MRC Psycholinguistic Database

This is the complete MRC psycholinguistic database as found on https://websites.psychology.uwa.edu.au/school/mrcdatabase/uwa_mrc.htm.

  Usage

This dataset is ideal for training and evaluating machine learning models for English word concreteness.

  Acknowledgments

We extend our heartfelt gratitude to all the authors of the original dataset.

  License

This dataset is made available under the MIT license.

A machine usable dictionary containing thousands of words, each with linguistic and psycholinguistic attributes (psychological measures are recorded for a small percentage of words). The dictionary may be of use to researchers in psychology or linguistics to develop sets of experimental stimuli, or those in artificial intelligence and computer science who require psychological and linguistic descriptions of words.

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Database containing papers sourced from the XML result of a PubMed search for those entries where the grant number includes a reference to the Wellcome Trust. The db contains two tables; one contains the set of papers, including unique IDs, the second is the set of journals that the papers appear in. Article Table was Created via: CREATE TABLE articles (id INTEGER PRIMARY KEY,created_on DATE DEFAULT CURRENT_DATE,created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,article_title TEXT,pubyear INTEGER,pmid INTEGER,doi TEXT,journal INTEGER) and the Journal Table via: CREATE TABLE journals (id INTEGER PRIMARY KEY,created_on DATE DEFAULT CURRENT_DATE,created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,journal_title TEXT, issn TEXT,nlm_unique_id INTEGER)

This is a full description of the quality control procedure undertaken and the derived files produced by the MRC-IEU associated with the full UK Biobank (version 3, March 2018) genetic data. This dataset supersedes the earlier version available at DOI: 10.5523/bris.3074krb6t2frj29yh2b03x3wxj Complete download (zip, 176.9 KiB)

This deposit provides full details of the genome wide association study (GWAS) pipeline developed by the MRC-IEU for the full UK Biobank (version 3, March 2018) genetic data. For any issues with use of this documentation please contact: mrc-ieu@bristol.ac.uk. This dataset supersedes the earlier version at https://doi.org/10.5523/bris.2fahpksont1zi26xosyamqo8rr

MRC Global stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

MRC Global (MRC) Stock: A Pipeline to Profit?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Reconstruction maps of cryo-electron microscopy (cryo-EM) exhibit distortion when the cryo-EM dataset is incomplete, usually caused by unevenly distributed orientations. Prior efforts had been attempted to address this preferred orientation problem using tilt-collection strategy, modifications to grids or to air-water-interfaces. However, these approaches often require time-consuming experiments and the effect was always protein dependent. Here, we developed a procedure containing removing mis-aligned particles and an iterative reconstruction method based on signal-to-noise ratio of Fourier component to correct such distortion by recovering missing data using a purely computational algorithm. This procedure called Signal-to-Noise Ratio Iterative Reconstruction Method (SIRM) was applied on incomplete datasets of various proteins to fix distortion in cryo-EM maps and to a more isotropic resolution. In addition, SIRM provides a better reference map for further reconstruction refinements, resulting in an improved alignment, which ultimately improves map quality and benefits model building.

MRC Global reported $571M in Debt for its fiscal quarter ending in December of 2024. Data for MRC Global | MRC - Debt including historical, tables and charts were last updated by Trading Economics this last July in 2025.

MRC Global reported $452M in Cost of Sales for its fiscal quarter ending in December of 2024. Data for MRC Global | MRC - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Subthalamic local field potential recordings from awake patients with Parkinson’s disease while leads were externalised. In 30 hemispheres, this data was recorded ON and OFF dopaminergic medication and in 26 hemispheres before and during 130 Hz deep brain stimulation of the subthalamic nucleus.DBS dataThis file contains data from 26 hemispheres. The sampling rates of the respective files can be found in MATRIX_DBS.fs. MATRIX_DBS.signal_base contains subthalamic local field potential data at rest. MATRIX_DBS.signal_dbs contains subthalamic local field potential data during 130 Hz deep brain stimulation of the subthalamic nucleus as described in the corresponding article.Medication dataThis dataset was recorded for 30 hemispheres ON and OFF levodopa. Each file has more information on how the data is arranged: SmrData.WvTits. Channel titles denote EEGs (e.g. Fz, Cz, F3), bipolar LFP channels (e.g. Le02, R02, L13) or accelerometer data (e.g. AcX, AcY).

The Centre for Longitudinal Studies (CLS) and the MRC Unit for Lifelong Health and Ageing (LHA) have carried out two online surveys of the participants of five national longitudinal cohort studies which have collected insights into the lives of study participants including their physical and mental health and wellbeing, family and relationships, education, work, and finances during the coronavirus pandemic. The Wave 1 Survey was carried out at the height of lockdown restrictions in May 2020 and focussed mainly on how participants’ lives had changed from just before the outbreak of the pandemic in March 2020 until then. The Wave 2 survey was conducted in September/October 2020 and focussed on the period between the easing of restrictions in June through the summer into the autumn. A third wave of the survey was conducted in early 2021.

In addition, CLS study members who had participated in any of the three COVID-19 Surveys were invited to provide a finger-prick blood sample to be analysed for COVID-19 antibodies. Those who agreed were sent a blood sample collection kit and were asked to post back the sample to a laboratory for analysis. The antibody test results and initial short survey responses are included in a single dataset, the COVID-19 Antibody Testing in the National Child Development Study, 1970 British Cohort Study, Next Steps and Millennium Cohort Study, 2021 (SN 8823).

The CLS studies are:

• Millennium Cohort Study (born 2000-02) both cohort members and parents (MCS)
• Next Steps (born 1989-90) (NS)
• 1970 British Cohort Study (BCS70)
• 1958 National Child Development Study (NCDS).

The LHA study is:

• MRC National Survey of Health and Development, 1946 British birth cohort (NSHD)

The content of the MCS, NS, BCS70 and NCDS COVID-19 studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The COVID-19 Survey in Five National Longitudinal Cohort Studies: MRC National Survey of Health and Development, 2020: Special Licence Access contains the data from Waves 1-3 for the 1946 birth cohort study.

The Wave 1 Survey was programmed and administered by CLS/LHA using Qualtrics. The Wave 2 and Wave 3 Survey was programmed and administered by Kantar Public.

Further information may be found on the https://cls.ucl.ac.uk/covid-19-survey/"> CLS COVID-19 survey website.

Latest edition information

For the third edition (June 2021), the Wave 3 data have been added to the study, and the Wave 2 data file replaced with a new version. The documentation has also been updated.

Muscle MRC activity data from the study "Mitochondrial complex I deficiency occurs in skeletal muscle of a subgroup of individuals with Parkinson’s disease".

Realtime Earth Satellite object tracking and orbit data for MRC-100. NORAD Identifier: 56993.

UAB FMĮ "MRC Markets" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

This file includes all archived data from the MRC funded Action 330 project which has MRC REF number MR/J000191/1

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

MRC MRC Global Inc. Common Stock

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data