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

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/. Dimensions is updated once every 24 hours, so the latest research can be viewed alongside existing information. 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, Dimensions is a one-stop shop for all COVID-19 related information. 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.

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 dataset contains the materials used in the session "Care to Share? Investigating Open Science practices adoption among researchers: a hackathon" presented at the Dutch National Open Science Festival on 22nd October 2024.

The data files are derived from: Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 ad contains two additional fields (Dimensions_Country and Dimensions_FoR) from Dimensions obtained on 15 October 2024, from Digital Science’s Dimensions platform, available at https://app.dimensions.ai.

File list:

PLOS-Dataset-for-Hackathon.xlsx

Data pertaining to the PLOS corpus of articles derived from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 with additional data from Dimensions.ai.

Comparator-Dataset-for-Hackathon.xlsx

Data pertaining to the Comparator corpus of articles derived from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 with additional data from Dimensions.ai.

Care to share resource sheet.pdf

Document outlining the questions to be investigated during the hackathon as well as key information about the dataset.

OSI-Column-Descriptions_v3_Dec23.pdf
This file is taken from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686. It describes the fields used in the two data files with the exception of Dimensions_Country and Dimensions_FoR. Descriptions for these are listed in the README tabs of the data files.

This is the underlying dataset used for the country analysis regarding the percentage of papers in Dimensions and Web of Science (WoS), published between 2015 and 2019 that are open access (OA), regardless of mode of OA.A paper was assigned a country affiliation based on the affiliation of the first author of a paper, thus each paper is only counted once, regardless whether the paper had multiple coauthors.Each row represents the data for a country. A country only appears once (i.e., each row is unique).Column headings:iso_alpha_2 = the ISO alpha 2 country code of the countrycountry = the name of the country as stated either in Dimensions or WoS.world_bank_region_2021 = pub_wos = total number of papers (document type articles and reviews) indexed in WoS, published from 2015 to 2019oa_pers_wos = Percentage of pub_wos that are OApub_dim = total number of papers (document type journal articles) indexed in Dimensions, published from 2015 to 2019oa_pers_dim = Percentage of pub_dim that are OArelative_diff = the relative difference between oa_pers_dim and oa_pers_wos using the following equation: ((x-y))/((x+y) ), with x representing the percentage of papers for the country in the Dimensions dataset that are OA, and y representing the percentage of papers for the country in the WoS dataset that are OA. In cases of "N/A" in a cell, a division by 0 occurred.Data availabilityRestriction apply to both datasets used to generate the aggregate data. The Web of Science data is owned by Clarivate Analytics. To obtain the bibliometric data in the same manner as authors (i.e. by purchasing them), readers can contact Clarivate Analytics at the following URL: https://clarivate.com/webofsciencegroup/solutions/web-of-science/contact-us/. The Dimensions data is owned by Digital Science, which has a programme that provides no cost access to its data. It can be accessed at: https://dimensions.ai/data_access.

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.

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.

The China Administrative Regions GIS Data: 1:1M, County Level, 1 July 1990 consists of geographic boundary data for the administrative regions of China as of 1 July 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.

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.

Dimensions.ai COVID-19 dataset preprocessed with CiteSpace.

The Optical Critical Dimension (CD) and Shape Metrology Systems market is experiencing robust growth, driven by the increasing demand for advanced semiconductor manufacturing processes. Miniaturization in electronics necessitates precise measurement and control of critical dimensions, making these systems indispensable for ensuring product quality and yield. The market is estimated to be valued at $2.5 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth is fueled by several key factors, including the expanding adoption of advanced semiconductor nodes (e.g., 5nm and 3nm), the rise of high-performance computing (HPC) and artificial intelligence (AI) applications demanding higher chip density, and the growing investment in research and development of next-generation metrology techniques. Key players such as KLA, ASML, and Onto Innovation are at the forefront of innovation, continually developing more sophisticated and precise measurement solutions to meet the evolving industry needs. The market segmentation is largely based on technology type (optical, electron beam, x-ray), application (logic, memory, foundry), and end-use industry (consumer electronics, automotive, healthcare). The market faces challenges such as the high cost of these sophisticated systems and the complexity of their implementation and maintenance. Despite these restraints, the long-term outlook remains positive, driven by the ongoing trend of miniaturization and the increasing demand for higher-performing chips across various applications. Emerging trends such as the integration of artificial intelligence and machine learning in metrology systems are further enhancing their accuracy and efficiency. Regional growth is expected to be largely concentrated in Asia-Pacific, particularly in China, Taiwan, and South Korea, due to the significant presence of leading semiconductor manufacturers in these regions. North America and Europe are also expected to maintain a significant market share, driven by strong research and development activities and the presence of established semiconductor companies. The continued expansion of the global semiconductor industry is set to propel significant growth for the Optical CD and Shape Metrology Systems market in the coming years.

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.

This list of Digital Object Identifiers (DOI) represents the results of the Sustainable Development Goal (SDG) content classifier for Goal No.3 Good Health and Well-Being. 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 China Maps Bibliographic Database is an historical collection of bibliographic information for more than 400 maps of China. The information resides in a searchable database and includes title, author/editor, publisher, location, projection, year, elevation, land cover type (forest, desert, marsh/swamp, grassland), vegetation, transportation (roads, railroads), rivers and lakes, spatial coverage (provincial, county, township), and language for maps published from 1765 to 1994. The information is available in both English and Chinese (GB Code for Chinese Characters). This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Center for International Earth Science Information Network (CIESIN).

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Kentucky Site Files" data publication.

This list of Digital Object Identifiers (DOI) represents the results of the Sustainable Development Goal (SDG) content classifier for Goal No.7 Affordable and Clean Energy. 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 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.