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DE-SynPUF is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,300,000 persom (2.3m) data sets in the OMOP Common Data Model format. The DE-SynPUF was created with the goal of providing a realistic set of claims data in the public domain while providing the very highest degree of protection to the Medicare beneficiaries’ protected health information. The purposes of the DE-SynPUF are to: allow data entrepreneurs to develop and create software and applications that may eventually be applied to actual CMS claims data; train researchers on the use and complexity of conducting analyses with CMS claims data prior to initiating the process to obtain access to actual CMS data; and, support safe data mining innovations that may reveal unanticipated knowledge gains while preserving beneficiary privacy. The files have been designed so that programs and procedures created on the DE-SynPUF will function on CMS Limited[...]

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The Sea Surface Temperature (SST) sub-facility produces 1-minute average data products. Observed data are 1-minute median SST values and are retrieved from the vessel once an hour. High-resolution 1-minute median data are available in the delayed mode approximately every 3 months, these were produced in delayed mode using visual (manual) inspection of the quality flags after the data had been run through the automated quality control software at the Bureau of Meteorology The data products are produced from data from 3 ships: L'Astrolabe, Xutra Buhm and Wana Buhm, within this sub-facility working in the Australian region, Southern Ocean and South-East Asia. L'Astrolabe is a research vessel operating in Antarctic and sub-Antarctic waters during the summer period (usually October through March). 1-minute averaged SST (from the SBE 38 temperature sensor) and thermosalinograph salinity data are logged on the vessel and manually retrieved by CMAR staff after the voyage is complete[...]

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This both the original .tfrecords and a Parquet representation of the YouTube 8 Million dataset. YouTube-8M is a large-scale labeled video dataset that consists of millions of YouTube video IDs, with high-quality machine-generated annotations from a diverse vocabulary of 3,800+ visual entities. It comes with precomputed audio-visual features from billions of frames and audio segments, designed to fit on a single hard disk. This dataset also includes the YouTube-8M Segments data from June 2019. This dataset is 'Lakehouse Ready'. Meaning, you can query this data in-place straight out of the Registry of Open Data S3 bucket. Deploy this dataset's corresponding CloudFormation template to create the AWS Glue Catalog entries[...]

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The Biodiversity Heritage Library (BHL) is the world’s largest open access digital library for biodiversity literature and archives. BHL operates as a worldwide consortium of natural history, botanical, research, and national libraries working together to address this challenge by digitizing the natural history literature held in their collections and making it freely available for open access.

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Integrated Marine Observing System (IMOS) have moorings across both it's National Mooring Network and Deep Water Moorings facilities. The National Mooring Network facility comprises a series of national reference stations and regional moorings designed to monitor particular oceanographic phenomena in Australian coastal ocean waters. The Deep Water Moorings facility (formerly known as the Australian Bluewater Observing System) provides the coordination of national efforts in the sustained observation of open ocean properties with particular emphasis on observations important to climate and carbon cycle studies, with selected moorings from its Deep Water Arrays sub-facility providing data to this collection. This collection represents the hourly aggregated time-series product, which combines selected variables measured by all the instruments deployed at a site, binning the values into a fixed one-hour interval. Only good-quality measurements (according to the automated quality-contr[...]

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This bucket contains the checkpoints used to reproduce the baseline results reported in the DialoGLUE benchmark hosted on EvalAI (https://evalai.cloudcv.org/web/challenges/challenge-page/708/overview). The associated scripts for using the checkpoints are located here: https://github.com/alexa/dialoglue. The associated paper describing the benchmark and checkpoints is here: https://arxiv.org/abs/2009.13570. The provided checkpoints include the CONVBERT model, a BERT-esque model trained on a large open-domain conversational dataset. It also includes the CONVBERT-DG and BERT-DG checkpoints described in the linked paper.

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PD12M is a collection of 12.4 million CC0/PD image-caption pairs for the purpose of training generative image models.

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Amazon product questions and their answers, along with the public product information.

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MultiCoNER 1 is a large multilingual dataset (11 languages) for Named Entity Recognition. It is designed to represent some of the contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities such as movie titles, and long-tail entity distributions. MultiCoNER 2 is a large multilingual dataset (12 languages) for fine grained Named Entity Recognition. Its fine-grained taxonomy contains 36 NE classes, representing real-world challenges for NER, where named entities, apart from the surface form, context represents a critical role in distinguishing between the different fine-grained types (e.g. Scientist vs. Athlete). Furthermore, the test data of MultiCoNER 2 contains noisy instances, where the noise has been applied to both context tokens as well as the entity tokens. The noise includes typing errors at character level based on keyboard layouts in the the different languages.

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Crop field boundaries digitized in Planet imagery collected across Africa between 2017 and 2023, developed by Farmerline, Spatial Collective, and the Agricultural Impacts Research Group at Clark University, with support from the Lacuna Fund