Recent Torrents https://academictorrents.com/ Course 1e6d57c0d31127fe9b97f5aac62c43c4fd9ffa65 https://academictorrents.com/details/1e6d57c0d31127fe9b97f5aac62c43c4fd9ffa65 https://academictorrents.com/details/1e6d57c0d31127fe9b97f5aac62c43c4fd9ffa65 This course is an introduction to mathematical modeling of computational problems, as well as common algorithms, algorithmic paradigms, and data structures used to solve these problems. It emphasizes the relationship between algorithms and programming and introduces basic performance measures and analysis techniques for these problems. Main course URL: https://ocw.mit.edu/6-006S20 This content hosted at the Internet Archive at https://archive.org/details/MIT6.006S20 Files may have changed, which prevents torrents from downloading correctly or completely; please check for an updated torrent at https://archive.org/download/MIT6.006S20/MIT6.006S20_archive.torrent Note: retrieval usually requires a client that supports webseeding (GetRight style). Note: many Internet Archive torrents contain a pad file directory. This directory and the files within it may be erased once retrieval completes. Note: the file MIT6.006S20_meta.xml contains metadata about this torrent s contents. 5117050880 Dataset cab03c1b0cbc8ed99a0495d491d281d80517146a https://academictorrents.com/details/cab03c1b0cbc8ed99a0495d491d281d80517146a https://academictorrents.com/details/cab03c1b0cbc8ed99a0495d491d281d80517146a Companion SQLite database for the Cara.app Art Dataset image archive. ## Full image database: https://academictorrents.com/details/77d4e2a65852258420a8e47bf29c8f3a5043a446 Cara.app Art Dataset — 123,056 Original Images from Top Community Posts (112 GB) Covers all 52,045 posts with over 150 likes on Cara.app (as of August 2026) and contains: full post records (title, body text, timestamps, like/comment/repost counters, software tags, topic tags, flair, polls, mention and quote/repost links, portfolio flags); 162,484 image rows (CDN URLs matching the image archive, carousel order, cover flags, dimensions where recovered, per-image AI-generation flags); 674,762 comments with complete nested thread structure, text, timestamps and like counts; and 5,775 author profiles (display name, bio, website, follower/following counts, account state). —- ## How to link image files to metadata (filename lookup) Every file in the images folder maps to exactly one row in the images table by its **basename**, which carries post_id — from there you reach the full post and author. **SQL (any sqlite3 client):** sql — all metadata for one downloaded file: SELECT i.*, p.title, p.content, p.created_at, p.like_counter, u.name AS author_name, u.slug AS author_slug, u.follower_counter FROM images i 403398656 Dataset 77d4e2a65852258420a8e47bf29c8f3a5043a446 https://academictorrents.com/details/77d4e2a65852258420a8e47bf29c8f3a5043a446 https://academictorrents.com/details/77d4e2a65852258420a8e47bf29c8f3a5043a446 # **companion metadata database**: https://academictorrents.com/details/cab03c1b0cbc8ed99a0495d491d281d80517146a This archive contains 123,056 original artwork image files (~120 GB, JPG/PNG/GIF/WEBP) belonging to the 52,047 most-liked posts (over 150 likes each) on Cara.app, an art-sharing and portfolio platform popular among professional and hobbyist illustrators. Images were collected in August 2026 from Cara s public content-delivery network and are provided at original uploaded resolution. Each file is uniquely named by its CDN path and maps one-to-one to rows in the companion metadata database ( subset_dataset.db , distributed separately) via its filename. The **companion database**: https://academictorrents.com/details/cab03c1b0cbc8ed99a0495d491d281d80517146a (Cara.app Art Dataset — Full Metadata Database for 52,045 Top Posts with 674k Comment Trees (SQLite)) provides per-image carousel ordering, cover flags, pixel dimensions where recoverable, platform AI-generation flags, full post text and engagement metrics, complete comment threads, and author profiles. Intended uses include computer-vision research, art-market and style-trend analysis, AI-vs-human classification studies, and digital-arts historiography. —- ## How to link image files to metadata (filename lookup) Every file in the images folder maps to exactly one row in the images table by its **basename**, which carries post_id — from there you reach the full post and author. **SQL (any sqlite3 client):** sql — all metadata for one downloaded file: SELECT i.*, p.title, p.content, p.created_at, p.like_counter, u.name AS author_name, u.slug AS author_slug, u.follower_counter FROM images i 119981202380 Dataset 794176de24c33cc0e8b629b45a746cd870f3e95f https://academictorrents.com/details/794176de24c33cc0e8b629b45a746cd870f3e95f https://academictorrents.com/details/794176de24c33cc0e8b629b45a746cd870f3e95f 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 2026-06-30. 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_20260630_sakura 99396616192 Dataset ba051999301b109eab37d16f027b3f49ade2de13 https://academictorrents.com/details/ba051999301b109eab37d16f027b3f49ade2de13 https://academictorrents.com/details/ba051999301b109eab37d16f027b3f49ade2de13 Reddit comments and submissions from 2005-06 to 2024-12 collected by pushshift and u/RaiderBDev. These are zstandard compressed ndjson files. Example python scripts for parsing the data can be found here https://github.com/Watchful1/PushshiftDumps The more recent dumps are collected by u/RaiderBDev License: No license specified, the work may be protected by copyright. 3124743990853 Dataset e04a4fda12826ab1d181eef6512b36aca63c70ff https://academictorrents.com/details/e04a4fda12826ab1d181eef6512b36aca63c70ff https://academictorrents.com/details/e04a4fda12826ab1d181eef6512b36aca63c70ff Reddit comments and submisReddit comments and submissions from 2026-07 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 80272561702 Dataset a77baccadceb644bed72cdbfb7cca22a08b493f7 https://academictorrents.com/details/a77baccadceb644bed72cdbfb7cca22a08b493f7 https://academictorrents.com/details/a77baccadceb644bed72cdbfb7cca22a08b493f7 Large language models (LLMs) are increasingly consulted for life-path guidance, yet the distribution of options they surface has gone largely unexamined. We characterize occupational-category visibility across 165,000 LLM responses spanning 100 user profiles, 15 prompts (grouped into seven framings), 11 models across seven families, two role framings, and five temperatures, with keywords derived automatically from Bureau of Labor Statistics (BLS) occupation titles. Trades & Labor occupations, roughly 40% of projected job openings, account for only 6% of surfaced visibility, the largest gap in the data, while Education and STEM are over-visible (31% and 14%) relative to their labor-market shares, and Business is under-visible (19% vs. 31%). Re-weighting by entry-level education exposes a sharper credentialing skew, in which LLM-implied openings at the Bachelor s-or-above level reach 44.8% against a BLS share of 20.6%, and this skew tracks socioeconomic profile signals. This signal is conditional: Low-Income responses discuss an education tier far more often than Wealthy/Privileged ones (28.9% vs. 4.1%), and among tier-mentioning responses Bachelor s-or-above is far rarer for Low-Income profiles (8.3% vs. 73.4% of mentions), carrying a 50% education-implied wage gap that the occupational-category channel does not produce. Across all responses, however, the two groups reach Bachelor s-or-above guidance at comparable rates (4.2% vs. 3.1%). The aggregate distribution lies closest (Jensen–Shannon divergence 0.021) to open-web "General Advice / Blogs" content and far from the BLS labor-market structure, consistent with web-text mirroring. The skew is stable across model families and temperatures, though prompt framing shifts which categories surface. 151257088 Dataset 3bac8bd352bbb74bbb23df4273cf3da5d66ee5a5 https://academictorrents.com/details/3bac8bd352bbb74bbb23df4273cf3da5d66ee5a5 https://academictorrents.com/details/3bac8bd352bbb74bbb23df4273cf3da5d66ee5a5 Reddit comments and submisReddit comments and submissions from 2026-06 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 70377849294 Dataset cab7744573688b0b39c521ef66453435785762ac https://academictorrents.com/details/cab7744573688b0b39c521ef66453435785762ac https://academictorrents.com/details/cab7744573688b0b39c521ef66453435785762ac This database provides the US ground truth of the left and right LM muscles at the L5 level (in prone and standing positions) of 109 US datasets of young athletic adult volunteers (64 males, 45 females, age: 21.1 ± 1.7). The LUMINOUS database contains the US images with their corresponding manually segmented binary masks, serving as the ground truth. The purpose of the database is to enable development and validation of deep learning algorithms used for automatic segmentation tasks related to the assessment of the LM cross-sectional area (CSA) and echo intensity (EI). The 109 athletes underwent a US procedure to obtain LM images at the L5 level in both the prone and standing positions. The LOGIQ e ultrasound machine (GE Healthcare, Milwaukee, WI) was used with a curvilinear probe with its imaging parameters maintained at the following values for all image acquisitions: frequency: 5 MHz, gain: 60, depth: 8.0 cm. To assess LM CSA, transverse US images were obtained bilaterally. For subjects with larger muscles, the right and left sides were imaged unilaterally. (a) B-mode image of subject 50 (acquired unilaterally) with corresponding segmentation of the left MF in the prone position shown in (b). (c) B-mode image of subject 50 (acquired unilaterally) with corresponding segmentation of the left MF in the standing position shown in (d) (a) B-mode image of subject 46 (acquired bilaterally) with corresponding segmentations of the left and right MF in the prone position shown in (b) The ground truth segmentations of LM CSA and LM EI measurements in prone and standing positions were performed on the acquired data using Fiji, a distribution of the ImageJ image processing software. The B-mode images and binary segmentation masks for each subject are deposited as *.tif files. https://users.encs.concordia.ca/~impact/wp-content/uploads/2020/07/1-550x400.png 60553822 Dataset c0f740a1261fb1d646bf4df1395c86581399d879 https://academictorrents.com/details/c0f740a1261fb1d646bf4df1395c86581399d879 https://academictorrents.com/details/c0f740a1261fb1d646bf4df1395c86581399d879 FAscicle Lower Leg Muscle Ultrasound Dataset is a dataset composed of 812 ultrasound images of lower leg muscles to analyze muscle weaknesses and prevent injuries. This dataset is presented in the article AW-Net: Automatic muscle structure analysis on B-mode ultrasound images for injury prevention. It combines the datasets provided by two articles, “Estimating Full Regional Skeletal Muscle Fibre Orientation from B-Mode Ultrasound Images Using Convolutional, Residual, and Deconvolutional Neural Networks” published by Ryan Cunningham et al. and “Automated Analysis of Musculoskeletal Ultrasound Images Using Deep Learning” published by Neil Cronin, with complementary annotations. The zip file contains the two datasets respectively separated into two folders named by their author. Each image of each dataset has one matching fascicle segmentation mask and one aponeurosis segmentation mask recognizable by name. Instructions for accessing the data set used in the paper "AW-Net: Automatic muscle structure analysis on B-mode ultrasound images for injury prevention". We have provided two folders RyanCunningham and NeilCronin that contain, respectively, the datasets provided in the articles "Estimating Full Regional Skeletal Muscle Fibre Orientation from B-Mode Ultrasound Images Using Convolutional, Residual, and Deconvolutional Neural Networks" published by Ryan Cunningham et al. and "Automated Analysis of Musculoskeletal Ultrasound Images Using Deep Learning" published by Neil Cronin. In each of these folders, you will find the folders : 1) images: contains the images of the dataset 2) fascicle_masks: contains the fascicle masks of the images 3) aponeurosis_masks: contains the aponeurosis masks of the images Note : The fascicle masks and aponeurosis masks can be matched with the corresponding image by using the names of these files. 130725729 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