This data-set includes information about a sample of 8,887 of Open Educational Resources (OERs) from SkillsCommons website. It contains title, description, URL, type, availability date, issued date, subjects, and the availability of following metadata: level, time_required to finish, and accessibility.

This data-set has been used to build a metadata scoring and quality prediction model for OERs.

Initial data source was UNESCO web site, supplemented by individual work on different countires/regions;A database of cultural heritage sites assembled by volunteers at the Archaeological Computing Laboratory, University of Sydney

This dataset is comprised of a collection of example DMPs from a wide array of fields; obtained from a number of different sources outlined in the README. Data included/extracted from the examples included the discipline and field of study, author, institutional affiliation and funding information, location, date modified, title, research and data-type, description of project, link to the DMP, and where possible external links to related publications, grant pages, or French language versions. This CSV document serves as the content for a McMaster Data Management Plan (DMP) Database as part of the Research Data Management (RDM) Services website, located at https://u.mcmaster.ca/dmps. Other universities and organizations are encouraged to link to the DMP Database or use this dataset as the content for their own DMP Database. This dataset will be updated regularly to include new additions and will be versioned as such. We are gathering submissions at https://u.mcmaster.ca/submit-a-dmp to continue to expand the collection.

This is the description of a dataset. The description can be quite long and this can look strange in the public dataset page. In the drafts page there is a scrollbar in the scrollbar, why not in the public page? Well, the public page needs to support viewing on a mobile phone and this can make scroll bars within scrollbars within scrollbars a little difficult. So maybe it’ll be better to try using ellipses. Additionally only adding a description does not make it a new version. .. This is the description of a dataset. The description can be quite long and this can look strange in the public dataset page. In the drafts page there is a scrollbar in the scrollbar, why not in the public page? Well, the public page needs to support viewing on a mobile phone and this can make scroll bars within scrollbars within scrollbars a little difficult. So maybe it’ll be better to try using ellipses. Additionally only adding a description does not make it a new version.

This is the description of a dataset. The description can be quite long and this can look strange in the public dataset page. In the drafts page there is a scrollbar in the scrollbar, why not in the public page? Well, the public page needs to support viewing on a mobile phone and this can make scroll bars within scrollbars within scrollbars a little difficult. So maybe it’ll be better to try using ellipses. Additionally only adding a description does not make it a new version. This is the description of a dataset. The description can be quite long and this can look strange in the public dataset page. In the drafts page there is a scrollbar in the scrollbar, why not in the public page? Well, the public page needs to support viewing on a mobile phone and this can make scroll bars within scrollbars within scrollbars a little difficult. So maybe it’ll be better to try using ellipses. Additionally only adding a description does not make it a new version.

Contains public sector information licensed under the Open Government Licence v3.0 An example of data that can be downloaded from WOW, used to demonstrate data surrounding the Met Office in a data tooling and analysis tutorial. File names are adapted from the source by giving the WOW site id and the year of the downloaded data.

SQLite Sakila Sample Database

Database Description

The Sakila sample database is a fictitious database designed to represent a DVD rental store. The tables of the database include film, film_category, actor, customer, rental, payment and inventory among others. The Sakila sample database is intended to provide a standard schema that can be used for examples in books, tutorials, articles, samples, and so forth. Detailed information about the database can be found on the MySQL website: https://dev.mysql.com/doc/sakila/en/

Sakila for SQLite is a part of the sakila-sample-database-ports project intended to provide ported versions of the original MySQL database for other database systems, including:

• Oracle
• SQL Server
• SQLIte
• Interbase/Firebird
• Microsoft Access

Sakila for SQLite is a port of the Sakila example database available for MySQL, which was originally developed by Mike Hillyer of the MySQL AB documentation team. This project is designed to help database administrators to decide which database to use for development of new products The user can run the same SQL against different kind of databases and compare the performance

License: BSD Copyright DB Software Laboratory http://www.etl-tools.com

Note: Part of the insert scripts were generated by Advanced ETL Processor http://www.etl-tools.com/etl-tools/advanced-etl-processor-enterprise/overview.html

Information about the project and the downloadable files can be found at: https://code.google.com/archive/p/sakila-sample-database-ports/

Other versions and developments of the project can be found at: https://github.com/ivanceras/sakila/tree/master/sqlite-sakila-db

https://github.com/jOOQ/jOOQ/tree/main/jOOQ-examples/Sakila

Direct access to the MySQL Sakila database, which does not require installation of MySQL (queries can be typed directly in the browser), is provided on the phpMyAdmin demo version website: https://demo.phpmyadmin.net/master-config/

Files Description

The files in the sqlite-sakila-db folder are the script files which can be used to generate the SQLite version of the database. For convenience, the script files have already been run in cmd to generate the sqlite-sakila.db file, as follows:

sqlite> .open sqlite-sakila.db # creates the .db file sqlite> .read sqlite-sakila-schema.sql # creates the database schema sqlite> .read sqlite-sakila-insert-data.sql # inserts the data

Therefore, the sqlite-sakila.db file can be directly loaded into SQLite3 and queries can be directly executed. You can refer to my notebook for an overview of the database and a demonstration of SQL queries. Note: Data about the film_text table is not provided in the script files, thus the film_text table is empty. Instead the film_id, title and description fields are included in the film table. Moreover, the Sakila Sample Database has many versions, so an Entity Relationship Diagram (ERD) is provided to describe this specific version. You are advised to refer to the ERD to familiarise yourself with the structure of the database.

This is the description of a dataset. The description can be quite long and this can look strange in the public dataset page. In the drafts page there is a scrollbar in the scrollbar, why not in the public page? Well, the public page needs to support viewing on a mobile phone and this can make scroll bars within scrollbars within scrollbars a little difficult. So maybe it’ll be better to try using ellipses. Additionally only adding a description does not make it a new version.

The Ultimate Arabic News Dataset is a collection of single-label modern Arabic texts that are used in news websites and press articles.

Arabic news data was collected by web scraping techniques from many famous news sites such as Al-Arabiya, Al-Youm Al-Sabea (Youm7), the news published on the Google search engine and other various sources.

• The data we collect consists of two Primary files:

UltimateArabic: A file containing more than 193,000 original Arabic news texts, without pre-processing. The texts contain words, numbers, and symbols that can be removed using pre-processing to increase accuracy when using the dataset in various Arabic natural language processing tasks such as text classification.

UltimateArabicPrePros: It is a file that contains the data mentioned in the first file, but after pre-processing, where the number of data became about 188,000 text documents, where stop words, non-Arabic words, symbols and numbers have been removed so that this file is ready for use directly in the various Arabic natural language processing tasks. Like text classification.

• We have added two folders containing additional detailed datasets:

1- Sample: This folder contains samples of the results of web-scraping techniques for two popular Arab websites in two different news categories, Sports and Politics. this folder contain two datasets:

Sample_Youm7_Politic: An example of news in the "Politic" category collected from the Youm7 website. Sample_alarabiya_Sport: An example of news in the "Sport" category collected from the Al-Arabiya website.

2- Dataset Versions: This volume contains four different versions of the original data set, from which the appropriate version can be selected for use in text classification techniques. The first data set (Original) contains the raw data without pre-processing the data in any way, so the number of tokens in the first data set is very high. In the second data set (Original_without_Stop) the data was cleaned, such as removing symbols, numbers, and non-Arabic words, as well as stop words, so the number of symbols is greatly reduced. In the third dataset (Original_with_Stem) the data was cleaned, and text stemming technique was used to remove all additions and suffixes that might affect the accuracy of the results and to obtain the words roots. In the 4th edition of the dataset (Original_Without_Stop_Stem) all preprocessing techniques such as data cleaning, stop word removal and text stemming technique were applied, so we note that the number of tokens in the 4th edition is the lowest among all releases.

• The data is divided into 10 different categories: Culture, Diverse, Economy, Sport, Politic, Art, Society, Technology, Medical and Religion.

The Geoscience Australia Geological Observations and Samples database contains locations, geological observations, and descriptions of physical samples (specimens) from field sites, measured …Show full descriptionThe Geoscience Australia Geological Observations and Samples database contains locations, geological observations, and descriptions of physical samples (specimens) from field sites, measured sections and boreholes associated with Geoscience Australia geological mapping surveys in Australia, its surrounds, and Antarctica since the 1960's. Descriptions include information on lithology, stratigraphy, alteration, structural measurements, and many other geological attributes.

This resource contains Jupyter Notebooks with examples for conducting quality control post processing for in situ aquatic sensor data. The code uses the Python pyhydroqc package. The resource is part of set of materials for hydroinformatics and water data science instruction. Complete learning module materials are found in HydroLearn: Jones, A.S., Horsburgh, J.S., Bastidas Pacheco, C.J. (2022). Hydroinformatics and Water Data Science. HydroLearn. https://edx.hydrolearn.org/courses/course-v1:USU+CEE6110+2022/about.

This resources consists of 3 example notebooks and associated data files.

Notebooks: 1. Example 1: Import and plot data 2. Example 2: Perform rules-based quality control 3. Example 3: Perform model-based quality control (ARIMA)

Data files: Data files are available for 6 aquatic sites in the Logan River Observatory. Each file contains data for one site for a single year. Each file corresponds to a single year of data. The files are named according to monitoring site (FranklinBasin, TonyGrove, WaterLab, MainStreet, Mendon, BlackSmithFork) and year. The files were sourced by querying the Logan River Observatory relational database, and equivalent data could be obtained from the LRO website or on HydroShare. Additional information on sites, variables, and methods can be found on the LRO website (http://lrodata.usu.edu/tsa/) or HydroShare (https://www.hydroshare.org/search/?q=logan%20river%20observatory). Each file has the same structure indexed with a datetime column (mountain standard time) with three columns corresponding to each variable. Variable abbreviations and units are: - temp: water temperature, degrees C - cond: specific conductance, μS/cm - ph: pH, standard units - do: dissolved oxygen, mg/L - turb: turbidity, NTU - stage: stage height, cm

For each variable, there are 3 columns: - Raw data value measured by the sensor (column header is the variable abbreviation). - Technician quality controlled (corrected) value (column header is the variable abbreviation appended with '_cor'). - Technician labels/qualifiers (column header is the variable abbreviation appended with '_qual').

The Geoscience Australia Geological Observations and Samples database contains locations, geological observations, and descriptions of physical samples (specimens) from field sites, measured sections and boreholes associated with Geoscience Australia geological mapping surveys in Australia, its surrounds, and Antarctica since the 1960's. Descriptions include information on lithology, stratigraphy, alteration, structural measurements, and many other geological attributes.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

Description

The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) contains 7356 files (total size: 24.8 GB). The dataset contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression. All conditions are available in three modality formats: Audio-only (16bit, 48kHz .wav), Audio-Video (720p H.264, AAC 48kHz, .mp4), and Video-only (no sound). Note, there are no song files for Actor_18.

The RAVDESS was developed by Dr Steven R. Livingstone, who now leads the Affective Data Science Lab, and Dr Frank A. Russo who leads the SMART Lab.

Citing the RAVDESS

The RAVDESS is released under a Creative Commons Attribution license, so please cite the RAVDESS if it is used in your work in any form. Published academic papers should use the academic paper citation for our PLoS1 paper. Personal works, such as machine learning projects/blog posts, should provide a URL to this Zenodo page, though a reference to our PLoS1 paper would also be appreciated.

Academic paper citation

Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. https://doi.org/10.1371/journal.pone.0196391.

Personal use citation

Include a link to this Zenodo page - https://zenodo.org/record/1188976

Commercial Licenses

Commercial licenses for the RAVDESS can be purchased. For more information, please visit our license page of fees, or contact us at ravdess@gmail.com.

Contact Information

If you would like further information about the RAVDESS, to purchase a commercial license, or if you experience any issues downloading files, please contact us at ravdess@gmail.com.

Example Videos

Watch a sample of the RAVDESS speech and song videos.

Emotion Classification Users

If you're interested in using machine learning to classify emotional expressions with the RAVDESS, please see our new RAVDESS Facial Landmark Tracking data set [Zenodo project page].

Construction and Validation

Full details on the construction and perceptual validation of the RAVDESS are described in our PLoS ONE paper - https://doi.org/10.1371/journal.pone.0196391.

The RAVDESS contains 7356 files. Each file was rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained adult research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity, interrater reliability, and test-retest intrarater reliability were reported. Validation data is open-access, and can be downloaded along with our paper from PLoS ONE.

Contents

Audio-only files

Audio-only files of all actors (01-24) are available as two separate zip files (~200 MB each):

• Speech file (Audio_Speech_Actors_01-24.zip, 215 MB) contains 1440 files: 60 trials per actor x 24 actors = 1440.
• Song file (Audio_Song_Actors_01-24.zip, 198 MB) contains 1012 files: 44 trials per actor x 23 actors = 1012.

Audio-Visual and Video-only files

Video files are provided as separate zip downloads for each actor (01-24, ~500 MB each), and are split into separate speech and song downloads:

• Speech files (Video_Speech_Actor_01.zip to Video_Speech_Actor_24.zip) collectively contains 2880 files: 60 trials per actor x 2 modalities (AV, VO) x 24 actors = 2880.
• Song files (Video_Song_Actor_01.zip to Video_Song_Actor_24.zip) collectively contains 2024 files: 44 trials per actor x 2 modalities (AV, VO) x 23 actors = 2024.

File Summary

In total, the RAVDESS collection includes 7356 files (2880+2024+1440+1012 files).

File naming convention

Each of the 7356 RAVDESS files has a unique filename. The filename consists of a 7-part numerical identifier (e.g., 02-01-06-01-02-01-12.mp4). These identifiers define the stimulus characteristics:

Filename identifiers

• Modality (01 = full-AV, 02 = video-only, 03 = audio-only).
• Vocal channel (01 = speech, 02 = song).
• Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).
• Emotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.
• Statement (01 = "Kids are talking by the door", 02 = "Dogs are sitting by the door").
• Repetition (01 = 1st repetition, 02 = 2nd repetition).
• Actor (01 to 24. Odd numbered actors are male, even numbered actors are female).

Filename example: 02-01-06-01-02-01-12.mp4

1. Video-only (02)
2. Speech (01)
3. Fearful (06)
4. Normal intensity (01)
5. Statement "dogs" (02)
6. 1st Repetition (01)
7. 12th Actor (12)
8. Female, as the actor ID number is even.

License information

The RAVDESS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, CC BY-NC-SA 4.0

Commercial licenses for the RAVDESS can also be purchased. For more information, please visit our license fee page, or contact us at ravdess@gmail.com.

Related Data sets

• RAVDESS Facial Landmark Tracking data set [Zenodo project page].

Additional file 1: Table for HDIs.

This is the description of a dataset. The description can be quite long and this can look strange in the public dataset page. In the drafts page there is a scrollbar in the scrollbar, why not in the public page? Well, the public page needs to support viewing on a mobile phone and this can make scroll bars within scrollbars within scrollbars a little difficult. So maybe it’ll be better to try using ellipses. Additionally only adding a description does not make it a new version. This is the description of a dataset. The description can be quite long and this can look strange in the public dataset page. In the drafts page there is a scrollbar in the scrollbar, why not in the public page? Well, the public page needs to support viewing on a mobile phone and this can make scroll bars within scrollbars within scrollbars a little difficult. So maybe it’ll be better to try using ellipses. Additionally only adding a description does not make it a new version.

This is the description of a dataset. The description can be quite long and this can look strange in the public dataset page. In the drafts page there is a scrollbar in the scrollbar, why not in the public page? Well, the public page needs to support viewing on a mobile phone and this can make scroll bars within scrollbars within scrollbars a little difficult. So maybe it’ll be better to try using ellipses. Additionally only adding a description does not make it a new version. This is the description of a dataset. The description can be quite long and this can look strange in the public dataset page. In the drafts page there is a scrollbar in the scrollbar, why not in the public page? Well, the public page needs to support viewing on a mobile phone and this can make scroll bars within scrollbars within scrollbars a little difficult. So maybe it’ll be better to try using ellipses. Additionally only adding a description does not make it a new version.