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amazon.scienceanalyticsdeep learninggeospatiallast milelogisticsmachine learningoptimizationroutingtransportationurban
The 2021 Amazon Last Mile Routing Research Challenge was an innovative research initiative led by Amazon.com and supported by the Massachusetts Institute of Technology’s Center for Transportation and Logistics. Over a period of 4 months, participants were challenged to develop innovative machine learning-based methods to enhance classic optimization-based approaches to solve the travelling salesperson problem, by learning from historical routes executed by Amazon delivery drivers. The primary goal of the Amazon Last Mile Routing Research Challenge was to foster innovative applied research in r...
amazon.sciencebioinformaticsbiologycoronavirusCOVID-19healthlife sciencesmedicineMERSSARS
A centralized repository of up-to-date and curated datasets on or related to the spread and characteristics of the novel corona virus (SARS-CoV-2) and its associated illness, COVID-19. Globally, there are several efforts underway to gather this data, and we are working with partners to make this crucial data freely available and keep it up-to-date. Hosted on the AWS cloud, we have seeded our curated data lake with COVID-19 case tracking data from Johns Hopkins and The New York Times, hospital bed availability from Definitive Healthcare, and over 45,000 research articles about COVID-19 and rela...
amazon.sciencebioinformaticshealthlife sciencesnatural language processingus
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:
amazon.sciencecomputer visionmachine learning
The Amazon Bin Image Dataset contains over 500,000 images and metadata from bins of a pod in an operating Amazon Fulfillment Center. The bin images in this dataset are captured as robot units carry pods as part of normal Amazon Fulfillment Center operations.
amazon.sciencecomputer visionlabeledmachine learningparquetvideo
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...
amazon.sciencebitcoinblockchain
The AWS Public Blockchain Data provide datasets from the Bitcoin and Ethereum blockchains. The blockchain data is transformed into multiple tables as compressed Parquet files partitioned by date to allow efficient access for most common analytics queries.
agricultureamazon.sciencebiologyCaenorhabditis elegansDanio reriogeneticgenomicHomo sapienslife sciencesMus musculusRattus norvegicusreference index
Common reference genomes hosted on AWS S3. Can be used when aligning and analysing raw DNA sequencing data.
amazon.sciencemachine learningnatural language processing
Amazon product questions and their answers, along with the public product information.
amazon.sciencemachine learningnatural language processing
Original StackExchange answers and their voice-friendly Reformulation.
amazon.sciencedeep learningmachine learningnatural language processingspeech recognition
Sentence classification datatasets with ASR Errors.
amazon.sciencecomputer visiondeep learning
The first large public body measurement dataset including 8978 frontal and lateral silhouettes for 2505 real subjects, paired with height, weight and 14 body measurements. The following artifacts are made available for each subject.
amazon.scienceconversation datamachine learningnatural language processing
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 descri...
amazon.scienceconversation datamachine learningnatural language processing
This dataset provides extra annotations on top of the publicly released Topical-Chat dataset(https://github.com/alexa/Topical-Chat) which will help in reproducing the results in our paper "Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems" (https://arxiv.org/abs/2005.12529?context=cs.CL). The dataset contains 5 files: train.json, valid_freq.json, valid_rare.json, test_freq.json and test_rare.json. Each of these files will have additional annotations on top of the original Topical-Chat dataset. These specific annotations are: dialogue act annotations a...
amazon.sciencebioinformaticsfastqgeneticgenomiclife scienceslong read sequencingshort read sequencingwhole exome sequencingwhole genome sequencing
To facilitate benchmarking and development, the Google Brain group has sequenced 9 human samples covering the Genome in a Bottle truth sets on different sequencing instruments, sequencing modalities (Illumina short read and Pacific BioSciences long read), sample preparation protocols, and for whole genome and whole exome capture. The original source of these data are gs://google-brain-genomics-public.
amazon.scienceinformation retrievaljsonnatural language processingtext analysis
A collection of sentences extracted from customer reviews labeled with their helpfulness score.
amazon.sciencemachine learningnatural language processing
This dataset provides labeled humor detection from product question answering systems. The dataset contains 3 csv files: Humorous.csv containing the humorous product questions, Non-humorous-unbiased.csv containing the non-humorous prodcut questions from the same products as the humorous one, and, Details →
amazon.sciencedialogmachine learningnatural language processing
Humor patterns used for querying Alexa traffic when creating the taxonomy described in the paper "“Alexa, Do You Want to Build a Snowman?” Characterizing Playful Requests to Conversational Agents" by Shani C., Libov A., Tolmach S., Lewin-Eytan L., Maarek Y., and Shahaf D. (CHI LBW 2022). These patterns corrospond to the researchers' hypotheses regarding what humor types are likely to appear in Alexa traffic. These patterns were used for querying Alexa traffic to evaluate these hypotheses.
amazon.sciencemachine learningnatural language processing
This dataset provides product related questions and answers, including answers' quality labels, as as part of the paper 'IR Evaluation and Learning in the Presence of Forbidden Documents'.
amazon.sciencenatural language processing
See https://lowcontext-ner-gaz.s3.amazonaws.com/readme.html
amazon.sciencemachine learningnatural language processing
This dataset provides masked sentences and multi-token phrases that were masked-out of these sentences. We offer 3 datasets: a general purpose dataset extracted from the Wikipedia and Books corpora, and 2 additional datasets extracted from pubmed abstracts. As for the pubmed data, please be aware that the dataset does not reflect the most current/accurate data available from NLM (it is not being updated). For these datasets, the columns provided for each datapoint are as follows: text- the original sentence span- the span (phrase) which is masked out span_lower- the lowercase version of span r...
amazon.sciencenatural language processing
Name Entity Recognition datasets containing short sentences and queries with low-context, including LOWNER, MSQ-NER, ORCAS-NER and Gazetteers (1.67 million entities). This release contains the multilingual versions of the datasets in Low Context Name Entity Recognition (NER) Datasets with Gazetteer.
amazon.sciencenatural language processingtext analysis
A collection of product reviews summaries automatically generated by PASS for 32 Amazon products from the FewSum dataset
amazon.sciencecomputer vision
PersonPath22 is a large-scale multi-person tracking dataset containing 236 videos captured mostly from static-mounted cameras, collected from sources where we were given the rights to redistribute the content and participants have given explicit consent. Each video has ground-truth annotations including both bounding boxes and tracklet-ids for all the persons in each frame.
amazon.sciencejsonnatural language processing
This dataset is part of the paper "McPhraSy: Multi-Context Phrase Similarity and Clustering" by DN Cohen et al (2022). The purpose of PCD is to evaluate the quality of semantic-based clustering of noun phrases. The phrases were collected from the [Amazon Review Dataset] (https://nijianmo.github.io/amazon/).
amazon.sciencemachine learningnatural language processing
This dataset provides product related questions, including their textual content and gap, in hours, between purchase and posting time. Each question is also associated with related product details, including its id and title.
amazon.sciencemachine learningnatural language processingonline shoppingproduct comparison
The Product Comparison dataset for online shopping is a new, manually annotated dataset with about 15K human generated sentences, which compare related products based on one or more of their attributes (the first such data we know of for product comparison). It covers ∼8K product sets, their selected attributes, and comparison texts.
amazon.sciencecommercenatural language processing
This dataset provides a set of non-shoppable items, which are items that can't be purchased via a virtual assistant (love, vampires, etc). In addition, for each non-shoppable item, the dataset contains humorous responses generated by two different large language models, and a template-based generation solution that uses a commonsense knowledge graph called ConceptNet. Finally, each row contains a score provided by human annotators that judge how funny each response is. The columns provided for each datapoint are as follows: question- purchase request of the non-shoppable item answer- a ge...
amazon.scienceanomaly detectionclassificationfewshotindustrialsegmentation
Largest Visual Anomaly detection dataset containing objects from 12 classes in 3 domains across 10,821(9,621 normal and 1,200 anomaly) images. Both image and pixel level annotations are provided.
amazon.scienceinformation retrievalmachine learningnatural language processing
Voice-based refinements of product search
amazon.sciencemachine learningnatural language processing
This dataset provides how-to articles from wikihow.com and their summaries, written as a coherent paragraph. The dataset itself is available at wikisum.zip, and contains the article, the summary, the wikihow url, and an official fold (train, val, or test). In addition, human evaluation results are available at wikisum-human-eval...
amazon.scienceconversation datadialogmachine learningnatural language processing
Wizard of Tasks (WoT) is a dataset containing conversations for Conversational Task Assistants (CTAs). A CTA is a conversational agent whose goal is to help humans to perform real-world tasks. A CTA can help in exploring available tasks, answering task-specific questions and guiding users through step-by-step instructions. WoT contains about 550 conversations with ~18,000 utterances in two domains, i.e., Cooking and Home Improvement.
amazon.sciencecomputer visiondeep learningmachine learning
Airborne Object Tracking (AOT) is a collection of 4,943 flight sequences of around 120 seconds each, collected at 10 Hz in diverse conditions. There are 5.9M+ images and 3.3M+ 2D annotations of airborne objects in the sequences. There are 3,306,350 frames without labels as they contain no airborne objects. For images with labels, there are on average 1.3 labels per image. All airborne objects in the dataset are labelled.
amazon.sciencecomputer visiondeep learninginformation retrievalmachine learningmachine translation
Amazon Berkeley Objects (ABO) is a collection of 147,702 product listings with multilingual metadata and 398,212 unique catalog images. 8,222 listings come with turntable photography (also referred as "spin" or "360º-View" images), as sequences of 24 or 72 images, for a total of 586,584 images in 8,209 unique sequences. For 7,953 products, the collection also provides high-quality 3d models, as glTF 2.0 files.
amazon.scienceHawkes Processmachine learningtemporal point process
When sellers need help from Amazon, such as how to create a listing, they often reach out to Amazon seller support through email, chat or phone. For each contact, we assign an intent so that we can manage the request more easily. The data we present in this release includes 548k contacts with 118 intents from 70k sellers sampled from recent years. There are 3 columns. 1. De-identified seller id - seller_id_anon; 2. Noisy inter-arrival time in the unit of hour between contacts - interarrival_time_hr_noisy; 3. An integer that represents the contact intent - contact_intent. Note that, to balance ...
amazon.sciencecomputer visionmachine learning
Fine-grained localized visual similarity and search for fashion.
amazon.sciencenatural language processing
N-grams are fixed size tuples of items. In this case the items are words extracted from the Google Books corpus. The n specifies the number of elements in the tuple, so a 5-gram contains five words or characters. The n-grams in this dataset were produced by passing a sliding window of the text of books and outputting a record for each new token.
amazon.sciencegraphtraffictransportation
Large-scale node-weighted conflict graphs for maximum weight independent set solvers
amazon.sciencejsonmetadata
The Registry of Open Data on AWS contains publicly available datasets that are available for access from AWS resources. Note that datasets in this registry are available via AWS resources, but they are not provided by AWS; these datasets are owned and maintained by a variety of government organizations, researchers, businesses, and individuals. This dataset contains derived forms of the data in https://github.com/awslabs/open-data-registry that have been transformed for ease of use with machine interfaces. Curren...