It is presented two datasets used to train a neural network that forecasts electricity prices in the Yucatan peninsula. The first one is the Input data, which is composed of five parameters, three describing environmental conditions and two reporting the levels of operation of the electricity system in the study region. The second is the output data, corresponding to local marginal electricity prices. These prices are compound from the next three costs: energy, losses of transmission, and congestion.

Also, these data allow detecting the dynamics of the electricity market, which can be related to environmental conditions. Also, they allow detecting phenomena of the electricity market, i.e. negative prices of transmission losses or congestion, and the negative merit-order effect.

Every parameter was collected for eight load zones in hourly resolution, it is the geographic distribution according to the Mexican independent system operator. The data begins in the first hour of January 1st of 2017 and ends in the last hour of April 4th of 2019. Each parameter has 157808 observations.

This dataset presents a unified, cross-continental time-series day-ahead electricity prices compiled from major wholesale markets across Asia, Europe, North America, South America, and Oceania. The dataset offers a standardized format that supports time-series forecasting and enables robust comparative analysis across diverse global electricity markets.

上证指数的五十家公司股票情况

India produces 1.5 million engineers every year. A relevant question is what determines the salary and the jobs these engineers are offered right after graduation. Various factors such as college grades, candidate skills, proximity of the college to industrial hubs, the specialization one is in, market conditions for specific industries determine this.

The dataset contains various information about a set of engineering candidates and their employment outcomes. For every candidate, the data contains both the profile information along with their employment outcome information. Candidate Profile Information includes:

• Scores on Aspiring Minds’ AMCAT – a standardized test of job skills. The test includes cognitive, domain and personality assessments
• Personal information like gender, date of birth, etc.
• Pre-university information like high school grades, high school location
• University information like GPA, college major, college reputation proxy.
• Demographic information like location of college, candidates’ permanent location

Employment Outcome Information includes:

• First job annual salary
• First job title
• First job location

This is the only data set where we have employment outcomes together with scores on a standardized job test, which makes this very unique. Other such data sets either do not test scores at all or scores on pre-university tests

Data Collection

A million undergraduates take AMCAT every year as a way to get job credentials and feedback to improve themselves. Candidates are tested on the following skills –

• English Language, Logical Ability and Quantitative Ability – these are IRT based adaptive modules. More information here on what IRT based adaptive tests are.
• Personality – Big Five based personality instrument. More information here.
• Skill tests – Chosen by candidates based on their interest

These assessments are validated against on-job performance and show a validity between 0.3-0.55 (Learn more about test validity here - http://www.centerforpubliceducation.org/Main-Menu/Evaluating-performance/A-guide-to-standardized-testing-The-nature-of-assessment). These scores are used by 2000+ companies.

Random AMCAT takers were surveyed via email wherein they provided information on the dependent variables in this dataset – the jobs they are in and their corresponding annual salaries. Corresponding independent information about the candidates was recorded at the time of them taking AMCAT.

Yearly citation counts for the publication titled "The long march to primary health care in China from collectivism to market economics".

This Roundup covers critical economic data and publications used by NSW Treasury for monitoring and analysis.

This Roundup covers critical economic data and publications used by NSW Treasury for monitoring and analysis.

Note: This resource was originally published on opengov.nsw.gov.au. The OpenGov website has been retired. If you have any questions, please contact the Agency Services team at transfer@mhnsw.au

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Short description of the contents of the publication.\tThis Roundup covers critical economic data and publications used by NSW Treasury for monitoring and analysis.

This Roundup covers critical economic data and publications used by NSW Treasury for monitoring and analysis.