ISI? HOW TO KNOW IF A JOURNAL IS INDEXED?

JOURNALS INDEXING

It is important to know if a journal is indexed before you submit a manuscript. Here is a quick guide to know if your journal is indexed or not. Indexing usually reflects the quality of a journal. Nowadays, many institutions require a journal to be indexed especially in ISI in order to consider the publication for either applications of postgraduate programs applicants or faculty promotions.

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INDEX MEDICUS/MEDLINE/PUBMED

1. Just go to https://www.ncbi.nlm.nih.gov/nlmcatalog/
2. Choose NLM Catalog from the dropdown menu next to the search box
3. Enter the journal name
4. Click on the journal name in search results
5. Scroll down to the “In” field. You will see if the journal is indexed in any of the databases.

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SCOPUS

1. Go to https://www.scopus.com/sources
2. Enter the journal name in the search box
3. If the journal name appears in the search results, it is indexed

Instead of entering a journal name, you can alternatively search for journals in a specific specialty. Just enter the specialty’s name like “cardiology” or “periodontics”.

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EBSCO

1. Go to https://www.ebsco.com/title-lists
2. Look for the specialty you need like “Medicine” or “Dentistry”
3. Click on “Excel” or “HTML” that appears next to coverage list
4. Check the indexing start date and stop date for the journal of interest
5. Make sure that indexing stop date is empty otherwise, it means that the journal is not indexed in EBSCO anymore

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EMBASE

1. Go to https://www.elsevier.com/solutions/embase-biomedical-research/embase-coverage-and-content
2. Download the list of journals. Alternatively, you can download the list of journals for 2017 here.

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CLARIVATE ANALYTICS (PREVIOUSLY KNOWN AS THOMSON REUTERS/ISI)

1. Go to http://mjl.clarivate.com/
2. In the search field, write the full journal name and choose “Full Journal Title” from the dropdown menu next to the search box
3. Click on coverage below the journal name in the results
4. If Science Citation Index appears, this means it is index (image below)

   

   Alternatively, you can click on “Science Citation Index” before searching and click on visit list.

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That was a quick guide for journals citations. It is a useful source for academicians and postgraduate programs applicants.

Coding / Programming

List of Websites which teaches you Coding / Programming


1. CodeCademy.com

2. Coursera.com

3. Edx.org

4. Udemy.com

5. GitHub.com

6. Hackpledge.org

7. CodeAvengers.com

8. KhanAcademy.org

9. FreeCodeCamp.com

10. SoloLearn.com

How to Choose Plagiarism Checker: The Complete Guide 2019

The free solutions are not suitable for constant use, as they’re usually worse at detecting plagiarism and have a number of limitations. The paid solutions seem to be all equally perfect, but in fact, examination shows that for the same price you can get vastly different results.

Best plagiarism checkers:

• Comparative chart ↓
• 1. Quetext ↓
• 2. The Pensters ↓
• 3. Small SEO Tools ↓
• 4. Search Engine Reports ↓
• 5. Plagramme ↓
• 6. PlagScan ↓
• 7. PaperRater ↓
• 8. Noplag ↓
• 9. Copyscape ↓
• 10. Unicheck ↓
• 11. Plagiarism Software ↓
• 12. PlagTracker ↓
• 13. Plagiarisma ↓
• 14. Grammarly ↓
• 15. Turnitin 

The table below lets you overview competitors’ capabilities and price at a glance.. The quality rate in the third column is based on a practical test, in which I challenged the tools with different duplicates. While some of the tools successfully passed the test, recognizing cheating and accurately tracing the original sources, others were not so precise. In brief, to get the highest accuracy at no cost, go for The Pensters. This free solution partially detected duplicates in some of the sample texts I submitted. In turn, Grammarly proved to be the most (the truly) accurate tool offering professional paid solutions.

You can learn more details about each checker, their unique features and pricing policies in the overview below the table.

Best plagiarism checkers

Acceptable formats	Features	Quality of examination & Price
1. Quetext ↓
The Copy & Paste option only.	Multiple language support	1/5 Free
2. The Pensters ↓
Copy and paste	Plagiarism percentage Citation generator	3/5 Free
3. Small SEO Tools ↓
DOCX and TXT	Plagiarism percentage Downloadable report URL exclusion	1/5 Free
4. Search Engine Reports ↓
DOCX and TXT	Plagiarism percentage	1/5 Free
5. Plagramme ↓
DOC and DOCX for free version users HTML, PDF, RTF, TXT, EBOOK and other formats are available for licensed users through customization.	Plagiarism percentage Multiple language support Character replacement detection Downloadable report Integration via API and Moodle LMS Plugin Bad citation and paraphrase detection Online correction tool	4/5 The basic verification is free. The cost for additional features starts from $0.35 per page.
6. PlagScan ↓
DOC, DOCX, PDF, TXT, ODT, RTF, PPT, ZIP, URL, and others	Plagiarism percentage Customizable and downloadable report API integration Citation, reference and URL exclusion Doc vs doc comparison Multiple document check Email notifications Web import (e.g., from Dropbox)	4/5 Private users buy scanning packages: $5.99 for 6,500 words, $12.99 for 25,000 words, $24.99 for 62,500 words, $49.99 for 150,000 words. Organization can acquire subscription plans at $1.99/month or more.
7. PaperRater ↓
DOC, DOCX, TXT, ODT, RTF are available for users with a premium membership	Plagiarism percentage Automatic grammar, spelling and punctuation proofreader Writing suggestions Automatic paper grading	2/5 Premium plans: $14.95/month, $7.95/month if paid annually.
8. Noplag ↓
DOC, DOCX, RTF, TXT, ODT, HTML, PDF	Plagiarism percentage Customizable and downloadable report Citation and reference exclusion Doc vs doc comparison	3/5 Free when checking against a custom library. The cost for using ready databases starts from $0.04 per page.
9. Copyscape ↓
Users of the free versions can submit a URL (of HTML pages only). Premium account owners can also paste in text.	API Integration Doc vs doc comparison Batch Search for checking an entire website	3/5 Basic verification is free. The cost for Copyscape Premium starts from $0.05 per verification with a minimum purchase of 100 credits ($5).
10. Unicheck ↓
DOC, DOCX, RTF, TXT, ODT, HTML, PDF, ZIP	Plagiarism percentage Multiple language support Downloadable report Integration via API, LTI and Moodle LMS Plugin Doc vs doc comparison Multiple document check	4/5 $9.97/month, $20.95/three months, $59.88/year for individual clients
11. Plagiarism Software ↓
DOC, TXT and URL	Plagiarism percentage URL exclusion Keyword detection	3/5 First trial check is free. Premium plans: $10/month, $20/month, $30/month.
12. PlagTracker ↓
Copy & Paste option for the free version. The Premium account owners can also upload files in DOC, DOCX and TXT.	Plagiarism percentage Automated grammar proofreader Multiple language support Reference exclusion Downloadable report API Integration	2/5 Basic verification is free. Premium plans: $14.99/month.
13. Plagiarisma ↓
TXT, HTML, RTF, DOC, DOCX, XLS, XLSX, PDF, ODT, EPUB, FB2, PDB, and URL	Plagiarism percentage Multiple language support Downloadable report Automatic article rewriter Spell checker	1/5 The basic verification is free. Premium plans: $5/day, $25/month, $35/three months, $65/six months.
14. Grammarly ↓
DOC, DOCX, ODT, TXT, and RTF	Plagiarism percentage Downloadable report Automatic grammar, spelling and punctuation proofreader Synonym suggestions Citation generator	5/5 $29.95/month, $19.98/month (if paid $59.95 for three months), $11.66/month (if paid $139.95/year.
15. Turnitin ↓
DOC, DOCX, TXT, PDF, RTF, PS, WPD, HTML, HTM	Plagiarism percentage Customizable and downloadable report Citation and reference exclusion Doc vs doc comparison Feedback option	4/5 Pricing should be discussed directly with the Turnitin sales managers

Best Public Datasets for Machine Learning

AUTHORS:

Stacy Stanford, Machine Learning Memoirs Inc.

Roberto Iriondo, Machine Learning Department, Carnegie Mellon University.

PUBLISHED:

October 2, 2018

LAST UPDATED:

April 8, 2019

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First, a couple of pointers to keep in mind when searching for datasets. According to Carnegie Mellon University:

1.- A high-quality dataset should not be messy, because you do not want to spend a lot of time cleaning data.

2.- A high-quality dataset should not have too many rows or columns, so it is easy to work with.

3.- The cleaner the data, the better — cleaning a large dataset can be incredibly time consuming.

4.- There should be an interesting question/decision to answer, which in turn can be answered with data.

Dataset Finders

Google Dataset Search: Similar to how Google Scholar works, Dataset Search lets you find datasets wherever they’re hosted, whether it’s a publisher’s site, a digital library, or an author’s personal web page.

Kaggle: A data science site that contains a variety of externally contributed interesting datasets. You can find all kinds of niche datasets in its master list, from ramen ratings to basketball data to and even seattle pet licenses.

UCI Machine Learning Repository: One of the oldest sources of datasets on the web, and a great first stop when looking for interesting datasets. Although the data sets are user-contributed, and thus have varying levels of cleanliness, the vast majority are clean. You can download data directly from the UCI Machine Learning repository, without registration.

VisualData: Discover computer vision datasets by category, it allows searchable queries.

Find Datasets | CMU Libraries: Discover high quality datasets thanks to the collection of Huajin Wang, CMU.

General Datasets

Public Government datasets

Data.gov: This site makes it possible to download data from multiple US government agencies. Data can range from government budgets to school performance scores. Be warned though: much of the data requires additional research.

Food Environment Atlas: Contains data on how local food choices affect diet in the US.

School system finances: A survey of the finances of school systems in the US.

Chronic disease data: Data on chronic disease indicators in areas across the US.

The US National Center for Education Statistics: Data on educational institutions and education demographics from the US and around the world.

The UK Data Service: The UK’s largest collection of social, economic and population data.

Data USA: A comprehensive visualization of US public data.

Finance & Economics

Quandl: A good source for economic and financial data — useful for building models to predict economic indicators or stock prices.

World Bank Open Data: Datasets covering population demographics, a huge number of economic, and development indicators from across the world.

IMF Data: The International Monetary Fund publishes data on international finances, debt rates, foreign exchange reserves, commodity prices and investments.

Financial Times Market Data: Up to date information on financial markets from around the world, including stock price indexes, commodities and foreign exchange.

Google Trends: Examine and analyze data on internet search activity and trending news stories around the world.

American Economic Association (AEA): A good source to find US macroeconomic data.

Machine Learning Datasets:

Images

Labelme: A large dataset of annotated images.

ImageNet: The de-facto image dataset for new algorithms, organized according to the WordNet hierarchy, in which hundreds and thousands of images depict each node of the hierarchy.

LSUN: Scene understanding with many ancillary tasks (room layout estimation, saliency prediction, etc.)

MS COCO: Generic image understanding and captioning.

COIL100 : 100 different objects imaged at every angle in a 360 rotation.

Visual Genome: Very detailed visual knowledge base with captioning of ~100K images.

Google’s Open Images: A collection of 9 million URLs to images “that have been annotated with labels spanning over 6,000 categories” under Creative Commons.

Labelled Faces in the Wild: 13,000 labeled images of human faces, for use in developing applications that involve facial recognition.

Stanford Dogs Dataset: Contains 20,580 images and 120 different dog breed categories.

Indoor Scene Recognition: A very specific dataset and very useful, as most scene recognition models are better ‘outside’. Contains 67 Indoor categories, and 15620 images.

Sentiment Analysis

Multidomain sentiment analysis dataset: A slightly older dataset that features product reviews from Amazon.

IMDB reviews: An older, relatively small dataset for binary sentiment classification features 25,000 movie reviews.

Stanford Sentiment Treebank: Standard sentiment dataset with sentiment annotations.

Sentiment140: A popular dataset, which uses 160,000 tweets with emoticons pre-removed.

Twitter US Airline Sentiment: Twitter data on US airlines from February 2015, classified as positive, negative, and neutral tweets

Natural Language Processing

HotspotQA Dataset: Question answering dataset featuring natural, multi-hop questions, with strong supervision for supporting facts to enable more explainable question answering systems.

Enron Dataset: Email data from the senior management of Enron, organized into folders.

Amazon Reviews: Contains around 35 million reviews from Amazon spanning 18 years. Data include product and user information, ratings, and the plaintext review.

Google Books Ngrams: A collection of words from Google books.

Blogger Corpus: A collection 681,288-blog posts gathered from blogger.com. Each blog contains a minimum of 200 occurrences of commonly used English words.

Wikipedia Links data: The full text of Wikipedia. The dataset contains almost 1.9 billion words from more than 4 million articles. You can search by word, phrase or part of a paragraph itself.

Gutenberg eBooks List: Annotated list of ebooks from Project Gutenberg.

Hansards text chunks of Canadian Parliament: 1.3 million pairs of texts from the records of the 36th Canadian Parliament.

Jeopardy: Archive of more than 200,000 questions from the quiz show Jeopardy.

Rotten Tomatoes Reviews: Archive of more than 480,000 critic reviews (fresh or rotten).

SMS Spam Collection in English: A dataset that consists of 5,574 English SMS spam messages

Yelp Reviews: An open dataset released by Yelp, contains more than 5 million reviews.

UCI’s Spambase: A large spam email dataset, useful for spam filtering.

Self-driving

Berkeley DeepDrive BDD100k: Currently the largest dataset for self-driving AI. Contains over 100,000 videos of over 1,100-hour driving experiences across different times of the day and weather conditions. The annotated images come from New York and San Francisco areas.

Baidu Apolloscapes: Large dataset that defines 26 different semantic items such as cars, bicycles, pedestrians, buildings, streetlights, etc.

Comma.ai: More than 7 hours of highway driving. Details include car’s speed, acceleration, steering angle, and GPS coordinates.

Oxford’s Robotic Car: Over 100 repetitions of the same route through Oxford, UK, captured over a period of a year. The dataset captures different combinations of weather, traffic and pedestrians, along with long-term changes such as construction and roadworks.

Cityscape Dataset: A large dataset that records urban street scenes in 50 different cities.

CSSAD Dataset: This dataset is useful for perception and navigation of autonomous vehicles. The dataset skews heavily on roads found in the developed world.

KUL Belgium Traffic Sign Dataset: More than 10000+ traffic sign annotations from thousands of physically distinct traffic signs in the Flanders region in Belgium.

MIT AGE Lab: A sample of the 1,000+ hours of multi-sensor driving datasets collected at AgeLab.

LISA: Laboratory for Intelligent & Safe Automobiles, UC San Diego Datasets: This dataset includes traffic signs, vehicles detection, traffic lights, and trajectory patterns.

Bosch Small Traffic Light Dataset: Dataset for small traffic lights for deep learning.

LaRa Traffic Light Recognition: Another dataset for traffic lights. This is taken in Paris.

WPI datasets: Datasets for traffic lights, pedestrian and lane detection.

Clinical

MIMIC-III: Openly available dataset developed by the MIT Lab for Computational Physiology, comprising de-identified health data associated with ~40,000 critical care patients. It includes demographics, vital signs, laboratory tests, medications, and more.

Note:

If you are aware of other high-quality, public datasets, which you recommend to people for research and application of machine learning, deep learning, data science, etc. Please feel free to suggest them along with the reasons, why they should be included in the comments below or by emailing Stacy directly at hello@mlmemoirs.xyz.

If the reason is strong, we will analyze them and include them in this list. Also, please let us know your experience with using any of these datasets in the comments section,

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Acknowledgements:

The authors would like to thank the members of the AI Community for the immense support, along constructive criticism in preparation of this article.

DISCLAIMER: The views expressed in this article are those of the author(s) and do not represent the views of Carnegie Mellon University, Machine Learning Memoirs Inc. nor other companies (directly or indirectly) associated with the author(s). These writings are not intended to be final products, yet rather a reflection of current thinking, along being a catalyst for discussion and improvement.

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