Last week, Web of Science rolled out a new interface. As it is one of the most heavily used e-resources, here we highlight some of the noteworthy new features.

IEEE DataPort is an online data repository developed by IEEE. This post is a preliminary review of this relatively new offering. Potential issues on access and deposit of datasets are highlighted.

Open Science, or Open Scholarship, is more than making your papers and research data open access. This May, UNESCO adopted a draft of the Recommendation on Open Science. All researchers should get to know Open Science as it becomes a guidance of good research practices.

Altmetrics are new measures of impact by capturing online mentioning of research outputs such as papers and datasets. Altmetric Explorer, Plum Analytics and Impactstory are some popular altmetrics tools, and the Library has recently started a subscription to Altmetric Explorer. In this post, you will learn more about Altmetric Explorer.

Unlike traditional citation databases which would yield results by keyword or topic search, Inciteful creates a graph of academic papers based on “seed papers” of your choice and helps you gain insight from it.

More and more journals require you to write a Data Availability Statement (DAS) when you submit a manuscript. What is DAS? How do you write one?

There are many things we can learn from peer review processes of others. Not only we learn how to give constructive feedback, it also helps us stay up to date with research developments and improve our critical thinking.

The University of California and the publisher Elsevier drew a landmark contract for open access publishing. The agreement costs about US$11 million per year. What does it mean for us at HKUST?

Google Dataset Search is a new search engine which allows you to search for datasets hosted in thousands of repositories across the Web. It looks on publisher sites, digital libraries, dataset providers, and on authors' personal webpages for metadata tags and returns a list of data repositories that best describes the datasets you need for your research.   On the other hand, if you want to share your datasets and make them publicly accessible, you can follow the Google's guidelines for dataset providers which is an open standard for tagging and structuring your datasets. These guidelines include salient information about datasets: who created the dataset, when it was published, how the data was collected, what the terms are for using the data, etc. The overall approach is to improve discovery of the datasets by adopting a common standard by which Google and other search engines can better understand the content of the datasets.   Here are some examples of what can qualify as a dataset as suggested by Google:

• A table or a CSV file with some data
• An organized collection of tables
• A file in a proprietary format that contains data
• A collection of files that together constitute some meaningful dataset
• A structured object with data in some other format that you might want to load into a special tool for processing
• Images capturing data
• Files relating to machine learning, such as trained parameters or neural network structure definitions
• Anything that looks like a dataset to you

For example, if you want to obtain COVID-19 data, you might try this query in Dataset Search:   You will see data from more than 100 different data sources. The results can be filtered by last updated (past month, year, or three years), download format (table, text, image, or others), usage rights (commercial or non-commercial use), topic (subject disciplines) and if it is possible to access the dataset for free. After reading the description and deciding the dataset is useful, you can click on the blue button to navigate to the external site for further actions. - By Lewis Li, Library

Datasets cannot speak for themselves. It is the way that you describe your research data and the methodology that matters.