comprising paired transabdominal 3DUS and CT kidney images from 48 human patients (96 kidneys), including segmentation, and anatomical landmark annotations by two experienced radiographers. Abstract Inter-modal image registration (IMIR) and image segmentation with abdominal Ultrasound (US) data have many important clinical applications, including image-guided surgery, automatic organ measurement, and robotic navigation. However, research is severely limited by the lack of public datasets. We propose TRUSTED (the Tridimensional Renal Ultra Sound TomodEnsitometrie Dataset), comprising paired transabdominal 3DUS and CT kidney images from 48 human patients (96 kidneys), including segmentation, and anatomical landmark annotations by two experienced radiographers. Inter-rater segmentation agreement was over 93% (Dice score), and gold-standard segmentations were generated using the STAPLE algorithm. Seven anatomical landmarks were annotated, for IMIR systems development and evaluation. To validate the dataset’s utility, 4 competitive Deep-Learning models for kidney segmentation were benchmarked, yielding average DICE scores from 79.63% to 90.09% for CT, and 70.51% to 80.70% for US images. Four IMIR methods were benchmarked, and Coherent Point Drift performed best with an average Target Registration Error of 4.47 mm and Dice score of 84.10%. The TRUSTED dataset may be used freely to develop and validate segmentation and IMIR methods. Ndzimbong, W., Fourniol, C., Themyr, L. et al. TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research. Sci Data 12, 615 (2025). https://doi.org/10.1038/s41597-025-04467-1 15987228105 Dataset fb252da63e5ba59bea91821018e5c83d172346ba https://academictorrents.com/details/fb252da63e5ba59bea91821018e5c83d172346ba https://academictorrents.com/details/fb252da63e5ba59bea91821018e5c83d172346ba This is a dataset of Ultrasound (US) images of abdominal organs. US imaging is widely accessible and a very common diagnostic tool, as it is non-invasive and does not involve radiation risk. This dataset was curated solely for research in deep learning, with potential applications in supervised, semi-supervised, and unsupervised learning to support disease detection in resource-constrained settings. The dataset comprises 5,005 images of different abdominal organs, namely: Abdominal Aorta (0), Gallbladder (1), Hepatic Vein (2), Kidneys (3), Liver (4), Ovaries (5), Pancreas (6), Portal Vein (7), Spleen (8), and the Urinary System (9), which includes the Urinary Bladder, Prostate, and Uterus. Images were collected from 563 patients at MH Samorita Medical College and Hospital and in Dhaka, Bangladesh. The author tried her best to curate this dataset systematically and organize uniquely. Every folder and subfolder has correctly numbered, serially ordered images, making it the first dataset one of its kind in terms of structure and usability. This ensures reproducibility and reliability for researchers worldwide In total, the dataset is organized into five distinct formats/folders, described below. Two radiologists were examining the patients. ## Radiologist one: - organ_classification_1: Contains 2,784 images of the 10 organs listed above. Designed for classification tasks. - anomaly_detection_1: Contains two sub-folders: normal (2,014 images) and abnormal (799 images). Designed for anomaly detection tasks. - organ_classification+anomaly_detection: Contains 2 sub-folders. One represents the normal organs (1,948 images) and one represents abnormal cases (981 images) including a newly added ascites folder. Ascites is a condition where the abdominal cavity becomes overly filled with fluid, and it was separated as a distinct abnormal class. This hybrid dataset (organ_classification+anomaly_detection) is an experimental extension combining both tasks. While curated carefully, users are advised to double-check labels for their specific tasks. ## Radiologist two: - anomaly_detection_2: Contains two sub-folders: normal (656 images) and abnormal (269 images). This batch was collected last and was used for semi-supervised anomaly detection tasks. - organ_classification_2: Contains 10 sub-folders representing the 10 different organs, with a total of 1,293 images. - Patient_Wise: This folder contains 170 patient images, their diagnosis as metadata in an xlsx file and a text file, Update Version 01_USG.txt. ## Acknowledgements: This dataset was developed as part of the author s research under the supervision of Dr. John E. Ball, Mississippi State University. The author is grateful for his guidance, encouragement, and support throughout the course of this work. The author would like to thank logistical support of her father, Dr. Md. Enayet Karim for his support in coordinating with MH Samorita Medical College and Hospital to obtain the ultrasound images and metadata for this dataset. The author gratefully acknowledges the contributions of the radiologists, sonographers, and staff at MH Samorita Medical College and Hospital, for their assistance in conducting the ultrasound examinations and providing access to the imaging data. Their efforts in patient care and technical support were invaluable in making this dataset possible. Ethics and Data Access Permissions: This dataset was collected with formal approval from the Institutional Ethical Review Board (IERB) of MH Samorita Medical College and Hospital, Dhaka, Bangladesh. The ethical clearance was obtained before data collection and written institutional permission was granted before sharing. All images were anonymized and de-identified before inclusion in this dataset, ensuring patient privacy and compliance with ethical research standards. ## License: This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Users are required to cite this dataset when using it in publications, research, or derivative works. This dataset is openly available for research and academic purposes, supporting reproducibility and transparency in medical AI research. https://orcid.org/0009-0005-8413-4149 1491269509 Dataset 668087bb8c8c9c763b27a1a4c5e7fcb6add25f2c https://academictorrents.com/details/668087bb8c8c9c763b27a1a4c5e7fcb6add25f2c https://academictorrents.com/details/668087bb8c8c9c763b27a1a4c5e7fcb6add25f2c Reddit comments and submisReddit comments and submissions from 2026-03 Documentation, json schemas and more can be found at https://github.com/ArthurHeitmann/arctic_shift Helper scripts for processing files can be found at https://github.com/Watchful1/PushshiftDumpssions 68404242141 Dataset aa2092fb6910c90c467a62293f74042a6ff7d251 https://academictorrents.com/details/aa2092fb6910c90c467a62293f74042a6ff7d251 https://academictorrents.com/details/aa2092fb6910c90c467a62293f74042a6ff7d251 The dataset is composed of ultrasound images of the GB organ from inside the gastrointestinal tract. The dataset includes 9 classes according to anatomical landmarks. Each class represents a GB disease. Published: 23 January 2024 | Version 1 | DOI: 10.17632/r6h24d2d3y.1 Turki, Amina; Mahdi Obaid, Ahmed; Bellaaj, Hatem; Ksantini, Mohamed; Altaee, Abdulla (2024), “Gallblader Diseases Dataset ”, Mendeley Data, V1, doi: 10.17632/r6h24d2d3y.1 "The UIdataGB dataset consists of 10692 images, annotated, and verified by medical doctorsand experienced radiologists. It includes 9 classes according to anatomical landmarks. Each classcontains nearly 1200 images. Therefore, the dataset is balanced in terms of diseases. In total,1782 patients were involved in the data collection; the number of female images was 6246,with an average age of 63.4, while the number of male images was 4446, with an average ageof 59.6.The number of images is sufficient to be used for different tasks, e.g., image retrieval, ML, DL,and transfer learning (TL), etc. The anatomical landmark of the GB determines the pathologicalfinding like cholecystitis, stone of the GB and polyps.The dataset consists of images with a resolution of 90 0×120 0 pixels and they are sorted intoseparate nine folders named according to the content. Tables 1 and 2 show the distribution ofdiseases in terms of images and patients’ numbers as well as the distribution of images accord-ing to gender." https://www.sciencedirect.com/science/article/pii/S2352340924003950 2042724460 Dataset 8ed9dcc05b0fdecb47f998186e9e5c30f8212cfc https://academictorrents.com/details/8ed9dcc05b0fdecb47f998186e9e5c30f8212cfc https://academictorrents.com/details/8ed9dcc05b0fdecb47f998186e9e5c30f8212cfc English Wikipedia Multistream Index 2026-04-01 https://en.wikipedia.org/wiki/Wikipedia:Database_download Corresponding multistream file: https://academictorrents.com/details/2b2ffc80941b61dcfe55fc444a6a78a60eef5944 280349081 Dataset 2b2ffc80941b61dcfe55fc444a6a78a60eef5944 https://academictorrents.com/details/2b2ffc80941b61dcfe55fc444a6a78a60eef5944 https://academictorrents.com/details/2b2ffc80941b61dcfe55fc444a6a78a60eef5944 English Wikipedia Multistream 2026-04-01 https://en.wikipedia.org/wiki/Wikipedia:Database_download Corresponding index file: https://academictorrents.com/details/8ed9dcc05b0fdecb47f998186e9e5c30f8212cfc 26207949994 Dataset 22508526b4bf4b08641b3dbba010eb083388322c https://academictorrents.com/details/22508526b4bf4b08641b3dbba010eb083388322c https://academictorrents.com/details/22508526b4bf4b08641b3dbba010eb083388322c A dataset of contrast transthoracic echocardiography, EchoCP, for patent foramen ovale diagnosis is published. We present EchoCP, the first dataset for cTTE based PFO diagnosis. EchoCP contains both VM and rest echocardiography videos captured from 30 patients. Data annotation including diagnosis annotation and segmentation annotation are performed by four experienced cardiovascular sonographers. As there are more than a thousand images in each patient s video, sparse labeling (only select representative frames) of the segmentation is adopted. EchoCP contains cTTE videos from 30 patients. For each patient, two videos corresponding to the rest and VM state of the patients are captured. Note that in the rest state, patients just relax and breathe normally. While in the VM, patients need to close their mouths, pinch their noses shut while expelling air out as if blowing up a balloon. The video is captured in the apical-4-chamber view and contains at least ten cardiac cycles. For the VM state, the action is performed three to five times during acquisition, and we selected the most representative one. If you used our dataset, please consider to cite our paper in MICCAI 2021, Tianchen Wang, Zhihe Li, Shanshan Bi, Meiping Huang, Jiawei Zhang, Jian Zhuang, Yiyu Shi, Hongwen Fei, Xiaowei Xu, "ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease," in Proc. of Medical Image Computing and Computer Assisted Interventions (MICCAI), Online, 2021. https://arxiv.org/abs/2101.10799 HIGHLIGHT 20231101: We have deployed the dataset on Kaggle! Please send emails to me xiao.wei.xu@foxmail.com if you have any questions about the dataset and the benchmark. 5554695068 Dataset 55cce2068badb8204b5de896922afc301c37a691 https://academictorrents.com/details/55cce2068badb8204b5de896922afc301c37a691 https://academictorrents.com/details/55cce2068badb8204b5de896922afc301c37a691 We collect CardiacUDA from our two hospitals: site G and site R. In order to guarantee all echocardiogram videos are standardscompliant, all cases of CardiacUDA are collected, annotated and approved by 5-6 experienced physicians. For ethical issues, we have required approval from medical institutions. Each patient underwent four views during scanning, which included parasternal left ventricle long axis (LVLA), pulmonary artery long axis (PALA), left ventricular short-axis (LVSA), and apical fourchamber heart (A4C), resulting in four videos per patient. The resolution of each video was either 800x600 or 1024x768, depending on the scanner used (Philips or HITACHI). A total of 516 and 476 videos were collected from Site G and Site R, respectively, from approximately 100 different patients. Each video consists of over 100 frames, covering at least one heartbeat cycle. We have provided pixel-level annotations for each view, including masks for the left ventricle (LV) and right ventricle (RV) in the LVLA view, masks for the pulmonary artery (PA) in the PALA view, masks for the LV and RV in the LVSA view, and masks for the LV, RV, left atrium (LA), and right atrium (RA) in the A4C view. The videos in both Site R and Site G were divided into a ratio of 8:1:1 for training, validation, and testing, respectively. To lower annotation costs in the training set, only five frames per video are provided with pixellevel annotation masks. To better measure the model performance, we provide pixel-level annotations for every frame in each video in the validation and testing sets. HIGHLIGHT 20231101: We have deployed the dataset on Kaggle! Please refer to the code (https://github.com/xmed-lab/GraphEcho) and our ICCV paper (https://arxiv.org/abs/2309.11145) for more detailes. Please send emails to me xiao.wei.xu@foxmail.com if you have any questions about the dataset and the benchmark. 4547904911