Dataset

This dataset was created by James Nakano

Contents

The location, name, and size of existing fitness stations in the City of Casey. This data was captured for Recreation Planning Assessments in 2019, extracted from the City of Casey's Asset Management System and GIS databases.

Global trade data of Fitness accessories under 95069920, 95069920 global trade data, trade data of Fitness accessories from 80+ Countries.

Social institutions under the responsibility of the MSAGD Rhineland-Palatinate:Education and further education in the health professions

NOTE: This dataset is not maintained. This note was added on the 4th of October 2024 and any resources with dead links removed. For API guidance from the Government Chief Digital Officer (GCDO) please check https://www.digital.govt.nz/standards-and-guidance/technology-and-architecture/application-programming-interfaces-apis and email GCDO@dia.govt.nz for more information. This is a convenient place for the Service Innovation team to keep track of APIs we discover across the New Zealand Government that we find useful for our work (which involves collaborating with agencies to design and deliver new services and reusable components.) We don’t run or have responsibility for these APIs. We just want to make them easier to find. If you know more APIs please contact us. This is a short-term exercise and we plan to use what we learn (from collecting and sharing information about APIs and reusable components) to inform future approaches to making it easier for people to discover and use APIs and platforms. Where possible we have linked to the landing page for the API and not directly to the end point API itself – this is so you can get the latest context and information about the API before you use it.

Introduction

Training.gov.au (TGA) is the National Register of Vocational Education and Training in Australia and contains authoritative information about Registered Training Organisations (RTOs), Nationally Recognised Training (NRT) and the approved scope of each RTO to deliver NRT as required in national and jurisdictional legislation.

TGA web-services overview

TGA has a web service available to allow external systems to access and utilise information stored in TGA through an external system. The TGA web service is exposed through a single interface and web service users are assigned a data reader role which will apply to all data stored in the TGA.

The web service can be broadly split into three categories:

1. RTOs and other organisation types;

2. Training components including Accredited courses, Accredited course Modules Training Packages, Qualifications, Skill Sets and Units of Competency;

3. System metadata including static data and statistical classifications.

Users will gain access to the TGA web service by first passing a user name and password through to the web server. The web server will then authenticate the user against the TGA security provider before passing the request to the application that supplies the web services.

There are two web services environments:

1. Production - ws.training.gov.au – National Register production web services

2. Sandbox - ws.sandbox.training.gov.au – National Register sandbox web services.

The National Register sandbox web service is used to test against the current version of the web services where the functionality will be identical to the current production release. The web service definition and schema of the National Register sandbox database will also be identical to that of production release at any given point in time. The National Register sandbox database will be cleared down at regular intervals and realigned with the National Register production environment.

Each environment has three configured services:

1. Organisation Service;

2. Training Component Service; and

3. Classification Service.

Sandbox environment access

To access the download area for web services, navigate to http://tga.hsd.com.au and use the below name and password:

Username: WebService.Read (case sensitive)

Password: Asdf098 (case sensitive)

This download area contains various versions of the following artefacts that you may find useful

• Training.gov.au web service specification document;

• Training.gov.au logical data model and definitions document;

• .NET web service SDK sample app (with source code);

• Java sample client (with source code);

• How to setup web service client in VS 2010 video; and

• Web services WSDL's and XSD's.

For the business areas, the specification/definition documents and the sample application is a good place to start while the IT areas will find the sample source code and the video useful to start developing against the TGA web services.

The web services Sandbox end point is: https://ws.sandbox.training.gov.au/Deewr.Tga.Webservices

Production web service access

Once you are ready to access the production web service, please email the TGA team at tgaproject@education.gov.au to obtain a unique user name and password.

Point geometry with attributes displaying fitness centers in East Baton Rouge Parish, Louisiana.Metadata

Content

The New York State Office of Parks, Recreation and Historic Preservation (OPRHP) oversees more than 214 state parks and historic sites, encompassing nearly 335,000 acres, that are visited by 60 million people annually. These facilities contribute to the economic vitality and quality of life of local communities and directly support New York’s tourism industry. Parks also provide a place for families and children to be active and exercise, promoting healthy lifestyles. The agency is responsible for the operation and stewardship of the state park system as well as advancing a statewide parks, historic preservation, and open space mission.From the famed Bethpage Black, to the rolling terrain of the Robert Trent Jones' designed 18-hole course at Green Lakes State Park, New York's state park golf courses rank among the best public courses in the world. For more information, visit http://nysparks.com/golf-courses/

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated quarterly.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Ben Hershey on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

EXERCISE EXERCISE EXERCISE CEDR Digital Corps is providing this "sheet of bleats" in the hope that it will prove useful to both our fellow organizations in the FEMA Crowdsourcing community and to federal, state and local agencies participating in the Shaken Fury exercise. While at first glance having such a spreadsheet may feel like 'cheating', it's actually very consistent with how CEDR addresses real-world incidents, where we have both our team of volunteers and automated search routines gathering potentially relevant or informative social media posts which are then brought into our teams' working environments to be reviewed, validated, "data-mined" and ultimately mapped or otherwise published. Being able to see all the bleats in such a format opens the door to many possibilities for data analysis on the bleat dataset (examining, for example, keyword frequency or other attributes of bleats' text), and, we believe, simply makes it easier for both volunteer organizations and agencies to review the latest bleats and (potentially) engage with them and provide appropriate responses on SimulationDeck. This Google Sheet is updated via a (manual) routine currently being executed two or three times each day, so while it will never be quite 'real-time', CEDR is endeavouring to keep it reasonably current for the remainder of the Shaken Fury exercise. Feedback / suggestions / thoughts are welcome and can be shared on FEMA Slack or directly to rob.neppell@cedrdigitalcorps.org. EXERCISE EXERCISE EXERCISE CEDR Digital Corps. is a 501c3 non-profit which coordinates the work of volunteers to create innovative technical solutions to address information gaps for the public, first responders, and emergency managers during disaster response. We leverage technology, social media, and a rapidly scalable volunteer brigade to quickly gather and vet critical information about conditions on the ground and communicate that information to those who need in most via our social media channels and the publication of datasets and mapping / GIS products. For more information visit:https://cedrdigitalcorps.orghttps://twitter.com/cedrdigital

Table of INEBase Currently carrying out studies or training courses by gender, age and disability group. Population aged 16 and over with a disability. National. Disability, Independence and Dependency Situations Survey

Table of INEBase Number of days per week of physical exercise during leisure time by sex, country of birth, and age group. Population aged 15 years old and over. National. European Health Survey

Social institutions under the responsibility of the MSAGD Rhineland-Palatinate:

Analysis of ‘Percentage of population pursuing academic education or training in the last four weeks by sex. EPA (API identifier: 45606)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-341-45606 on 07 January 2022.

--- Dataset description provided by original source is as follows ---

Table of INEBase Percentage of population pursuing academic education or training in the last four weeks by sex. Annual. National. Economically Active Population Survey

--- Original source retains full ownership of the source dataset ---

LifeSnaps Dataset Documentation

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.

Data Import: Reading CSV

For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command.

Data Import: Setting up a MongoDB (Recommended)

To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.

To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here.

For the Fitbit data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c fitbit 

For the SEMA data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c sema 

For surveys data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c surveys 

If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.

Data Availability

The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:

{
  _id: 

Locations of offices providing job training in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visithttp://egis3.lacounty.gov/lms/.

Content

This dataset is a listing of congregate and home delivered meals served 1974 to the present by the network of Area Agencies on Aging (AAAs). AAAs - local offices for the Aging - provide services at senior center locations either directly or through subcontracts. Services may include but are not limited to congregate meals, health promotion, educational programs, recreation, etc.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Lily Banse on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Overview

This is a dataset of Chess board photos and various pieces. All photos were captured from a constant angle, a tripod to the left of the board. The bounding boxes of all pieces are annotated as follows: white-king, white-queen, white-bishop, white-knight, white-rook, white-pawn, black-king, black-queen, black-bishop, black-knight, black-rook, black-pawn. There are 2894 labels across 292 images.

https://i.imgur.com/nkjobw1.png" alt="Chess Example">

Follow this tutorial to see an example of training an object detection model using this dataset or jump straight to the Colab notebook.

Use Cases

At Roboflow, we built a chess piece object detection model using this dataset.

https://blog.roboflow.ai/content/images/2020/01/chess-detection-longer.gif" alt="ChessBoss">

You can see a video demo of that here. (We did struggle with pieces that were occluded, i.e. the state of the board at the very beginning of a game has many pieces obscured - let us know how your results fare!)

Using this Dataset

We're releasing the data free on a public license.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility.

Table of INEBase Early dropout of education and training of persons aged 18 to 24. Spain and EU-28. Annual. National. Women and Men in Spain

Complete list of all 4293 Planet Fitness POI locations in the USA with name, geo-coded address, city, email, phone number etc for download in CSV format or via the API.

Table of INEBase Average time per week (minutes) of physical exercise during leisure time by sex, country of birth, and age group. Average and standard deviation. Population aged 15 years old and over that practises Physical exercise at least once a week during his/her leisure time. National. European Health Survey