MY NASA DATA (MND) is a tool that allows anyone to make use of satellite data that was previously unavailable.Through the use of MND’s Live Access Server (LAS) a multitude of charts, plots and graphs can be generated using a wide variety of constraints. This site provides a large number of lesson plans with a wide variety of topics, all with the students in mind. Not only can you use our lesson plans, you can use the LAS to improve the ones that you are currently implementing in your classroom.

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

This time series dataset includes viral COVID-19 laboratory test [Polymerase chain reaction (PCR)] results from over 1,000 U.S. laboratories and testing locations including commercial and reference laboratories, public health laboratories, hospital laboratories, and other testing locations. Data are reported to state and jurisdictional health departments in accordance with applicable state or local law and in accordance with the Coronavirus Aid, Relief, and Economic Security (CARES) Act (CARES Act Section 18115).

Data are provisional and subject to change.

Data presented here is representative of diagnostic specimens being tested - not individual people - and excludes serology tests where possible. Data presented might not represent the most current counts for the most recent 3 days due to the time it takes to report testing information. The data may also not include results from all potential testing sites within the jurisdiction (e.g., non-laboratory or point of care test sites) and therefore reflect the majority, but not all, of COVID-19 testing being conducted in the United States.

Sources: CDC COVID-19 Electronic Laboratory Reporting (CELR), Commercial Laboratories, State Public Health Labs, In-House Hospital Labs

Data for each state is sourced from either data submitted directly by the state health department via COVID-19 electronic laboratory reporting (CELR), or a combination of commercial labs, public health labs, and in-house hospital labs. Data is taken from CELR for states that either submit line level data or submit aggregate counts which do not include serology tests.

The Ancillary Data component of the Indicators of Coastal Water Quality Collection includes a 5 arc-minute (approximately 9 x 9 km at the equator) sequence grid, grid cell centroids that relate to the grid cells in the tabular "Indicators of Coastal Water Quality: Change in Chlorophyll-a Concentration 1998-2007" data set, and a country buffer data set that is divided by exclusive economic zones (EEZ). The data are produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

Head Data Set 2

## Overview

Head Data Set 2 is a dataset for object detection tasks - it contains Heads QiDz annotations for 2,342 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.

The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

Models and external data of 3rd place efficiency solution for https://www.kaggle.com/competitions/pii-detection-removal-from-educational-data competition.

See https://www.kaggle.com/code/devinanzelmo/piidd-efficiency-3rd-process-external-data for links to external data and processing code

See https://www.kaggle.com/code/devinanzelmo/piidd-efficiency-3rd-train for training code that generated models.

See https://www.kaggle.com/code/devinanzelmo/piidd-efficiency-3rd-inference for inference code

This dataset globally (excluding frigid/polar zones) quantifies the different facets of variability in surface soil (0 – 30 cm) salinity and sodicity for the period between 1980 and 2018. This is realised by developing 4-D predictive models of Electrical Conductivity of saturated soil Extract (ECe) and soil Exchangeable Sodium Percentage (ESP) as indicators of soil salinity and sodicity. These machine learning-based models make predictions for ECe and ESP at different times, locations, and depths and by extracting meaningful statistics form those predictions, different facets of variability in the surface soil salinity and sodicity are quantified. The dataset includes 10 maps documenting different aspects of soil salinity and sodicity variations, and auxiliary data required for generation of those maps. Users are referred to the corresponding "READ_ME" file for more information about this dataset.

Link to the Open Data site for the United States Census Bureau.

Note: Find data at source; data is continuously updated・ PG&E provides non-confidential, aggregated usage data that are available to the public and updated on a quarterly basis. These public datasets consist of monthly consumption aggregated by ZIP code and by customer segment: Residential, Commercial, Industrial and Agricultural. The public datasets must meet the standards for aggregating and anonymizing customer data pursuant to CPUC Decision 14-05-016, as follows: a minimum of 100 Residential customers; a minimum of 15 Non-Residential customers, with no single Non-Residential customer in each sector accounting for more than 15% of the total consumption. If the aggregation standard is not met, the consumption will be combined with a neighboring ZIP code until the aggregation requirements are met.

The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following: -sensor recordings -sampling rate -units -parameter description -parameter ID

Use this guide to find information on Tempe data policy and standards.Open Data PolicyEthical Artificial Intelligence (AI) PolicyEvaluation PolicyExpedited Data Sharing PolicyData Sharing Agreement (General)Data Sharing Agreement (GIS)Data Quality Standard and ChecklistDisaggregated Data StandardsData and Analytics Service Standard

Description

In this dataset, there are soil data analyses with properties such as pH, organic matter (OM), salinity (EC), etc., major elements (N, P, K, Mg) as well as some microelements (Fe, Zn, Mn, Cu, B) with significant impact on plant nutrition.

Categories

Agricultural Soil

Acknowledgements & Source

Panagiotis Tziachris

Data Source

View Details

Image Source

This data set consists of Conductivity, Temperature, Depth (CTD) data in MATLAB Format from the 2002 Polar Star Mooring Cruise (AWS-02-I). These data are provided in a single mat-file (MATLAB) for the entire cruise.

This file contains raw data for cameras and wearables of the ConfLab dataset.

./cameras

contains the overhead video recordings for 9 cameras (cam2-10) in MP4 files.

These cameras cover the whole interaction floor, with camera 2 capturing the

bottom of the scene layout, and camera 10 capturing top of the scene layout.

Note that cam5 ran out of battery before the other cameras and thus the recordings

are cut short. However, cam4 and 6 contain significant overlap with cam 5, to

reconstruct any information needed.

Note that the annotations are made and provided in 2 minute segments.

The annotated portions of the video include the last 3min38sec of x2xxx.MP4

video files, and the first 12 min of x3xxx.MP4 files for cameras (2,4,6,8,10),

with "x" being the placeholder character in the mp4 file names. If one wishes

to separate the video into 2 min segments as we did, the "video-splitting.sh"

script is provided.

./camera-calibration contains the camera instrinsic files obtained from

https://github.com/idiap/multicamera-calibration. Camera extrinsic parameters can

be calculated using the existing intrinsic parameters and the instructions in the

multicamera-calibration repo. The coordinates in the image are provided by the

crosses marked on the floor, which are visible in the video recordings.

The crosses are 1m apart (=100cm).

./wearables

subdirectory includes the IMU, proximity and audio data from each

participant at the Conflab event (48 in total). In the directory numbered

by participant ID, the following data are included:

1. raw audio file

2. proximity (bluetooth) pings (RSSI) file (raw and csv) and a visualization

3. Tri-axial accelerometer data (raw and csv) and a visualization

4. Tri-axial gyroscope data (raw and csv) and a visualization

5. Tri-axial magnetometer data (raw and csv) and a visualization

6. Game rotation vector (raw and csv), recorded in quaternions.

All files are timestamped.

The sampling frequencies are:

- audio: 1250 Hz

- rest: around 50Hz. However, the sample rate is not fixed

and instead the timestamps should be used.

For rotation, the game rotation vector's output frequency is limited by the

actual sampling frequency of the magnetometer. For more information, please refer to

https://invensense.tdk.com/wp-content/uploads/2016/06/DS-000189-ICM-20948-v1.3.pdf

Audio files in this folder are in raw binary form. The following can be used to convert

them to WAV files (1250Hz):

ffmpeg -f s16le -ar 1250 -ac 1 -i /path/to/audio/file

Synchronization of cameras and werables data

Raw videos contain timecode information which matches the timestamps of the data in

the "wearables" folder. The starting timecode of a video can be read as:

ffprobe -hide_banner -show_streams -i /path/to/video

./audio

./sync: contains wav files per each subject

./sync_files: auxiliary csv files used to sync the audio. Can be used to improve the synchronization.

The code used for syncing the audio can be found here:

https://github.com/TUDelft-SPC-Lab/conflab/tree/master/preprocessing/audio

Time series state level datasets showing important indicators regarding mental health. Includes data on mental health readmissions within 28 days, Mental health Community Care with within seven days …Show full descriptionTime series state level datasets showing important indicators regarding mental health. Includes data on mental health readmissions within 28 days, Mental health Community Care with within seven days of discharge and mental health average length of stay (days).

In the field of manufacturing, high-quality datasets are essential for optimizing production processes, improving energy efficiency, and developing predictive maintenance strategies. This repository introduces a comprehensive CNC machining data repository that includes three key data categories: (1) product geometry data, (2) NC code data, and (3) high frequency energy consumption data. This dataset is particularly valuable for researchers and engineers working in manufacturing analytics, energy-efficient machining, and machine learning applications in smart manufacturing. Potential use cases include optimizing machining parameters for energy reduction, predicting tool wear based on power consumption patterns, and enhancing digital twin models with real-world machining data. By making this dataset publicly available, we aim to support the development of data-driven solutions in modern manufacturing and facilitate benchmarking efforts across different machining strategies.

This data release contains California Department of Water Resource borehole data that were regularized by the US Geological Survey. This dataset contains borehole lithologic data, and geospatial data of water wells in the Hat Creek basin California, located east of Mount Shasta in southern California. The borehole dataset is released as an excel table and includes (1) individual borehole location, and (2) downhole lithologic interval data derived from well drillers’ lithology logs.

The dataset contains combined counts for hospital discharges, emergency room encounters, and ambulatory surgeries by preferred language spoken at each facility. The nearly 100 languages collected in the patient-level data were combined into eight geographical or cultural groups: English Language, Spanish Language, Asian/Pacific Islander Languages, Middle Eastern Languages, European Languages, African Languages, Latin American Languages, Native American Languages, and Sign Language. See the Preferred Language Spoken Language List below to see the exact separation of languages.

Dataset Card for deepseek-r1-gen-data

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://hf-mirror.com/datasets/Aristo2333/deepseek-r1-gen-data/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/Aristo2333/deepseek-r1-gen-data.