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. 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-2022 and single recent year data pertain to citations received during calendar year 2022. 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 (6) is based on the October 1, 2023 snapshot from Scopus, updated to end of citation year 2022. This work uses Scopus data provided by Elsevier through ICSR Lab (https://www.elsevier.com/icsr/icsrlab). Calculations were performed using all Scopus author profiles as of October 1, 2023. 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, please read the 3 associated PLoS Biology papers that explain the development, validation and use of these metrics and databases. (https://doi.org/10.1371/journal.pbio.1002501, https://doi.org/10.1371/journal.pbio.3000384 and https://doi.org/10.1371/journal.pbio.3000918).

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

The CPU DB, a complete database of processors for researchers and hobbyists alike.

You can browse the processor database by manufacturer, processor family, code name, or microarchitecture.

History For years Stanford University's VLSI Research Group collected and maintained a unique spreadsheet of commercial microprocessor characteristics. Over the years these data points were useful in a variety of research talks and publications. Unfortunately, the lack of a central repository for this database made it difficult to both share the data with everyone, and enhance it with outside contributions... until now. Welcome to CPUDB.

What's the big deal? There are certainly a number of existing useful online resources for microprocessor information. To name a few,

• Intel has detailed specifications for many products,
• SPEC has measured performance characteristics for many processors,
• CPUWorld contains diephotos, specifications, and benchmarks
• Wikipedia is a great source of architecture/specification information.

CPUDB seeks to unify all of this information in a research-friendly, community-reviewed database. Additionally, it contains process technology information for each microprocessor, allowing for technology normalization across designs.

• Visualize interesting trends
• Browse processor data online
• Download the entire database in convenient CSV files and begin your own analysis

Contributing As with any large set of data, there are a number of holes (and possibly a few erroneous entries). We encourage anyone to contribute modifications to the database, or even to suggest new data fields they would find useful.

Citations Database Authors This site was created by the following people.

Andrew Danowitz
Kyle Kelley
James Mao
John P. Stevenson
Mark Horowitz
Omid Azizi
John S. Brunhaver II
Ron Ho
Stephen Richardson
Ofer Shacham
Alex Solomatnikov

Database Acknowledgments A special thanks to the following contributors:

Chris Batten
Ron Ho
Francois Labonte
Craig Teegarden

Source & License: http://cpudb.stanford.edu

The Fundamental Kinetic Database Utilizing Shock Tube Measurements Database summarizes the published shock tube experimental work performed under the supervision of Prof. Ronald K. Hanson of the Mechanical Engineering Department at Stanford University. The database covers the years from 1974 to 2013 inclusively. The database is divided into three types of data: ignition delay times, species time-history measurements, and reaction rate measurements. Volumes are in DOCX format and data tables in the volumes can be easily cut and pasted into separate user spread sheets. Volume 1 of the Fundamental Kinetic Database Utilizing Shock Tube Measurements includes a summary of the ignition delay time data measured and published by the Shock Tube Group in the Mechanical Engineering Department of Stanford University. The cut-off date for inclusion into this volume was January 2005. Volume 2 includes a summary of the species concentration time-histories. The cut-off date for inclusion in this volume was December 2005. Some of the figures embedded in this DOCX file can be opened using ORIGIN software. The data in this volume is available in tabular form in the accompanying ZIP file or in this volume. Volume 3 includes a summary of the reaction rate measurements. The cut-off date for inclusion in this volume was January 2009. Volume 4 includes a summary of the ignition delay time data. The start data for inclusion into this volume is January 2005 (the cutoff date for Volume 1) and the cutoff date is January 2014. Volume 5 includes a summary of the species concentration time-histories. The cut-off date for inclusion in this volume was January 2014. The format of this volume differs from that of Volume 2 in that we have not included the data files. Some of this data is available in the relevant papers and some of the data files may be accessible by contacting Dr. David Davidson at dfd@stanford.edu. Volume 6 includes a summary of the reaction rates. The cut-off date for inclusion in this volume was January 2014. Volumes 7, 8 and 9 continue this summary (of Ignition Delay Times, Speciation and Rate Measurements respectively) up to June 2019. Full versions of the data bases can be found in the FKDUSTM Full Database files.

Diagnosis and procedure groups (DX_PR_GRPS) file showing diagnosis and procedure groups linkable to Inpatient Core File. Unit of observation: discharge-level. (2016-2017 not included)

Context

Dataset from Stanford's DeepSolar project. You can read more about the project here: http://web.stanford.edu/group/deepsolar/home

Content

Data was downloaded from here: http://web.stanford.edu/group/deepsolar/deepsolar_tract.csv GitHub repository with all the code can be found here: https://github.com/wangzhecheng/DeepSolar

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 2nd,2023. TMAD stores raw and processed data from Tissue Microarray experiments along with their corresponding stained tissue images. In addition, TMAD provides methods for data retrieval, grouping of data, analysis and visualization as well as export to standard formats. Researchers at the Stanford University School of Medicine and their collaborators worldwide have constructed many tissue microarrays for use in basic research.

Diagnosis and Procedure Groups Files: is an encounter-level file that contains data elements from AHRQ software tools. They are designed to facilitate the use of the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnostic and procedure information in the HCUP databases. The unit of observation is an outpatient record. The HCUP unique record identifier (KEY) provides the linkage between the Core files and the Diagnosis and Procedure Groups files.

Background: Stanford type A aortic dissection (ATAAD) is a common life-threatening event in the aorta. Recently, immune disorder has been linked to the risk factors that cause ATAAD at the molecular level. However, the specific immune-related gene signature during the progression is unclear.Methods: The GSE52093 and GSE98770 datasets related to ATAAD from the Gene Expression Omnibus (GEO) database were acquired. The immune gene expression levels were analyzed by single sample gene set enrichment analysis (ssGSEA). The correlations between gene networks and immune scores were determined by weighted gene correlation network analysis (WGCNA). The different immune subgroups were finally divided by consensus clustering. The differentially expressed genes (DEGs) were identified and subsequent functional enrichment analyses were conducted. The hub genes were identified by protein–protein interaction (PPI) network and functional similarities analyses. The immune cell infiltration proportion was determined by the CIBERSORT algorithm.Results: According to the ssGSEA results, the 13 ATAAD samples from the GEO database were divided into high- and low-immune subgroups according to the ssGSEA, WGCNA, and consensus clustering analysis results. Sixty-eight immune-related DEGs (IRDEGs) between the two subgroups were enriched in inflammatory-immune response biological processes, including leukocyte cell–cell adhesion, mononuclear cell migration, and myeloid leukocyte migration. Among these IRDEGs, 8 genes (CXCR4, LYN, CCL19, CCL3L3, SELL, F11R, DPP4, and VAV3) were identified as hub genes that represented immune-related signatures in ATAAD after the PPI and functional similarities analyses. The proportions of infiltrating CD8 T cells and M1 macrophages were significantly higher in ATAAD patients in the immune-high group than the immune-low group.Conclusion: Eight immune-related genes were identified as hub genes representing potential biomarkers and therapeutic targets linked to the immune response in ATAAD patients.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on December 17, 2021. Database to store, annotate, view, analyze and share microarray data. It provides registered users access to their own data, provides users access to public data, and tools with which to analyze those data, to any public user anywhere in the world. The GenePattern software package has been incorporated directly into SMD, providing access to many new analysis tools, as well as a plug-in architecture that allows users to directly integrate and share additional tools through SMD. This extension is available with the SMD source code that is fully and freely available to others under an Open Source license, enabling other groups to create a local installation of SMD with an enriched data analysis capability. SMD search options allow the user to Search By Experiments, Search By Datasets, or Search By Gene Names. Web services are provided using common standards, such as Simple Object Access Protocol (SOAP). This enables both local and remote researchers to connect to an installation of the database and retrieve data using pre-defined methods, without needing to resort to use of a web browser.

The emergence and spread of new HIV-1 variants pose a challenge for the effectiveness of antiretrovirals (ARV) targeting Pol proteins. During viral evolution, non-synonymous mutations have fixed along the viral genome, leading to amino acid (aa) changes that can be variant-specific (V-markers). Those V-markers fixed in positions associated with drug resistance mutations (DRM), or R-markers, can impact drug susceptibility and resistance pathways. All available HIV-1 Pol sequences from ARV-naïve subjects were downloaded from the United States Los Alamos HIV Sequence Database, selecting 59,733 protease (PR), 6,437 retrotranscriptase (RT), and 6,059 integrase (IN) complete sequences ascribed to the four HIV-1 groups and group M subtypes and circulating recombinant forms (CRFs). Using a bioinformatics tool developed in our laboratory (EpiMolBio), we inferred the consensus sequences for each Pol protein and HIV-1 variant to analyze the aa conservation in Pol. We analyzed the Wu–Kabat protein variability coefficient (WK) in PR, RT, and IN group M to study the susceptibility of each site to evolutionary replacements. We identified as V-markers the variant-specific aa changes present in >75% of the sequences in variants with >5 available sequences, considering R-markers those V-markers that corresponded to DRM according to the IAS-USA2019 and Stanford-Database 9.0. The mean aa conservation of HIV-1 and group M consensus was 82.60%/93.11% in PR, 88.81%/94.07% in RT, and 90.98%/96.02% in IN. The median group M WK was 10 in PR, 4 in RT, and 5 in IN. The residues involved in binding or catalytic sites showed a variability

"ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. The project has been instrumental in advancing computer vision and deep learning research. The data is available for free to researchers for non-commercial use." (https://www.image-net.org/index.php)

I do not hold any copyright to this dataset. This data is just a re-distribution of the data Imagenet.org shared on Kaggle. Please note that some of the ImageNet1K images are under copyright.

This version of the data is directly sourced from Kaggle, excluding the bounding box annotations. Therefore, only images and class labels are included.

All images are resized to 256 x 256.

Integer labels are assigned after ordering the class names alphabetically.

Please note that anyone using this data abides by the original terms: ``` RESEARCHER_FULLNAME has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:

1. Researcher shall use the Database only for non-commercial research and educational purposes.
2. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.
4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
5. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
7. The law of the State of New Jersey shall apply to all disputes under this agreement.

The images are processed using [TPU VM](https://cloud.google.com/tpu/docs/users-guide-tpu-vm) via the support of Google's [TPU Research Cloud](https://sites.research.google/trc/about/).

Dataset Card for "imdb"

  Dataset Summary

Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.

  Supported Tasks and Leaderboards

More Information Needed

  Languages

More Information Needed

  Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/imdb.

This point shapefile contains the names and locations of main road roundabouts in Hungary. The road network data come from DTA-50 1.0 database (MoD Mapping Company) and it is updated by ortophotos and by fieldwork (differential GPS).The database update is continuous.

This point shapefile contains the locations of automotive rest areas near main raods in Hungary. The road network data come from DTA-50 1.0 database (MoD Mapping Company) and it is updated by ortophotos and by fieldwork (differential GPS).The database update is continuous.

This point shapefile contains names and locations of highway and motor road junctions in Hungary. The road network data come from DTA-50 1.0 database (MoD Mapping Company) and it is updated by ortophotos and by fieldwork (differential GPS).The database update is continuous.

The table IL-Demographic-2025-07-22 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 8464532 rows across 698 variables.

The table FL-Demographic-2025-06-10 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 14938427 rows across 698 variables.

The table OK-Demographic-2025-08-13 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 2224820 rows across 698 variables.