The Data Science Ontology is a research project of IBM Research AI and Stanford University Statistics. Its long-term objective is to improve the efficiency and transparency of collaborative, data-driven science.

Context

The AI Index is a starting point for informed conversations about the state of artificial intelligence (AI). The report aggregates a diverse set of metrics, and makes the underlying data easily accessible to the general public.

Acknowledgements

This data is from Stanford University's Human Centered AI Center and is posted here: https://hai.stanford.edu/ai-index/2019

Timeseries data from '158 - Cabrillo Point Nearshore, CA (46240)' (edu_ucsd_cdip_158) cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=webmaster.ndbc@noaa.gov,,mailto:hmsinformation@lists.stanford.edu,None,mailto:hmsinformation@lists.stanford.edu,feedback@axiomdatascience.com contributor_name=NOAA National Data Buoy Center (NDBC),World Meteorological Organization (WMO),Hopkins Marine Station, Stanford University,U.S. Army Corps of Engineers (USACE),Hopkins Marine Station, Stanford University,Axiom Data Science contributor_role=contributor,contributor,collaborator,sponsor,sponsor,processor contributor_role_vocabulary=NERC contributor_url=https://www.ndbc.noaa.gov/,https://wmo.int/,https://hopkinsmarinestation.stanford.edu/,http://www.usace.army.mil/,https://hopkinsmarinestation.stanford.edu/,https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=sea_surface_wave_from_direction,sea_surface_wave_mean_period_qc_agg,sea_water_temperature,sea_water_temperature_qc_agg,sea_surface_wave_period_at_variance_spectral_density_maximum,z,time,sea_surface_wave_period_at_variance_spectral_density_maximum_qc_agg,sea_surface_wave_significant_height,sea_surface_wave_from_direction_qc_agg,sea_surface_wave_mean_period,sea_surface_wave_significant_height_qc_agg&time>=max(time)-3days Easternmost_Easting=-121.9071 featureType=TimeSeries geospatial_lat_max=36.6263 geospatial_lat_min=36.6263 geospatial_lat_units=degrees_north geospatial_lon_max=-121.9071 geospatial_lon_min=-121.9071 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from Coastal Data Information Program (CDIP) at https://cdip.ucsd.edu/themes/cdip?pb=1&u2=s:158:st:1&d2=p9 id=130294 infoUrl=https://sensors.ioos.us/#metadata/130294/station institution=Coastal Data Information Program (CDIP) naming_authority=com.axiomdatascience Northernmost_Northing=36.6263 platform=buoy platform_name=158 - Cabrillo Point Nearshore, CA (46240) platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://cdip.ucsd.edu/themes/cdip?pb=1&u2=s:158:st:1&d2=p9,https://cdip.ucsd.edu/themes/cdip?pb=1&u2=s:158:st:1&d2=p9,https://www.ndbc.noaa.gov/station_page.php?station=46240,https://cdip.ucsd.edu/m/documents/data_processing.html#quality-control sourceUrl=https://cdip.ucsd.edu/themes/cdip?pb=1&u2=s:158:st:1&d2=p9 Southernmost_Northing=36.6263 standard_name_vocabulary=CF Standard Name Table v72 station_id=130294 time_coverage_end=2025-11-16T05:58:20Z time_coverage_start=2008-12-02T23:14:27Z Westernmost_Easting=-121.9071 wmo_platform_code=46240

This dataset is about universities in Stanford. It has 1 row. It features 6 columns including country, city, total students, and latitude.

Studyportals dataset provides a comprehensive set of data that can help students find Master's programs in Computer Science and IT worldwide. 🌍

This Dataset features a database of universities and colleges from around the world that offer Master's programs in these fields. Students can search for programs based on various criteria such as location, specialization, duration, and tuition fees.

• Once students find a program of interest, Studyportals provides detailed information about the program, including the curriculum, admission requirements, application deadlines, and tuition fees. Students can also read reviews and ratings from other students who have completed the program, which can help them make informed decisions about their education.

• Studyportals also offers a range of resources to help students prepare for their Master's program in Computer Science and IT. These resources include language courses, standardized test preparation courses, and career advice.

• Overall, Studyportals is a useful resource for students who are interested in pursuing a Master's program in Computer Science and IT, as it provides a comprehensive database of programs and offers a range of resources to help students prepare for their studies.

1. Times Higher Education Ranking (2018): The Times Higher Education (THE) ranking is one of the most prestigious university rankings in the world. It ranks universities based on various factors such as research, teaching quality, knowledge transfer, and international outlook. Some universities that may be included in this ranking are the University of Oxford (UK), the University of Cambridge (UK), and Stanford University (USA).

2. Shanghai Jiao Tong University Ranking (2017): The Shanghai Jiao Tong University (SJTU) ranking is another well-known university ranking system. It focuses mainly on research performance and ranks universities based on factors such as Nobel Prize winners, highly cited researchers, and publications in top journals. Some universities that may be included in this ranking are Harvard University (USA), the University of Tokyo (Japan), and the University of Toronto (Canada).

   1. TopUniversities Ranking (2018): The TopUniversities ranking is based on several factors such as academic reputation, employer reputation, and research impact. It is widely used by students and universities to compare different institutions. Some universities that may be included in this ranking are the Massachusetts Institute of Technology (USA), the University of Cambridge (UK), and the University of Melbourne (Australia).

3. U.S. News & World Report Ranking (2018): The U.S. News & World Report ranking focuses primarily on American universities and colleges, but it also includes some international institutions. It ranks universities based on factors such as research performance, student outcomes, and academic reputation. Some universities that may be included in this ranking are Princeton University (USA), the California Institute of Technology (USA), and the University of Oxford (UK).

"""When using Studyportals, students can search for universities based on their rankings in these systems and also filter by location, program, tuition fees, and other criteria. This can help students find universities that meet their specific needs and preferences."""

Timeseries data from 'SAN FRANCISQUITO C A STANFORD UNIVERSITY CA (USGS 11164500)' (gov_usgs_nwis_11164500) cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com contributor_name=Axiom Data Science contributor_role=processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=water_surface_height_above_reference_datum_above_localstationdatum_qc_agg,river_discharge,water_surface_height_above_reference_datum_above_localstationdatum,water_surface_height_above_reference_datum_above_navd88_qc_agg,z,time,water_surface_height_above_reference_datum_above_navd88,river_discharge_qc_agg&time>=max(time)-3days Easternmost_Easting=-122.189409 featureType=TimeSeries geospatial_lat_max=37.423273 geospatial_lat_min=37.423273 geospatial_lat_units=degrees_north geospatial_lon_max=-122.189409 geospatial_lon_min=-122.189409 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from USGS National Water Information System (NWIS) at id=132103 infoUrl=https://sensors.ioos.us/#metadata/132103/station institution=USGS National Water Information System (NWIS) naming_authority=com.axiomdatascience Northernmost_Northing=37.423273 platform=fixed platform_name=SAN FRANCISQUITO C A STANFORD UNIVERSITY CA (USGS 11164500) platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://waterdata.usgs.gov/monitoring-location/11164500,, sourceUrl=https://waterdata.usgs.gov/monitoring-location/11164500 Southernmost_Northing=37.423273 standard_name_vocabulary=CF Standard Name Table v72 station_id=132103 time_coverage_end=2025-11-15T13:00:00Z time_coverage_start=2023-12-15T03:00:00Z Westernmost_Easting=-122.189409

Experiments with faster dissemination of research began in the 1960s, and in the 1990s first preprint servers emerged and became widely used in Physical Sciences and Economics. Since 2010, more than 30 new preprint servers have emerged and the number of deposited preprints has grown exponentially, with numerous journals now supporting posting of preprints and accepting preprints as submissions for journal peer review and publication. Research on preprints is, however, still scarce.

The goals of this project are: 1) Study preprint policies, submission requirements and addressing of transparency in reporting and research integrity topics of all know preprint servers that allow deposit of preprints to researchers regardless of their institutional affiliation or funding.
2) Study comments deposited on preprint servers’ platforms and social media and their relation to peer review and information exchange. 3) Study differences between preprint version(s) and version of record. 4) Living review of manuscript changes

Team Members (by first name alphabetical order):

Ana Jerončić,1 Gerben ter Riet,2,3 IJsbrand Jan Aalbersberg,4 John P.A. Ioannidis,5-9 Joseph Costello,10 Juan Pablo Alperin,11,12 Lauren A. Maggio,10 Lex Bouter,13,14 Mario Malički,5 Steve Goodman5-7

1 Department of Research in Biomedicine and Health, University of Split School of Medicine, Split, Croatia 2 Urban Vitality Centre of Expertise, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands 3 Amsterdam UMC, University of Amsterdam, Department of Cardiology, Amsterdam, The Netherlands 4 Elsevier, Amsterdam, The Netherlands 5 Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA 6 Department of Medicine, Stanford University School of Medicine, Stanford, California, USA 7 Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA 8 Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA 9 Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, California, USA 10 Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA 11 Scholarly Communications Lab, Simon Fraser University, Vancouver, British Columbia, Canada 12 School of Publishing, Simon Fraser University, Vancouver, British Columbia, Canada 13 Department of Philosophy, Faculty of Humanities, Vrije Universiteit, Amsterdam, The Netherlands 14 Amsterdam UMC, Vrije Universiteit, Department of Epidemiology and Statistics, Amsterdam, The Netherlands

Abstract

Data includes metadata from over 12,500 journals from around the world in Sciences, Social Science and Humanities disciplines. Data are available from 1900 and currently include over 73 million article records and 1.4 billion cited references.

Bulk Data Access

Data access is required to view this section.

The Stanford VLF Group collects data from ground stations located across the globe. There are two principle types of data collected, broadband and narrowband. Broadband data is full waveform data sampled at 100 kHz (frequency range of 300 Hz to 40 kHz). Narrowband data refers to the demodulated amplitude and phase of narrowband VLF transmitters. Both broadband and narrowband data is typically collected on two orthogonal antennas oriented in the North/South and East/West directions. Data availability charts, data summary charts, raw data, and tools for reading and plotting the data are available.

Citation metrics are widely used and misused. We have created a publicly available database of top-cited scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator (c-score). Separate data are shown for career-long and, separately, for single recent year impact. Metrics with and without self-citations and ratio of citations to citing papers are given and data on retracted papers (based on Retraction Watch database) as well as citations to/from retracted papers have been added. Scientists are classified into 22 scientific fields and 174 sub-fields according to the standard Science-Metrix classification. Field- and subfield-specific percentiles are also provided for all scientists with at least 5 papers. Career-long data are updated to end-of-2024 and single recent year data pertain to citations received during calendar year 2024. The selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field. This version (7) is based on the August 1, 2025 snapshot from Scopus, updated to end of citation year 2024. This work uses Scopus data. Calculations were performed using all Scopus author profiles as of August 1, 2025. If an author is not on the list, it is simply because the composite indicator value was not high enough to appear on the list. It does not mean that the author does not do good work. PLEASE ALSO NOTE THAT THE DATABASE HAS BEEN PUBLISHED IN AN ARCHIVAL FORM AND WILL NOT BE CHANGED. The published version reflects Scopus author profiles at the time of calculation. We thus advise authors to ensure that their Scopus profiles are accurate. REQUESTS FOR CORRECIONS OF THE SCOPUS DATA (INCLUDING CORRECTIONS IN AFFILIATIONS) SHOULD NOT BE SENT TO US. They should be sent directly to Scopus, preferably by use of the Scopus to ORCID feedback wizard (https://orcid.scopusfeedback.com/) so that the correct data can be used in any future annual updates of the citation indicator databases. The c-score focuses on impact (citations) rather than productivity (number of publications) and it also incorporates information on co-authorship and author positions (single, first, last author). If you have additional questions, see attached file on FREQUENTLY ASKED QUESTIONS. Finally, we alert users that all citation metrics have limitations and their use should be tempered and judicious. For more reading, we refer to the Leiden manifesto: https://www.nature.com/articles/520429a

Abstract

Title: Cotality Loan-Level Market Analytics (LLMA)

Cotality Loan-Level Market Analytics (LLMA) for primary mortgages contains detailed loan data, including origination, events, performance, forbearance and inferred modification data. This dataset may not be linked or merged with any of the other datasets we have from Cotality.

Formerly known as CoreLogic Loan-Level Market Analytics (LLMA).

Methodology

Cotality sources the Loan-Level Market Analytics data directly from loan servicers. Cotality cleans and augments the contributed records with modeled data. The Data Dictionary indicates which fields are contributed and which are inferred.

The Loan-Level Market Analytics data is aimed at providing lenders, servicers, investors, and advisory firms with the insights they need to make trustworthy assessments and accurate decisions. Stanford Libraries has purchased the Loan-Level Market Analytics data for researchers interested in housing, economics, finance and other topics related to prime and subprime first lien data.

Cotality provided the data to Stanford Libraries as pipe-delimited text files, which we have uploaded to Data Farm (Redivis) for preview, extraction and analysis.

For more information about how the data was prepared for Redivis, please see Cotality 2024 GitLab.

Usage

Per the End User License Agreement, the LLMA Data cannot be commingled (i.e. merged, mixed or combined) with Tax and Deed Data that Stanford University has licensed from Cotality, or other data which includes the same or similar data elements or that can otherwise be used to identify individual persons or loan servicers.

The 2015 major release of Cotality Loan-Level Market Analytics (for primary mortgages) was intended to enhance the Cotality servicing consortium through data quality improvements and integrated analytics. See **Cotality_LLMA_ReleaseNotes.pdf **for more information about these changes.

For more information about included variables, please see Cotality_LLMA_Data_Dictionary.pdf.

**

For more information about how the database was set up, please see LLMA_Download_Guide.pdf.

Bulk Data Access

Data access is required to view this section.

All of the datasets and the below description are quoted from Project - Data-driven prediction of battery cycle life before capacity degradation.

Objective

This dataset, used in our publication “Data-driven prediction of battery cycle life before capacity degradation”, consists of 124 commercial lithium-ion batteries cycled to failure under fast-charging conditions. These lithium-ion phosphate (LFP)/graphite cells, manufactured by A123 Systems (APR18650M1A), were cycled in horizontal cylindrical fixtures on a 48-channel Arbin LBT potentiostat in a forced convection temperature chamber set to 30°C. The cells have a nominal capacity of 1.1 Ah and a nominal voltage of 3.3 V.

The objective of this work is to optimize fast charging for lithium-ion batteries. As such, all cells in this dataset are charged with a one-step or two-step fast-charging policy. This policy has the format “C1(Q1)-C2”, in which C1 and C2 are the first and second constant-current steps, respectively, and Q1 is the state-of-charge (SOC, %) at which the currents switch. The second current step ends at 80% SOC, after which the cells charge at 1C CC-CV. The upper and lower cutoff potentials are 3.6 V and 2.0 V, respectively, which are consistent with the manufacturer’s specifications. These cutoff potentials are fixed for all current steps, including fast charging; after some cycling, the cells may hit the upper cutoff potential during fast charging, leading to significant constant-voltage charging. All cells discharge at 4C.

The dataset is divided into three “batches”, representing approximately 48 cells each. Each batch is defined by a “batch date”, or the date the tests were started. Each batch has a few irregularities, as detailed on the page for each batch.

The data is provided in two formats. For each batch, a MATLAB struct is available. The struct provides a convenient data container in which the data for each cycle is easily accessible. This struct can be loaded in either MATLAB or python (via the h5py package). Pandas dataframes can be generated via the provided code. Additionally, the raw data for each cell is available as a CSV file. Note that the CSV files occasionally exhibit errors in both test time and step time in which the test time resets to zero mid-cycle; these errors are corrected for in the structs.

The temperature measurements are performed by attaching a Type T thermocouple with thermal epoxy (OMEGATHERM 201) and Kapton tape to the exposed cell can after stripping a small section of the plastic insulation. Note that the temperature measurements are not perfectly reliable; the thermal contact between the thermocouple and the cell can may vary substantially, and the thermocouple sometimes loses contact during cycling.

Internal resistance measurements were obtained during charging at 80% SOC by averaging 10 pulses of ±3.6C with a pulse width of 30 ms (2017-05-12 and 2017-06-30) or 33 ms (2018-04-12).

The following repository contains some starter code to load the datasets in either MATLAB or python:

https://github.com/rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation

Low rate data used to generate figure 4:

• 2018-02-20_batchdata_updated_struct_errorcorrect.mat
• 2018-04-03_varcharge_batchdata_updated_struct_errorcorrect.mat

If using this dataset in a publication, please cite: Severson et al. Data-driven prediction of battery cycle life before capacity degradation. Nature Energy volume 4, pages 383–391 (2019).

**Batch - 2017-05-12**
Experimental design
- All cells were cycled with one-step or two-step charging policies. The charging time varies from ~8 to 13.3 minutes (0-80% SOC). There are generally two cells tested per policy, with the exception of 3.6C(80%).
- 1 minute and 1 second rests were placed after reaching 80% SOC during charging and after discharging, respectively.
-We cycle to 80% of nominal capacity (0.88 Ah).
- An initial C/10 cycle was performed in the beginning of each test.
- The cutoff currents for the constant-voltage steps were C/50 for both charge and discharge.
- The pulse width of the IR test is 30 ms.

Experimental notes
- The computer automatically restarted twice. As such, there are some time gaps in the data.
- The temperature control is somewhat inconsistent, leading to variability in the baseline chamber temperature.
- The tests in channels 4 and 8 did not successfully start and thus do not have data.
- The thermocouples for channels 15 and 16 were accidentally switched.

Data notes
- Cycle 1 data is not available in the struct. The sampling rate for this cycle was initially too high, so we excluded it from the data set to create more manageable file sizes.
- The cells in Channels 1, 2, 3, 5, and 6 (3.6C(80%) and 4C(80%) policies) were stopped at the end of this batch and resume...

A dataset consisting of Poincaré embeddings of the FoodEx2 hierarchy, as well as the clustering results obtained with the Poincaré embeddings.

It consists of two folders:

foodex2vec_clusters - where the clustering using the Poincaré embeddings can be seen;

foodex2vec_poincare_embeddings - the actual Poincaré embeddings with different dimensions.

This dataset contains methylation quantitative trait loci (meQTL) results for the following study:

"regionalpcs improve discovery of DNA methylation associations with complex traits"

Tiffany Eulalio*¹, Min Woo Sun¹, Olivier Gevaert¹, Michael D. Greicius², Thomas J. Montine³, Daniel Nachun*‡³, Stephen B. Montgomery*‡^1,3

‡ These authors contributed equally as senior authors

* Corresponding authors: Tiffany Eulalio (eulalio@alumn.stanford.edu), Daniel Nachun (dnachun@stanford.edu), Stephen B. Montgomery (smontgom@stanford.edu)

Author affiliations:

1. Department of Biomedical Data Science, Stanford University, Stanford, CA

2. Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA

3. Department of Pathology, Stanford University, Stanford, CA

Dataset description:

This dataset contains QTL results generated from FastQTL, organized by region type (full gene, gene body, preTSS, and promoters) and summary types (averages and regional principal components).

Contents:

• Parquet tar files: These compressed archives contain output files in Parquet format from FastQTL, split by chromosome. parquet1 includes chromosomes 1-10, and parquet2 includes chromosomes 11-22.
• cis_qtl_summary_stats.csv: Provides summary statistics for each phenotype-variant pair, including effect sizes, p-values, TSS distances, and additional details.
• cis_qtl.signif_pairs.csv: Contains the significant QTL results identified by FastQTL.
• cis_qtls.time_to_run.txt: Reports the running time for FastQTL analysis.
• cis_qtls.cis_qtl.txt.gz: Contains comprehensive results for all cis QTLs.

This dataset is intended to support replication and further exploration of QTL associations across different genomic regions and summary methods.

Scientific Transparency (SciTran) is a software project that has grown out of the Project on Scientific Transparency at Stanford University. At the heart of SciTran is a scientific data management system – SDM – designed to enable and foster reproducible research. SciTran SDM delivers efficient and robust archiving, organization, and sharing of scientific data. We have developed the system around neuroimaging data, but our goal is to build a system that is flexible enough to accomodate all types of scientific data – from paper-and-pencil tests to genomics data. SDM will also allow for the sharing of data and computations between remote sites. SciTran is open-source software, released under the MIT license. Our code is hosted on GitHub. Feel free to try it out or to contribute. Commercial support for SciTran SDM is available through our partners at Flywheel. Check out their demo, if you''d like to give SDM a quick try.

This data set contains archival results from radio science investigations conducted during the Mars Global Surveyor (MGS) mission. Radio measurements were made using the MGS spacecraft and Earth-based stations of the NASA Deep Space Network (DSN). The data set includes high-resolution spherical harmonic models of Mars' gravity field generated by groups at the Jet Propulsion Laboratory and Goddard Space Flight Center, covariance matrices for some models, and maps for some models; these results were derived from raw radio tracking data. Also included are profiles of atmospheric temperature and pressure and ionospheric electron density, derived from phase measurements collected during radio occultations. The data set also includes analyses of transient surface echoes observed close to occultations during the first few years of MGS operations. The atmospheric and surface investigations were conducted by Radio Science Team members at Stanford University. The data set also includes 93 line-of-sight acceleration profiles derived at JPL from radio tracking data collected near periapsis while Mars Global Surveyor was in its Science Phasing Orbit and below its nominal Mapping altitude of 400 km. The data were delivered to PDS in approximately chronological order at the rate of one CD-WO volume (typically 100 MB) every three months.

The booming market for integrated medicine & engineering education is projected to reach $7.68 billion by 2033, growing at an 8% CAGR. This report analyzes market trends, key players (MIT, Stanford, etc.), and regional insights, covering biomedical engineering, health informatics, and more. Explore lucrative investment opportunities in this rapidly expanding sector.

Most real-world data is stored in relational form. In contrast, most statistical learning methods work with “flat” data representations, forcing us to convert our data into a form that loses much of the relational structure. The recently introduced framework of Probabilistic Relational Models (PRMs) allows us to represent probabilistic models over multiple entities that utilize the relations between them. However, for extremely large domains it may be impossible to represent every object and every relation in the domain explicitly. We propose representing the domain as an Averaged PRM using only “schema level” statistical information about the objects and relations, and present an approximation algorithm for reasoning about the domain with only this information. We present experimental results showing that interesting inferences can be made about extremely large domains, with a running time that does not depend on the number of objects.

This dataset is the result of a project to support policy making by providing insights on the availability and composition of education offer in four key digital domains: artificial intelligence, high performance computing, cybersecurity, and data science. Following a text mining methodology that captures the inclusion of advanced digital technologies in the programmes’ syllabus, we monitor the availability of masters’ programmes, bachelor’s programmes and short professional courses and study their characteristics. These include the scope or depth with which the digital content is taught (classified into broad or specialised), education fields in which digital technologies are embedded (e.g., Information and communication technologies, Business, administration and law), and the content areas covered by the programmes (e.g. robotics, machine learning). Also, we consider the overlap between the four domains, to identify complementarities and synergies in the academic offer of advanced digital technologies. The dataset covers yearly data, starting from the academic year 2019-2020 and ending in academic year 2023-24 (and will not be further updated). In order to provide comparison with other competing economies, the dataset covers the EU and its Member States plus six additional countries: the United Kingdom, Norway, Switzerland, Canada, the United States, and Australia. Results of the study have been used as reference in the European Artificial Intelligence Strategy, the White Paper on Artificial Intelligence – a European approach to excellence and trust, in the Stanford University’s Artificial Intelligence Index Report 2019 and 2021. These data have substantiated the assessment of the national Recovery and Resilience plans, and are used as input for the Digital Resilience Dashboard, among others.

Abstract

Web of Science languages table This dataset was created on 2019-11-05.