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.Learn more

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.

Dimensions analysis for Taylor & Francis Impact Assessment Author Survey

This dataset was compiled as part of a study investigating the trends, volume, and patterns of research data production and archiving in Kenya, using Dimensions.ai, a global research information platform. The study adopts an informetric approach to examine how Kenyan institutions, authors, and disciplines contribute to global research data and how such outputs are archived, cited, and accessed.

Data Source:

• Extracted from the Dimensions.ai database using filters for:
• Country: Kenya
• Publication Years 1952 to 2023
• Research Output Types: Datasets and linked publications
• Fields of Research: All disciplines

Contents and Variables:

The dataset includes:

• Title and DOI of dataset/publication
• Year of publication
• Author(s) and affiliation(s)
• Institutional contributors
• Source or repository (e.g., Figshare, Dryad, institutional repository)
• Type of data (raw, processed, software, code, etc.)
• Citation counts
• Altmetric indicators (if available)
• Research field/classification

Sample Size:

• A total of 45,737 datasets were retrieved and analysed, representing datasets linked to researchers or institutions affiliated with Kenya.

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 6^th, 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?

The online revenue of t-dimension.com amounted to US$1.4m in 2024. Discover eCommerce insights, including sales development, shopping cart size, and many more.

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.

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.

This study examined the effect of various Artificial Intelligence (AI) dimensions on audit quality within major audit firms in Nigeria, specifically focusing on Machine Learning, Natural Language Processing (NLP), Robotic Process Automation (RPA), and Data Analytics. A survey research design was utilized, allowing data collection from 249 respondents across four prominent audit firms (PwC, KPMG, Deloitte, and EY) through structured questionnaires. Using regression analysis, the study found that all AI dimensions positively influence audit quality, with Machine Learning having the most significant effect, followed by Data Analytics, NLP, and RPA. The findings indicate that Machine Learning enhances audit quality by enabling robust anomaly detection and improving risk assessment, while Data Analytics contributes to trend analysis and informed decision-making. NLP and RPA further support audit quality by analyzing unstructured data for compliance and automating repetitive tasks to minimize errors and increase efficiency. It concludes with recommendations for adopting Machine Learning and Data Analytics as priorities and emphasizes continuous assessments to evaluate AI’s impact on audit quality. Future research should investigate the longitudinal effects of AI integration and consider additional AI technologies, such as Cognitive Computing, to further validate the findings across different auditing contexts. These results highlight the transformative potential of AI in enhancing audit quality, compliance, and fraud detection in Nigeria’s auditing sector.

Summary of privacy dimensions on which each scenario was rated.

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.

This dataset contains data collected during a study "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems" conducted by Martin Lnenicka (University of Hradec Králové, Czech Republic), Anastasija Nikiforova (University of Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Serbia), Daniel Rudmark (Swedish National Road and Transport Research Institute, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Karlo Kević (University of Zagreb, Croatia), Anneke Zuiderwijk (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

As there is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems, the aim of the study is: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems, and develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations called Evolutionary Model of Public Data Ecosystems (EMPDE). Finally, three avenues for a future research agenda are proposed.

This dataset is being made public both to act as supplementary data for "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems ", Telematics and Informatics*, and its Systematic Literature Review component that informs the study.

Description of the data in this data set

PublicDataEcosystem_SLR provides the structure of the protocol

Spreadsheet#1 provides the list of results after the search over three indexing databases and filtering out irrelevant studies

Spreadsheets #2 provides the protocol structure.

Spreadsheets #3 provides the filled protocol for relevant studies.

The information on each selected study was collected in four categories:(1) descriptive information,(2) approach- and research design- related information,(3) quality-related information,(4) HVD determination-related information

Descriptive Information

Article number

A study number, corresponding to the study number assigned in an Excel worksheet

Complete reference

The complete source information to refer to the study (in APA style), including the author(s) of the study, the year in which it was published, the study's title and other source information.

Year of publication

The year in which the study was published.

Journal article / conference paper / book chapter

The type of the paper, i.e., journal article, conference paper, or book chapter.

Journal / conference / book

Journal article, conference, where the paper is published.

DOI / Website

A link to the website where the study can be found.

Number of words

A number of words of the study.

Number of citations in Scopus and WoS

The number of citations of the paper in Scopus and WoS digital libraries.

Availability in Open Access

Availability of a study in the Open Access or Free / Full Access.

Keywords

Keywords of the paper as indicated by the authors (in the paper).

Relevance for our study (high / medium / low)

What is the relevance level of the paper for our study

Approach- and research design-related information

Approach- and research design-related information

Objective / Aim / Goal / Purpose & Research Questions

The research objective and established RQs.

Research method (including unit of analysis)

The methods used to collect data in the study, including the unit of analysis that refers to the country, organisation, or other specific unit that has been analysed such as the number of use-cases or policy documents, number and scope of the SLR etc.

Study’s contributions

The study’s contribution as defined by the authors

Qualitative / quantitative / mixed method

Whether the study uses a qualitative, quantitative, or mixed methods approach?

Availability of the underlying research data

Whether the paper has a reference to the public availability of the underlying research data e.g., transcriptions of interviews, collected data etc., or explains why these data are not openly shared?

Period under investigation

Period (or moment) in which the study was conducted (e.g., January 2021-March 2022)

Use of theory / theoretical concepts / approaches? If yes, specify them

Does the study mention any theory / theoretical concepts / approaches? If yes, what theory / concepts / approaches? If any theory is mentioned, how is theory used in the study? (e.g., mentioned to explain a certain phenomenon, used as a framework for analysis, tested theory, theory mentioned in the future research section).

Quality-related information

Quality concerns

Whether there are any quality concerns (e.g., limited information about the research methods used)?

Public Data Ecosystem-related information

Public data ecosystem definition

How is the public data ecosystem defined in the paper and any other equivalent term, mostly infrastructure. If an alternative term is used, how is the public data ecosystem called in the paper?

Public data ecosystem evolution / development

Does the paper define the evolution of the public data ecosystem? If yes, how is it defined and what factors affect it?

What constitutes a public data ecosystem?

What constitutes a public data ecosystem (components & relationships) - their "FORM / OUTPUT" presented in the paper (general description with more detailed answers to further additional questions).

Components and relationships

What components does the public data ecosystem consist of and what are the relationships between these components? Alternative names for components - element, construct, concept, item, helix, dimension etc. (detailed description).

Stakeholders

What stakeholders (e.g., governments, citizens, businesses, Non-Governmental Organisations (NGOs) etc.) does the public data ecosystem involve?

Actors and their roles

What actors does the public data ecosystem involve? What are their roles?

Data (data types, data dynamism, data categories etc.)

What data do the public data ecosystem cover (is intended / designed for)? Refer to all data-related aspects, including but not limited to data types, data dynamism (static data, dynamic, real-time data, stream), prevailing data categories / domains / topics etc.

Processes / activities / dimensions, data lifecycle phases

What processes, activities, dimensions and data lifecycle phases (e.g., locate, acquire, download, reuse, transform, etc.) does the public data ecosystem involve or refer to?

Level (if relevant)

What is the level of the public data ecosystem covered in the paper? (e.g., city, municipal, regional, national (=country), supranational, international).

Other elements or relationships (if any)

What other elements or relationships does the public data ecosystem consist of?

Additional comments

Additional comments (e.g., what other topics affected the public data ecosystems and their elements, what is expected to affect the public data ecosystems in the future, what were important topics by which the period was characterised etc.).

New papers

Does the study refer to any other potentially relevant papers?

Additional references to potentially relevant papers that were found in the analysed paper (snowballing).

Format of the file.xls, .csv (for the first spreadsheet only), .docx

Licenses or restrictionsCC-BY

For more info, see README.txt

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.

A Six-dimensional Analysis of In-memory Aggregation

Comprehensive performance analytics and metrics for Ocean Spin Kingdom's Treasures (Dimension 49) by Konami.

A dataset containing customer commenters that obtain from user-generated content on Twitter and Instagram @byu_id. The dataset is used Bahasa, and it includes 32.684 raws. The following is an explanation of the variables in each column:

- comment: comments using Indonesian obtained from January 1 to June 30, 2021. This comment has been through a preprocessing process.

- sentiment: consists of neutral, positive, and negative sentiments of customers.

- dimension: there are six dimensions using customer experience proposed by Malviya and Varma (2012).

This dataset dismantles agricultural brands through 5 dimensions (visual, functional, value, emotional, and cultural) to explore the brand loyalty of agricultural brands to consumers and their willingness to choose.

The global Dynamic Dimension Weight Scanning (DWS) system market is experiencing robust growth, driven by the increasing need for efficient and accurate parcel and freight handling across various industries. The expanding e-commerce sector, demanding faster and more reliable delivery services, is a significant catalyst. Furthermore, the rising adoption of automation in logistics and warehousing, coupled with stricter regulations regarding package dimensions and weight, fuels the demand for DWS systems. These systems offer significant advantages over traditional manual measurement methods, including improved accuracy, reduced labor costs, faster processing speeds, and minimized shipping errors. The market is segmented by application (logistics, retail & warehousing), and system type (in-motion conveyor-based, forklift-mounted, AGV-based). While in-motion conveyor-based systems currently hold the largest market share due to their integration into automated sorting facilities, forklift-mounted and AGV-based systems are gaining traction due to their flexibility and suitability for diverse operational environments. Major players in the market, including Mettler Toledo, SICK, and Cubiscan, are constantly innovating to enhance system capabilities, such as integrating advanced imaging technologies and AI-powered data analysis for improved dimensional accuracy and efficiency. Geographical growth is expected to be strong across North America and Europe, followed by Asia-Pacific, driven by robust e-commerce growth and increasing industrial automation in these regions. The competitive landscape is characterized by both established players and emerging companies. Established players leverage their extensive industry experience and technological expertise to maintain a strong market presence. However, the increasing demand for customized solutions and cost-effective alternatives creates opportunities for smaller, specialized companies to carve a niche. Future growth will be shaped by technological advancements, such as improved sensor technologies and the integration of cloud-based data analytics, which will enable real-time monitoring and predictive maintenance. Furthermore, the development of more compact and easily integrable DWS systems will expand their adoption across a wider range of applications and industries. The market is also expected to see increased focus on data security and compliance with evolving data privacy regulations. This necessitates development of robust security protocols and data management systems within DWS solutions.

Comprehensive performance analytics and metrics for Ocean Spin Pirate's Riches (Dimension 49) by Konami.

This dataset is a cleaned set of data from the myPersonality Project which focuses on data from the IPIP and Hofstede Cultural Dimensions.