dur à dire

Narrative language samples elicited using the ALPS Oral Narrative Retell and Oral Narrative Generation tasks from diverse K-3 students. The test data set was drawn randomly from the larger corpus of narrative language samples.

systemk/test-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Super brief overview. This is a test dataset.. Visit https://dataone.org/datasets/urn%3Auuid%3A7bb531f1-d374-45b7-9077-bf0c5985e37f for complete metadata about this dataset.

jazza234234/test-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

The purpose of the data environment is to provide multi-modal data and contextual information (weather and incidents) that can be used to research and develop Intelligent Transportation System applications. This data set contains the following data for the two months of September and October 2011 in Pasadena, California: Highway network data, Demand data, Sample mobile sightings provided for a two-hour period, provided by AirSage (see note 1 below), Network performance data (measured and forecast), Work zone data, Weather data, and Changeable message sign data. This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov.

This is just a test dataset to check this CKAN instance.

Table 2 Description

The table Test Table 2 is part of the dataset Adam Test Dataset, available at https://redivis.com/datasets/bw62-c39y5pq3e. It contains 1129 rows across 33 variables.

Closeup Test

## Overview

Closeup Test is a dataset for classification tasks - it contains Close Uptest annotations for 251 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Water Test

## Overview

Water Test is a dataset for object detection tasks - it contains Numbers annotations for 4,802 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

The CERT Division, in partnership with ExactData, LLC, and under sponsorship from DARPA I2O, generated a collection of synthetic insider threat test datasets. These datasets provide both synthetic background data and data from synthetic malicious actors. Datasets are organized according to the data generator release that created them. Most releases include multiple datasets (e.g., r3.1 and r3.2). Generally, later releases include a superset of the data generation functionality of earlier releases. Each dataset file contains a readme file that provides detailed notes about the features of that release. The answer key file answers.tar.bz2 contains the details of the malicious activity included in each dataset, including descriptions of the scenarios enacted and the identifiers of the synthetic users involved.

Bottles Test

## Overview

Bottles Test is a dataset for object detection tasks - it contains Bottles annotations for 704 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Oregon.gov News room

Connectomix test dataset 2

This dataset is designed to test features of Connectomix. For more information, please visit the GitHub repository:

https://github.com/ln2t/connectomix

The dataset contains data for 4 participants.

Rawdata

• Anatomical: T1w image (defaced)
• Functional: A standard 6 minutes resting-state scan
• Groups: the participants are split in control and patient groups in the participants.tsv file. This split has been done artificially and serves only testing purposes.

Derivatives

• fmriprep: preprocessed data using fMRIPrep
• connectomix: results of the connectomix software (see below for details)

Commands

The exact commands to run the analyzes depends on your installation of fMRIPrep. In what follows, we simply assume that fmriprep is the command for fMRIPrep. We show here the simplest version of the commands, assuming you adapt those depending on your setup (e.g. if you use Docker).

We also assume that the data are at the following locations: bash bids_dir='/data/ds005699' derivatives_dir='/data/ds005699/derivatives'

Preprocessing (fMRIPrep)

fmriprep $bids_dir ${derivatives_dir}/fmriprep participant --fs-license-file /path/to/fs/license

Analysis: connectomix

Note: The following has been tested for connectomix version 1.0.1.

First set-up path to connectomix script: bash connectomix_cmd='/path/to/connectomix/connectomix/connectomix.py'

Second, set-up paths to config directory: bash config_dir='/data/ds005625/code/connectomix/config'

Participant-level

$connectomix_cmd ${bids_dir} ${derivatives_dir}/connectomix participant --derivatives fmriprep="${derivatives_dir}/fmriprep" --config "${config_dir}/participant_level_config.yaml"

Group-level

Notes: - this is an example of Independent two-samples t-test - Since the dataset contains only four subjects (two subjects per group), the number of possible permutations is very low. For this reason, the number of computed permutations is set to 4, and connectomix can then complete the group level-analysis. Of course, realistic cases should not only include much more participants, but also a much larger number of permutations (see connectomix documentation).

$connectomix_cmd ${bids_dir} ${derivatives_dir}/connectomix group --config "${config_dir}/group_level_config.yaml"

DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 test results by date of specimen collection, including total, positive, negative, and indeterminate for molecular and antigen tests. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests. Test results may be reported several days after the result. Data are incomplete for the most recent days. Data from previous dates are routinely updated. Records with a null date field summarize tests reported that were missing the date of collection. Starting in July 2020, this dataset will be updated every weekday.

Dataset

This dataset was created by surf4fame

Released under Apache 2.0

Contents

testing an audio dataset

Wider Test

## Overview

Wider Test is a dataset for object detection tasks - it contains Face annotations for 3,226 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Brian Kaka

Contents

A test to take a sign-language data set to speech with p5 lib