Dimensions is the largest database of research insight in the world. It represents the most comprehensive collection of linked data related to the global research and innovation ecosystem available in a single platform. Because Dimensions maps the entire research lifecycle, you can follow academic and industry research from early stage funding, through to output and on to social and economic impact. Businesses, governments, universities, investors, funders and researchers around the world use Dimensions to inform their research strategy and make evidence-based decisions on the R&D and innovation landscape. With Dimensions on Google BigQuery, you can seamlessly combine Dimensions data with your own private and external datasets; integrate with Business Intelligence and data visualization tools; and analyze billions of data points in seconds to create the actionable insights your organization needs. Examples of usage: Competitive intelligence Horizon-scanning & emerging trends Innovation landscape mapping Academic & industry partnerships and collaboration networks Key Opinion Leader (KOL) identification Recruitment & talent Performance & benchmarking Tracking funding dollar flows and citation patterns Literature gap analysis Marketing and communication strategy Social and economic impact of research About the data: Dimensions is updated daily and constantly growing. It contains over 112m linked research publications, 1.3bn+ citations, 5.6m+ grants worth $1.7trillion+ in funding, 41m+ patents, 600k+ clinical trials, 100k+ organizations, 65m+ disambiguated researchers and more. The data is normalized, linked, and ready for analysis. Dimensions is available as a subscription offering. For more information, please visit www.dimensions.ai/bigquery and a member of our team will be in touch shortly. If you would like to try our data for free, please select "try sample" to see our openly available Covid-19 data.Scopri di più

Traffic analytics, rankings, and competitive metrics for dimensions.ai as of September 2025

IntroductionArtificial intelligence (AI) has created a plethora of prospects for communication. The study aims to examine the impacts of AI dimensions on family communication. By investigating the multifaceted effects of AI on family communication, this research aims to provide valuable insights, uncover potential concerns, and offer recommendations for both families and society at large in this digital era.MethodA convenience sampling technique was adopted to recruit 300 participants.ResultsA linear regression model was measured to examine the impact of AI dimensions which showed a statistically significant effect on accessibility (p = 0.001), personalization (p = 0.001), and language translation (p = 0.016).DiscussionThe findings showed that in terms of accessibility (p = 0.006), and language translation (p = 0.010), except personalization (p = 0.126), there were differences between males and females. However, using multiple AI tools was statistically associated with raising concerns about bias and privacy (p = 0.015), safety, and dependence (p = 0.049) of parents.ConclusionThe results showed a lack of knowledge and transparency about the data storage and privacy policy of AI-enabled communication systems. Overall, there was a positive impact of AI dimensions on family communication.

Content

This file contains all relevant publications, datasets, and clinical trials from Dimensions that are related to COVID-19. The content has been exported from Dimensions using a query in the openly accessible Dimensions application, which you can access at https://covid-19.dimensions.ai/.With its range of research outputs including datasets and clinical trials, both of which are just as important as journal articles in the face of a potential pandemic.

Audience

Who is this Data for? Basically everyone, but specifically:
- Researchers
- Journalists
- Health care professionals
- Members of the general public
- Anyone else that would benefit from the most up-to-date research information about COVID-19.

Acknowledgements

https://static-content.dimensions.ai/static/radar/default/logo-20190221.svg">

Dimensions are pleased to support the global research effort to manage and minimize the impact of COVID-19
through the timely and efficient sharing of research information.

I found this Data here. Thought of projecting it to our Data Science community.✌️

This dataset from Dimensions.ai contains all published articles, preprints, clinical trials, grants and research datasets that are related to COVID-19. This growing collection of research information now amounts to hundreds of thousands of items, and it is the only dataset of its kind. You can find an overview of the content in this interactive Data Studio dashboard: https://reports.dimensions.ai/covid-19/ The full metadata includes the researchers and organizations involved in the research, as well as abstracts, open access status, research categories and much more. You may wish to use the Dimensions web application to explore the dataset: https://covid-19.dimensions.ai/. This dataset is for researchers, universities, pharmaceutical & biotech companies, politicians, clinicians, journalists, and anyone else who wishes to explore the impact of the current COVID-19 pandemic. It is updated daily, and free for anyone to access. Please share this information with anyone you think would benefit from it. If you have any suggestions as to how we can improve our search terms to maximise the volume of research related to COVID-19, please contact us at support@dimensions.ai. About Dimensions: Dimensions is the largest database of research insight in the world. It contains a comprehensive collection of linked data related to the global research and innovation ecosystem, all in a single platform. This includes hundreds of millions of publications, preprints, grants, patents, clinical trials, datasets, researchers and organizations. Because Dimensions maps the entire research lifecycle, you can follow academic and industry research from early stage funding, through to output and on to social and economic impact. This Covid-19 dataset is a subset of the full database. The full Dimensions database is also available on BigQuery, via subscription. Please visit www.dimensions.ai/bigquery to gain access.Más información

Claude-2 is the most trustworthy AI model based on responsible AI dimensions in 2024.

The aim of this research:Scientometric investigation of publication activity of China University of Petroleum and Sinopec during 2016-2019 years. Topic Mining from bibliografic texts by network analysis and clustering.Bibliographic Resources: Web of Science Core Collection; Dimensions - https://app.dimensions.ai/discover/publicationMain query:Results: 11,226(from Web of Science Core Collection)You searched for: ORGANIZATION-ENHANCED: (China University of Petroleum OR Sinopec)Timespan: 2016-2019. Indexes: SCI-EXPANDED, ESCIWoS Sort by Times Cited 23 files savedrecs([0-22]).txtFiles description:list of resources Dimensions.txtlist-of-files-0-7-KW-634.txtWorkflow-some queries from WoS.txtfiles-15-22-WoS.txtMain tools:VOSviewer - a software tool for constructing and visualizing bibliometric networks - http://www.vosviewer.com/KH Coder - a free software for quantitative content analysis or text mining - http://khcoder.net/en/Notepad++ - a free source code editor - https://notepad-plus-plus.org/SmoothCSV - a powerful CSV file editor - https://smoothcsv.com/2/ The Next To-Do List:define the files format for the table of contents and the list of captions for picturesvisual comparison of graphical resources hosted on Figsharebibliometrics on define topic refers to funding agencies (based on WoS and Scopus; We have no subscription for the dimensions.ai)Suggestions are welcome

This collection contains five sets of datasets: 1) Publication counts from two multidisciplinary humanities data journals: the Journal of Open Humanities Data and Research Data in the Humanities and Social Sciences (RDJ_JOHD_Publications.csv); 2) A large dataset about the performance of research articles in HSS exported from dimensions.ai (allhumss_dims_res_papers_PUB_ID.csv); 3) A large dataset about the performance of datasets in HSS harvested from the Zenodo REST API (Zenodo.zip); 4) Impact and usage metrics from the papers published in the two journals above (final_outputs.zip); 5) Data from Twitter analytics on tweets from the @up_johd account, with paper DOI and engagement rate (twitter-data.zip).

Please note that, as requested by the Dimensions team, for 2 and 4, we only included the Publication IDs from Dimensions rather than the full data. Interested parties only need the Dimensions publications IDs to retrieve the data; even if they have no Dimensions subscription, they can easily get a no-cost agreement with Dimensions, for research purposes, in order to retrieve the data.

The dataset contains locations and attributes of owner lines with dimensions. The tax information (attribution) comes from the Office of Tax and Revenue's Public Extract file. The creation of this layer is automated, occurs weekly, and uses the most currently available tax information. The date of the extract can be found in the EXTRACTDAT field in this layer.

Information about grants funded by NSF to support SES research from 2000-2015. The grants included in this dataset are a subset that we identified as having an SES research focus from a set of grants accessed using the Dimensions platform (https://dimensions.ai). CSV file with 35 columns and names in header row: "Grant Searched" lists the granting NSF program (text); "Grant Searched 2" lists a secondary granting NSF program, if applicable (text); "Grant ID" is the ID from the Dimensions platform (string); "Grant Number" is the NSF Award number (integer); "Number of Papers (NSF)" is the count of publications listed under "PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH" in the NSF Award Search page for the grant (integer); "Number of Pubs Tracked" is the count of publications from "Number of Papers (NSF)" included in our analysis (integer); "Publication notes" are our notes about the publication information. We used "subset" to denote when a grant was associated with >10 publications and we used a random sample of 10 publications in our analysis (text); "Unique ID" is our unique identifier for each grant in the dataset (integer); "Collaborative/Cross Program" denotes whether the grant was submitted as part of a set of collaborative or cross-program proposals. In this case, all linked proposals are given the same unique identifier and treated together in the analysis. (text); "Title" is the title of the grant (text); "Title translated" is the title of the grant translated to English, where applicable (text); "Abstract" is the abstract of the grant (text); "Abstract translated" is the abstract of the grant translated to English, where applicable (text); "Funding Amount" is the numeric value of funding awarded to the grant (integer); "Currency" is the currency associated with the field "Funding Amount" (text); "Funding Amount in USD" is the numeric value of funding awarded to the grant expressed in US Dollars (integer); "Start Date" is the start date of the grant (mm/dd/yyyy); "Start Year" is the year in which grant funding began (year); "End Date" is the end date of the grant (mm/dd/yyyy); "End Year" is the year in which the term of the grant expired (year); "Researchers" lists the Principal Investigators on the grant in First Name Last Name format, separated by semi-colons (text); "Research Organization - original" gives the affiliation of the lead PI as listed in the grant (text); "Research Organization - standardized" gives the affiliation of each PI in the list, separated by semi-colons (text); "GRID ID" is a list of the unique identifier for each the Research Organization in the Global Research Identifier Database [https://grid.ac/?_ga=2.26738100.847204331.1643218575-1999717347.1643218575], separated by semi-colons (string); "Country of Research organization" is a list of the countries in which each Research Organization is located, separated by semi-colons (text); "Funder" gives the NSF Directorate that funded the grant (text); "Source Linkout" is a link to the NSF Award Search page with information about the grant (URL); "Dimensions URL" is a link to information about the grant in Dimensions (URL); "FOR (ANZSRC) Categories" is a list of Field of Research categories [from the Australian and New Zealand Standard Research Classification (ANZSRC) system] associated with each grant, separated by semi-colons (string); "FOR [1-5]" give the FOR categories separated. "NOTES" provide any other notes added by the authors of this dataset during our processing of these data.

Longitudinal behavior of Altmetrics in Orthodontic research: Analysis of the orthodontic journals indexed in the journal citation reports from 2014 to 2018 A first search was carried out, in December 2019, in the inCites JCR database to select orthodontic journals that were included in the category of dentistry, oral surgery, and medicine of the JCR during the period from 2014 to 2018. The online interest generated by the orthodontic research outputs, was observed and tracked through the Dimensions free app https://app.dimensions.ai/discover/publication in the Dimensions database. The search was limited to the nine journals listed in the JCR in 2018, which were the American Journal of Orthodontics & Dentofacial Orthopedics (AJODO), The Angle Orthodontist, The European Journal of Orthodontics (EJO), Progress in Orthodontics, Korean Journal of Orthodontics (KJO), Orthodontics & Craniofacial Research (OCR), Journal of Orofacial Orthopedics/Fortschritte der Kieferorthopädie, Seminars in Orthodontics, and the Australian Orthodontic Journal. The Dimension App was used to carry out the search and the following filters were applied: publication year (2018 or 2017 or 2016 or 2015 or 2014); source title (American Journal of Orthodontics & Dentofacial Orthopedics OR The European Journal of Orthodontics OR The Angle Orthodontist OR Korean Journal of Orthodontics OR Orthodontics & Craniofacial Research OR Journal of Orofacial Orthopedics/Fortschritte der Kieferorthopädie OR Progress in Orthodontics OR Seminars in Orthodontics OR the Australian Orthodontic Journal). Data were exported to an Excel data sheet (Microsoft Office for Mac version 16.43). In December 2021 a second search was performed on the Dimension Web app by the members of the research team introducing the DOI or the article title of the 3678 items included in the 2019 sample. Here are presented the data related to the 3678 analysed Items divided per journal, the number of altmetrics mentions is presented for each item at both time intervals as well as their change over the studied period.

The China Administrative Regions GIS Data: 1:1M, County Level, 1990 consists of geographic boundary data for the administrative regions of China as of 31 December 1990. The data includes the geographical location, area, administrative division code, and county and island name. The data are at a scale of one to one million (1:1M) at the national, provincial, and county level. This data set is produced in collaboration with the Center for International Earth Science Information Network (CIESIN), Chinese Academy of Surveying and Mapping (CASM), and the University of Washington as part of the China in Time and Space (CITAS) project.

RAIL-HH-10K: Multi-Dimensional Safety Alignment Dataset

The first large-scale safety dataset with 99.5% multi-dimensional annotation coverage across 8 ethical dimensions.

📖 Read Blog • 📖 Paper (Coming Soon) • 🚀 Quick Start • 🔌 RAIL API • 💻 Examples

  🌟 What Makes RAIL-HH-10K Special?





  🎯 Near-Complete Coverage

99.5% dimension coverage across all 8 ethical dimensions Most existing datasets: 40-70% coverage RAIL-HH-10K: 98-100% coverage… See the full description on the dataset page: https://huggingface.co/datasets/responsible-ai-labs/RAIL-HH-10K.

The GuoBiao (GB) Codes for the Administrative Divisions of the People's Republic of China consists of geographic codes for the administrative divisions of China. The data includes provinces (autonomous regions, municipalities directly under the Central Government), prefectures (prefecture-level cities, autonomous prefectures, leagues), and counties (districts, county-level cities, autonomous counties, banners) for 1 January 1982 to 31 December 1992. This data set is produced in collaboration with the Chinese Academy of Surveying and Mapping (CASM), University of Washington as part of the China in Time and Space (CITAS) project, and the Columbia University Center for International Earth Science Information Network (CIESIN).

This list of Digital Object Identifiers (DOI) represents the results of the Sustainable Development Goal (SDG) content classifier for Goal No.11 Sustainable Cities and Communities. All DOIs contain at least one author whose is affiliated with a research organisation in the Netherlands. The classifier was created as part of a unique collaboration between Springer Nature, Digital Science, and VSNU/UKB. For further information, see below.

All data in the Excel was sourced from Dimensions, an inter-linked research information system provided by Digital Science (https://www.dimensions.ai) The data has been released for strictly non-commercial use under a CC-BY-NC-SA 4.0 license. The data may be analysed for non-commercial reports or studies related to the SDGs until December 2022. Thereafter further reuse of the data requires Digital Science's approval.

Background information: Springer Nature, together with Digital Science, and The Association of Universities in the Netherlands (VSNU) and the Dutch Consortium of University Libraries and The National Library of The Netherlands (UKB) created a model from a selection of Sustainable Development Goals (SDG) focusing on societal aspects in the United Nations (UN) Sustainability Agenda. Keyword search strings for five goals were defined, with input from the project partners, in order to produce training sets based on publications from the Dimensions platform. Using improved search strings instead of a manual build-up of respective sets of SDG related publications, the created training sets were used to apply Natural Language Processing and Machine Learning resulting in a classification scheme based on five UN SDGs.

The dataset contains the publications' ID and DOI numbers used in the bibliometric analysis for the paper "Exploring adult age-at-death research in anthropology: Bibliometric mapping and content analysis," which was published in Forensic Sciences. Dimensions only granted the authors permission to disclose the publication IDs and DOIs for this study. However, using the identification codes, it is possible to extract titles' full metadata through Dimensions (https://www.dimensions.ai/).

As of 2024, the financial services sector showed varied levels of responsible AI adoption across different dimensions. The fairness dimension had the highest adoption rate, where only ***** percent of the respondents did not adopt any of the listed measures, and ** percent adopted at least half of the measures. Cybersecurity and transparency were the dimensions with the lowest number of adopted measures.

In this project, we will use a dataset of movies with plots. The original dataset is on https://www.kaggle.com/datasets/gabrieltardochi/wikipedia-movie-plots-with-plot-summaries

The plots were scraped from Wikipedia by jrobischon and then summarized by gabrieltardochi using DistilBART-CNN-12-6 model.

There are two plots, one is full and the other is shortened. I used CO.HERE AI to vectorize them. The processed dataset was published on Kaggle with two extra columns:

plot_vector_1024: Vectorized of the full plot in 1024 dimension (a vector of 1024 float numbers) plot_summary_vector_1024: Vectorized of the summarized plot in 1024 dimension (a vector of 1024 float numbers)

The detail of the process is on https://github.com/linhhlp/Machine-Learning-Applications/Text-2-Vect-Vector-Search

The Global Development Potential Indices (DPIs) data set contains thirteen sector-level DPIs for sectors related to renewable energy (concentrated solar power, photovoltaic solar, wind, hydropower), fossil fuels (coal, conventional and unconventional oil and gas), mining (metallic, non-metallic), and agriculture (crop, biofuels expansion). The DPI for each sector represents land suitability that accounts for both resource potential and development feasibility. Each DPI is a 1-km spatially-explicit, global land suitability map that has been validated using locations of current and planned development, and examined for uncertainty and sensitivity. The DPIs can be used to identify lands with current favorable economic and physical conditions for individual sector expansion and assist in planning for sector and cumulative development across the globe.

The CODATA Catalog of Roads Data Sets, Version 1 contains 367 entries describing national-level road network data sets for 147 countries and four entries describing global data sets. It was produced by the Columbia University Center for International Earth Science Information Network (CIESIN) under the oversight of the CODATA Global Roads Data Development Working Group, and as a contribution to the development of the Global Roads Open Access Data Set (gROADS).