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

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

The authors have unveiled a gold standard dataset that aims to advance the realm of opinion mining within the software engineering domain. They've accomplished this by carefully selecting and annotating 2,000 Stack Overflow posts, employing the expertise of multiple human annotators. These posts have been meticulously categorized across four dimensions: sentiment analysis, identification of polar facts, categorization of aspects, and recognition of named entities.

Objectives:

• I leveraged advanced data visualization techniques to extract valuable insights from a comprehensive dataset. By visualizing sales patterns, customer behavior, and product trends, I identified key growth opportunities and provided actionable recommendations to optimize business strategies and enhance overall performance. you can find the GitHub repo here Link to GitHub Repository.

Data Description:

there are exactly 6 table and 1 is a fact table and the rest of them are dimension tables: Fact Table:

payment_key:
  Description: An identifier representing the payment transaction associated with the fact.
  Use Case: This key links to a payment dimension table, providing details about the payment method and related information.

customer_key:
  Description: An identifier representing the customer associated with the fact.
  Use Case: This key links to a customer dimension table, providing details about the customer, such as name, address, and other customer-specific information.

time_key:
  Description: An identifier representing the time dimension associated with the fact.
  Use Case: This key links to a time dimension table, providing details about the time of the transaction, such as date, day of the week, and month.

item_key:
  Description: An identifier representing the item or product associated with the fact.
  Use Case: This key links to an item dimension table, providing details about the product, such as category, sub-category, and product name.

store_key:
  Description: An identifier representing the store or location associated with the fact.
  Use Case: This key links to a store dimension table, providing details about the store, such as location, store name, and other store-specific information.

quantity:
  Description: The quantity of items sold or involved in the transaction.
  Use Case: Represents the amount or number of items associated with the transaction.

unit:
  Description: The unit or measurement associated with the quantity (e.g., pieces, kilograms).
  Use Case: Specifies the unit of measurement for the quantity.

unit_price:
  Description: The price per unit of the item.
  Use Case: Represents the cost or price associated with each unit of the item.

total_price:
  Description: The total price of the transaction, calculated as the product of quantity and unit price.
  Use Case: Represents the overall cost or revenue generated by the transaction.

Customer Table: customer_key:

Description: An identifier representing a unique customer.
Use Case: Serves as the primary key to link with the fact table, allowing for easy and efficient retrieval of customer-specific information.

name:

Description: The name of the customer.
Use Case: Captures the personal or business name of the customer for identification and reference purposes.

contact_no:

Description: The contact number associated with the customer.
Use Case: Stores the phone number or contact details for communication or outreach purposes.

nid:

Description: The National ID (NID) or a unique identification number for the customer.

Item Table: item_key:

Description: An identifier representing a unique item or product.
Use Case: Serves as the primary key to link with the fact table, enabling retrieval of detailed information about specific items in transactions.

item_name:

Description: The name or title of the item.
Use Case: Captures the descriptive name of the item, providing a recognizable label for the product.

desc:

Description: A description of the item.
Use Case: Contains additional details about the item, such as features, specifications, or any relevant information.

unit_price:

Description: The price per unit of the item.
Use Case: Represents the cost or price associated with each unit of the item.

man_country:

Description: The country where the item is manufactured.
Use Case: Captures the origin or manufacturing location of the item.

supplier:

Description: The supplier or vendor providing the item.
Use Case: Stores the name or identifier of the supplier, facilitating tracking of item sources.

unit:

Description: The unit of measurement associated with the item (e.g., pieces, kilograms).

Store Table: store_key:

Description: An identifier representing a unique store or location.
Use Case: Serves as the primary key to link with the fact table, allowing for easy retrieval of information about transactions associated with specific stores.

division:

Description: The administrative division or region where the store is located.
Use Case: Captures the broader geographical area in which...

Dimensions analysis for Taylor & Francis Impact Assessment Author Survey

This dataset can be used for modeling gravity models or other research purposes, and also serves as a valuable resource for students in macroeconomics courses.

The dataset "Gravity & Hofstede Data | 2015 (49 Countries)" includes data from the following 49 countries:

• Argentina
• Australia
• Austria
• Bangladesh
• Belgium
• Brazil
• Bulgaria
• Canada
• Chile
• China
• Colombia
• Croatia
• Denmark
• El Salvador
• Estonia
• Finland
• France
• Germany
• Greece
• Hungary
• India
• Indonesia
• Ireland
• Italy
• Japan
• Latvia
• Lithuania
• Luxembourg
• Malaysia
• Malta
• Mexico
• Morocco
• Netherlands
• Norway
• Pakistan
• Peru
• Philippines
• Poland
• Portugal
• Singapore
• Slovenia
• Spain
• Sweden
• Switzerland
• Thailand
• Trinidad and Tobago
• Turkey
• Uruguay
• Vietnam

Source:

World Bank WITS - Trade Data: https://wits.worldbank.org/ CEPII Gravity Model Database: https://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=8 Hofstede Insights - Dimension Data Matrix: https://geerthofstede.com/research-and-vsm/dimension-data-matrix/

The countries represent a diverse range of regions across the globe, rather than being concentrated in a single geographic area, in order to make the data more suitable for comprehensive research and analysis.

Hofstede data is presented as the absolute difference in levels between the exporting and importing countries; therefore, the closer the data is to 0, the more similar the countries are in terms of cultural dimensions.

State capacity is a core concept in political science research, and it is widely recognized that state institutions exert considerable influence on outcomes such as economic development, civil conflict, democratic consolidation, and international security. Yet, researchers across these fields of inquiry face common problems involved in conceptualizing and measuring state capacity. In this article, we examine these conceptual issues, identify three core dimensions of state capacity, and develop the expectation that they are mutually supporting and interlinked. We then use Bayesian latent variable analysis to estimate state capacity at the conjunction of indicators related to these dimensions. We find strong interrelationships between the three dimensions and produce a new, general-purpose measure of state capacity with demonstrated validity for use in a wide range of empirical inquiries. It is hoped that this project will provide effective guidance and tools for researchers studying the causes and consequences of state capacity.

The Computer Administered Panel Study (CAPS) collected demographic, personality, attitudinal, and other social psychological data from annual samples of University of North Carolina undergraduates from 1983 through 1988. Respondents spent 60 to 90 minutes per week for 20 weeks during the academic year answering questions via computer terminals. In their comparison of demographic and academic variables, researchers found few significant differences between respondents and the general undergraduate population. This dataset contains the Dimensions of Religious Commitment. Additional modules are available for free download through the Odum Institute's electronic archive.

The Dimensions of Religious Commitment is a questionnaire designed to measure the four dimensions of religiosity (Glock and Stark, 1965)--Belief, Ritual, Experience, and Knowledge. Originally, Glock and Stark proposed five dimensions, which include "Consequences" as the fifth dimension. However, the authors did not generate measures for this last dimension. Their analysis of the first four dimensions showed that these dimensions are essentially uncorrelated, and that other attitudes and behavior can be predicted from positions on these dimensions. Furthermore, the authors had constructed indices of the four dimensions, mainly by summing points assigned to each item that was answered in a certain direction. Among these indices, the orthodoxy index was found to be the best predictor of all other aspects of religiosity, implying that belief is the most significant component of religiosity. The entire Glock and Stark questionnaire contained more than 500 items. The interested reader may consult the published analysis.

Dimensions of analysis, criteria and decision factors according to Conitec recommendation reports.

This dataset was used as a data corpus for a bibliometric analysis with the title "Unveiling Openness in Energy Research: A Bibliometric Analysis Focusing on Open Access and Data Sharing Practices". The CSV file (2024-12-06_OpenAlex_API_download_works_Energy_Germany_(2013-2023)) was collected on December 6th, 2024, by using the OpenAlex API and search criteria: OpenAlex field "Energy", continent “Europe”, country “Germany”, and publication years 2013 – 2023. Based on this file, two sample files were extracted - one by subfield (2024-12-06_OpenAlex_API_dwonload_works_Energy_Germany_(2013-2023)_sampled_by_subfield) and another by year group (2024-12-06_OpenAlex_API_download_works_Energy_Germany_(2013-2023)_sampled_by_year_group). This dataset was collected and used to answer the following research questions: - What percentage of energy research publications are OA? How do the types (gold, green, etc.) of these publications differ? - Are there notable differences in OA and data sharing practices in different subfields of energy research? - How commonly are datasets for energy studies shared? What are the primary repositories used? - What kind of data sharing or publication practices are widespread? How has this evolved over the last decade?

Bolt Dimensional Analysis

## Overview

Bolt Dimensional Analysis is a dataset for object detection tasks - it contains Bolt annotations for 230 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Summary of privacy dimensions on which each scenario was rated.

The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

DEMATEL helps to rank the alternatives between the main and sub-dimensions of any decision-problem. Although, the ranking is not the only choice point as, via DEMATEL the causer and receiver relations are also to be revealed, helping decision-maker in predicting the importance of the dimensions of the main subjects.

Linewidth and Dimension Measuring Systems Market Analysis The global linewidth and dimension measuring systems market is projected to experience significant growth, reaching a value of XXX million by 2033, exhibiting a CAGR of XX% during the forecast period. Key drivers include the increasing demand for precision measurement in semiconductor manufacturing, advancements in optics and sensor technology, and growing adoption of metrology systems in research and development. The market is segmented by type (standard, high-precision, ultra high-precision), application (optical measurement, 3D measurement), and region. North America and Asia Pacific are expected to remain dominant markets, driven by the presence of major semiconductor manufacturers. Key trends in the market include the adoption of artificial intelligence and machine learning for automated data analysis, the development of non-destructive measurement techniques, and the integration of measurement systems with other manufacturing equipment.

This is a dataset from three-dimensional constrained variational analysis (3DCVA). It can be used to generate large-scale forcing data for SCM/CRM/LES, or evaluate model results.

This module introduces students to the theme of human dimensions in conservation and provides them with an opportunity to engage in practices related to social science research.

The Computer Administered Panel Study (CAPS) collected demographic, personality, attitudinal, and other social psychological data from annual samples of University of North Carolina undergraduates from 1983 through 1988. Respondents spent 60 to 90 minutes per week for 20 weeks during the academic year answering questions via computer terminals. In their comparison of demographic and academic variables, researchers found few significant differences between respondents and the general undergraduate population. The Dimensions of Religious Commitment is a questionnaire designed to measure four dimensions of religiosity (Glock and Stark, 1965): Belief, Ritual, Experience, and Knowledge. Originally, Glock and Stark proposed five dimensions, which include "Consequences" as the fifth dimension. However, the authors did not generate measures for this last dimension. Their analysis of the first four dimensions showed that these dimensions are essentially uncorrelated, and that other attitudes and behavior can be predicted from positions on these dimensions. Furthermore, the authors had constructed indices of the four dimensions, mainly by summing points assigned to each item that was answered in a certain direction. Among these indices, the orthodoxy index was found to be the best predictor of all other aspects of religiosity, implying that belief is the most significant component of religiosity. The entire Glock and Stark questionnaire contained more than 500 items. The interested reader may consult the published analysis.

Summary of scenarios presented to participants.

This paper tests whether the dimensions involved in preferential choice tasks are evaluated independently from one another. Common decision heuristics satisfy dimensional independence, and multi-strategy models that assume that decision makers use a repertoire of these heuristics predict that they are unable to represent and respond to dimensional dependencies in the decision environment. In contrast, some single-strategy models are able to violate dimensional independence, and subsequently adapt to environments that feature interacting dimensions. Across five experiments, this paper documents systematic violations of the assumption of dimensional independence. This suggests that decision makers are able to modify their behavior to respond to dimensional dependencies in their environment, and in turn those models that are unable to do this do not provide a full account of human strategy selection and behavior change. This paper ends with a discussion of ways in which some existing models can be modified to incorporate violations of dimensional independence.

This network project brings together economists, psychologists, computer and complexity scientists from three leading centres for behavioural social science at Nottingham, Warwick and UEA. This group will lead a research programme with two broad objectives: to develop and test cross-disciplinary models of human behaviour and behaviour change; to draw out their implications for the formulation and evaluation of public policy. Foundational research will focus on three inter-related themes: understanding individual behaviour and behaviour change; understanding social and interactive behaviour; rethinking the foundations of policy analysis. The project will explore implications of the basic science for policy via a series of applied projects connecting naturally with the three themes. These will include: the determinants of consumer credit behaviour; the formation of social values; strategies for evaluation of policies affecting health and safety. The research will integrate theoretical perspectives from multiple disciplines and utilise a wide range of complementary methodologies including: theoretical modeling of individuals, groups and complex systems; conceptual analysis; lab and field experiments; analysis of large data sets. The Network will promote high quality cross-disciplinary research and serve as a policy forum for understanding behaviour and behaviour change.