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

John Ioannidis and co-authors [1] created a publicly available database of top-cited scientists in the world. This database, intended to address the misuse of citation metrics, has generated a lot of interest among the scientific community, institutions, and media. Many institutions used this as a yardstick to assess the quality of researchers. At the same time, some people look at this list with skepticism citing problems with the methodology used. Two separate databases are created based on career-long and, single recent year impact. This database is created using Scopus data from Elsevier[1-3]. The Scientists included in this database are classified into 22 scientific fields and 174 sub-fields. The parameters considered for this analysis are total citations from 1996 to 2022 (nc9622), h index in 2022 (h22), c-score, and world rank based on c-score (Rank ns). Citations without self-cites are considered in all cases (indicated as ns). In the case of a single-year case, citations during 2022 (nc2222) instead of Nc9622 are considered.

To evaluate the robustness of c-score-based ranking, I have done a detailed analysis of the matrix parameters of the last 25 years (1998-2022) of Nobel laureates of Physics, chemistry, and medicine, and compared them with the top 100 rank holders in the list. The latest career-long and single-year-based databases (2022) were used for this analysis. The details of the analysis are presented below: Though the article says 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, the actual career-based ranking list has 204644 names[1]. The single-year database contains 210199 names. So, the list published contains ~ the top 4% of scientists. In the career-based rank list, for the person with the lowest rank of 4809825, the nc9622, h22, and c-score were 41, 3, and 1.3632, respectively. Whereas for the person with the No.1 rank in the list, the nc9622, h22, and c-score were 345061, 264, and 5.5927, respectively. Three people on the list had less than 100 citations during 96-2022, 1155 people had an h22 less than 10, and 6 people had a C-score less than 2.
In the single year-based rank list, for the person with the lowest rank (6547764), the nc2222, h22, and c-score were 1, 1, and 0. 6, respectively. Whereas for the person with the No.1 rank, the nc9622, h22, and c-score were 34582, 68, and 5.3368, respectively. 4463 people on the list had less than 100 citations in 2022, 71512 people had an h22 less than 10, and 313 people had a C-score less than 2. The entry of many authors having single digit H index and a very meager total number of citations indicates serious shortcomings of the c-score-based ranking methodology. These results indicate shortcomings in the ranking methodology.

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.

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=2025-08-06T03:00:00Z time_coverage_start=2023-12-15T03:00:00Z Westernmost_Easting=-122.189409

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

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

Available registries

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

phsdatacore@stanford.edu for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Documentation

Denmark has hundreds of linkable population-based registries and databases available for research. These include administrative, health, and clinical quality databases linkable using civil registration numbers, part of the Danish Civil Registration System established in 1968, and taken together collectively represent nearly complete population coverage and follow-up, universal healthcare access, and deterministic linkage. An understanding of the state, regional and municipal-levels of the healthcare system is critical to successful research using these data, but the opportunities for working with a dynamic cohort with long-term follow-up are extensive across disciplines.

To date, the Stanford-Aarhus partnership has resulted in deep investigations of associations related to dementia, stress disorders and mental health, healthcare utilization and cost blooms, neighborhood level indicators of later outcomes, among others. Several cohorts have been identified as priorities for future research collaboration, including the Danish Labor Force Survey (LFS), the aging population, and pediatric data.

In collaboration with PHS, researchers can present a proposal to determine if a project of interest is aligned with the research agenda and mission of the Stanford-Aarhus partnership. Once initiated, collaborators work hand-in-hand with an Aarhus data scientist who manages the data and conducts analyses, supports the drafting of manuscripts and presentations.

If you are interested in working with this collaborator, please contact Stanford Population Health Sciences Data Core team at phsdatacore@stanford.edu

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 and a single set of power spectra acquired during a quasi-specular bistatic radar experiment in 2000. 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 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.

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 archive contains the GWAS integration results for the 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:

The GWAS integration includes colocalization and PTWAS analyses, organized within subdirectories in the main tar file.

Colocalization results: These include the output from FastENLOC for each cell type (astrocytes, endothelial cells, neurons, oligodendrocytes/OPCs and bulk) across different region types (full gene, gene body, preTSS, and promoters). Results are provided separately for averages (avgs) and regionalpcs (pcs).

PTWAS results: PTWAS output is similarly organized by cell types, region type, and summary types, providing output from the PTWAS method.

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.

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.

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.

The global market for integrated medicine and engineering education is experiencing robust growth, projected at a compound annual growth rate (CAGR) of 8% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing demand for healthcare professionals skilled in both medical science and engineering principles is fueling the need for specialized programs. Advances in medical technology, such as robotics, AI, and bioprinting, require professionals with a holistic understanding of both engineering design and its biological applications. Furthermore, the integration of data science and informatics into healthcare necessitates professionals capable of managing and interpreting vast datasets for improved patient care and medical research. The growing aging population, coupled with a rising prevalence of chronic diseases, further intensifies the demand for such skilled individuals. The market is segmented by subject areas including biomedical engineering, health informatics, clinical engineering, and robotics in healthcare; and by course levels, encompassing undergraduate, graduate, and certificate programs. Major players are established universities globally, each with its unique strengths and competitive strategies, focusing on program innovation, industry partnerships, and attracting top faculty. Geographic distribution reveals strong market presence in North America and Europe, owing to established research infrastructure and well-funded educational institutions. However, the APAC region, particularly India and China, shows significant growth potential driven by rapid economic development and increased investment in healthcare infrastructure and education. The market's future growth hinges on sustained investment in research and development, industry-academia collaborations, and government initiatives promoting STEM education. Challenges include the high cost of specialized equipment and training, along with the need for standardized curriculum and accreditation across different regions and institutions. Ultimately, the market's trajectory reflects a critical need for a multidisciplinary approach to address evolving healthcare challenges, presenting substantial opportunities for educational institutions and technology providers alike.

SPEED+ is the next-generation dataset for spacecraft pose estimation with specific emphasis on the robustness of Machine Learning (ML) models across the domain gap. Similar to its predecessor, SPEED+ consists of images of the Tango spacecraft from the PRISMA mission. SPEED+ consists of three different domains of imageries from two distinct sources. The first source is the OpenGL-based Optical Stimulator camera emulator software of Stanford’s Space Rendezvous Laboratory (SLAB), which is used to create the synthetic domain comprising 59,960 synthetic images. The labeled synthetic domain is split into 80:20 train/validation sets and is intended to be the main source of training of an ML model. The second source is the Testbed for Rendezvous and Optical Navigation (TRON) facility at SLAB, which is used to generate two simulated Hardware-In-the-Loop (HIL) domains with different sources of illumination: lightbox and sunlamp. Specifically, these two domains are constructed using realistic illumination conditions using lightboxes with diffuser plates for albedo simulation and a sun lamp to mimic direct high-intensity homogeneous light from the Sun. Compared to synthetic imagery, they capture corner cases, stray lights, shadowing, and visual effects in general which are not easy to obtain through computer graphics. The lightbox and sunlamp domains are unlabeled and thus intended mainly for testing, representing a typical scenario in developing a spaceborne ML model in which the labeled images from the target space domain are not available prior to deployment. SPEED+ is made publicly available to the aerospace community and beyond as part of the second international Satellite Pose Estimation Competition (SPEC2021) co-hosted by SLAB and the Advanced Concepts Team (ACT) of the European Space Agency.

The construction of the TRON testbed was partly funded by the U.S. Air Force Office of Scientific Research (AFOSR) through the Defense University Research Instrumentation Program (DURIP) contract FA9550-18-1-0492, titled High-Fidelity Verification and Validation of Spaceborne Vision-Based Navigation. The SPEED+ dataset is created using the TRON testbed by SLAB at Stanford University. The post-processing of the raw images is reviewed by ACT to meet the quality requirement of SPEC2021.

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=,mailto:hmsinformation@lists.stanford.edu,None,webmaster.ndbc@noaa.gov,mailto:hmsinformation@lists.stanford.edu,feedback@axiomdatascience.com contributor_name=World Meteorological Organization (WMO),Hopkins Marine Station, Stanford University,U.S. Army Corps of Engineers (USACE),NOAA National Data Buoy Center (NDBC),Hopkins Marine Station, Stanford University,Axiom Data Science contributor_role=contributor,collaborator,sponsor,contributor,sponsor,processor contributor_role_vocabulary=NERC contributor_url=https://wmo.int/,https://hopkinsmarinestation.stanford.edu/,http://www.usace.army.mil/,https://www.ndbc.noaa.gov/,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=2025-08-03T10:58:20Z time_coverage_start=2008-12-02T23:14:27Z Westernmost_Easting=-121.9071 wmo_platform_code=46240

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.

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.

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.