Timeseries data from 'SAN FRANCISQUITO C A STANFORD UNIVERSITY CA' (ism-cencoos-gov_usgs_waterdata_1-45) _NCProperties=version=2,netcdf=4.7.4,hdf5=1.10.6 cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com,feedback@axiomdatascience.com contributor_name=Axiom Data Science,Axiom Data Science contributor_role=contributor,processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com,https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3 defaultDataQuery=height_geoid_local_station_datum_qc_agg,river_discharge,z,time,height_geoid_local_station_datum,river_discharge_qc_agg&time>=max(time)-3days Easternmost_Easting=-122.18941 featureType=TimeSeries geospatial_lat_max=37.423273 geospatial_lat_min=37.423273 geospatial_lat_units=degrees_north geospatial_lon_max=-122.18941 geospatial_lon_min=-122.18941 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 Central & Northern California Ocean Observing System (CeNCOOS) at http://erddap.cencoos.org/erddap/tabledap/gov_usgs_waterdata_11164500 id=106633 infoUrl=https://sensors.ioos.us/#metadata/106633/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 platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=http://erddap.cencoos.org/erddap/tabledap/gov_usgs_waterdata_11164500,http://erddap.cencoos.org/erddap/tabledap/gov_usgs_waterdata_11164500, sourceUrl=http://erddap.cencoos.org/erddap/tabledap/gov_usgs_waterdata_11164500 Southernmost_Northing=37.423273 standard_name_vocabulary=CF Standard Name Table v72 station_id=106633 time_coverage_end=2023-09-23T05:45:00Z Westernmost_Easting=-122.18941

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.

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 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 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

The Chinese National Foundation Project Data was released on the Chinese Open Data Platform (CnOpenData). The dataset includes information about all Chinese government funded research projects in the social sciences and humanities from 1991-2019.

Methodology

The raw data were wrangled for inclusion in Data Farm. For more information, please see CnOpenData GitLab.

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.

Timeseries data from '158 - Cabrillo Point Nearshore, CA (46240)' (edu_ucsd_cdip_158) _NCProperties=version=2,netcdf=4.7.4,hdf5=1.10.6 cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=None,mailto:hmsinformation@lists.stanford.edu,mailto:hmsinformation@lists.stanford.edu,,webmaster.ndbc@noaa.gov,feedback@axiomdatascience.com contributor_name=U.S. Army Corps of Engineers (USACE),Hopkins Marine Station, Stanford University,Hopkins Marine Station, Stanford University,World Meteorological Organization (WMO),NOAA National Data Buoy Center (NDBC),Axiom Data Science contributor_role=sponsor,collaborator,sponsor,contributor,contributor,processor contributor_role_vocabulary=NERC contributor_url=http://www.usace.army.mil/,https://hopkinsmarinestation.stanford.edu/,https://hopkinsmarinestation.stanford.edu/,https://wmo.int/,https://www.ndbc.noaa.gov/,https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3 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=2025-03-12T15:58:20Z time_coverage_start=2008-12-02T23:14:27Z Westernmost_Easting=-121.9071 wmo_platform_code=46240

Snapshot img

Alternative Non Credential Courses Market Size 2024-2028

The alternative non credential courses market size is forecast to increase by USD 15.38 billion at a CAGR of 23.31% between 2023 and 2028. The growth of m-learning is driven by the rising prominence of alternative non-credentialing, the availability of open educational resources, and the emergence of virtual schools. These trends cater to the increasing demand for flexible and accessible education options. However, the market faces challenges such as inadequate cybersecurity measures, which raise concerns over data privacy and safety. Additionally, traditional degree programs pose a competitive threat, as they continue to be valued for their established credibility and recognition. Furthermore, limited demand from developing economies hampers market expansion, as these regions often lack the necessary infrastructure and resources to support m-learning effectively. Addressing these challenges is crucial for the sustained growth and broader adoption of m-learning platforms globally. The report includes historic market data from 2018 - 2022.

Overview of the Market

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Key Companies & Market Insights

Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.

Boston University: The company offers alternative non credential courses such as MS in Applied Business Analytics, and MS in Supply Chain Management.

The market report also includes detailed analyses of the competitive landscape of the market and information about 15 market companies, including:

Blue Mountain Community College
Boston University
Colorado State University
Columbia University
Elmira College
Harvard University
Michigan Technological University
Montgomery College
New York Institute of Finance Inc.
New York University
Southern New Hampshire University
Stanford University
Temple University
Tennessee Tech
University of Arkansas
University of Cape Town
University of Illinois
University of Pennsylvania
University of Southern Indiana
University System of New Hampshire
Wake Technical Community College
Yale University

Qualitative and quantitative analysis of companies has been conducted to help clients understand the wider business environment as well as the strengths and weaknesses of key market research and growth and players. Data is qualitatively analyzed to categorize companies as pure play, category-focused, industry-focused, and diversified; it is quantitatively analyzed to categorize companies as dominant, leading, strong, tentative, and weak.

Regional Analysis

North America is estimated to contribute 58% to the growth of the global market during the forecast period. Technavio's analysts have provided extensive insight into market forecasting, detailing the regional trends and drivers influencing the market's trajectory throughout the forecast years.

View the Bestselling Market Report Instantly

M-learning refers to mobile learning, enabling students to access educational content on their smartphones or tablets. Open educational resources are freely available materials, often digital, for self-directed learning. Virtual schools deliver instruction online, while internet-enabled devices facilitate remote access to educational content. Non-traditional courses in this market include digital badges, micro-credentials, workshops, bootcamps, and industry certifications. Publishers play a crucial role in producing and promoting these courses, while vendor selection methodologies ensure that learners choose high-quality offerings. Qualitative and quantitative research are essential for evaluating the effectiveness and value of these courses. Skill acquisition is a primary focus of the Market, with practical skills being a significant concern for employers.

Job requirements continue to evolve, necessitating reskilling and upskilling. Training providers offer various solutions to meet these demands, including hybrid and blended learning models. Skill assessment, adaptive learning paths, and blockchain technology facilitate credential verification and lifelong learning. Information technology sectors, such as data science and digital marketing, are major contributors to the Alternative Non-Credential Courses Market. Collaborations between educational institutions, industry partners, and edtech companies are driving standardization, validity, and comparability in these offerings. Employers increasingly adopt skills-based hiring practices, making it essential for learners to acquire relevant, in-demand skills. Economic uncertainties further emphasize the importance of adaptability and continuous learning.

Market Segmentation

The non-institutional segment is estimated to witness significant growth during the forecast period. There ar

This data set consists of several tables and supporting documentation from final analysis of the Voyager 2 radio occultation by Triton. The data set is based on a Ph.D. dissertation by Eric M. Gurrola of Stanford University [GURROLA1995]. The tabulated data were derived from raw radio science observations, which are being archived separately. General principles for conducting these types of experiments have been described by [TYLER1987] results of the Triton analysis were published by [TYLERETAL1989].

This dataset contains metadata for methane flux sites in Version 1.0 of FLUXNET-CH4. The dataset also has seasonality parameters for select freshwater wetlands, which were extracted from the raw datasets published at https://fluxnet.org/data/fluxnet-ch4-community-product/. These data are used to analyze global methane flux seasonality patterns in the paper "FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands" by Delwiche et al.

This data set is derived from a project that seeks to understand the status and resilience of the key communities of coral reef fishes that underpin the ecology of the British Indian Ocean Territory (Chagos Archipelago) Marine Protected Area. It is funded by the Bertarelli Program in Marine Science (Bertarelli Foundation) and represents a collaboration between AIMS, Lancaster University, and Stanford University. The data are collected on annual field expeditions to the Chagos Archipelago. The data are primarily fish specimen biological characteristics (collected by spearfishing), whereby otoliths from specimens are actively being used for biochronological reconstructions of growth histories. The research program also used a series of shallow water (<10m) BRUVS deployments in March 2019 to obtain a comprehensive spatial survey of sharks and other predatory fish species.

The SDO/AIA Level 1.5 Synoptic FITS Data.

The AIA Synoptic FITS data are reduced to 1024x1024 2.4 arc-sec pixels by summing with pixel value in DN per original 0.6 arc-sec pixel. The data cadence is 2 minutes. These images are produced approximately 7 days after T_OBS since they are derived from definitive level 1 data.

The SDO/AIA Level 1 FITS Data.

The Atmospheric Imaging Assembly (AIA) onboard Solar Dynamics Observatory (SDO) focuses on the evolution of the magnetic environment in the Sun’s atmosphere, and its interaction with embedded and surrounding plasma. The AIA investigation covers a broad range of science objectives that focus on five core research themes that both advance solar and heliospheric physics in general and provide advanced warning of coronal and inner-heliospheric disturbances of interest to the Living With a Star (LWS) program, i.e., global change, space weather, human exploration of space, and technological infrastructure in space and on Earth.

AIA provides the following essential capabilities: i) A view of the entire Sun in 4k x 4k resolution (pixel size of 0.6 arcseconds), with full thermal coverage of the corona; ii) A high signal-to-noise ratio for two- to three-second exposures that reaches 100 in quiescent conditions for the low-temperature coronal-imaging channels and during flaring in the higher-temperature channels, with a dynamic range of up to 10,000; iii) Essentially uninterrupted viewing for months at a temporal cadence of 12 seconds in seven extreme UV (EUV) band passes (94, 131, 171, 193, 211, 304 and 335 Å), and 24 seconds for two UV channels (1600 and 1700 Å) band passes; iv) In special observing modes, AIA can capture images of the Sun at higher cadence while keeping within the instrument allocated telemetry by using a subset of bandpass channels, and/or using a crop table.

U-Pb isotopic data from zircon measured using the SHRIMP-RG ion microprobe at Stanford University, CA. These data are used to calculate absolute ages of the samples from which the zircons were extracted. These samples are shown on the Poncha Pass map being submitted for publication (Minor et al.)

The HMI continuum data refers to map of the continuum intensity of the solar spectrum in the region of the Fe I absorption line at 6173 Å on the surface of the sun.

HMI samples the Fe I absorption line at 6173.3 Å at six points. Assuming that the "pure" solar Fe I line profile is a Gaussian, and the HMI filter filter transmission profiles are delta functions, the first and second Fourier coefficients of the of the Fe I line can be calculated and an estimate of the Doppler Velocity can be made. An estimate (proxy) for the continuum intensity Ic is obtained by "reconstructing" the solar line from the estimates of the Doppler shift λo, the linewidth σ, and the linedepth Id. More details can be found at http://jsoc.stanford.edu/relevant_papers/observables.pdf.

In 2023, the top ranked full-time business school in the United States was the Stanford Graduate School of Business in Stanford, California, where tuition costs students a total of 80,613 U.S. dollars.

The Gastroparesis Registry (GpR) is an observational study to clarify the epidemiology, natural history, clinical course, and other outcomes of gastroparesis.

SHARP Near Real Time data in CCD coordinates.

SHARP stands for Space-weather HMI Active Region Patch. A SHARP is a DRMS series that contains (1) various space-weather quantities calculated from the photospheric vector magnetogram data and stored as FITS header keywords, and (2) 31 data segments (described in detail below), including each component of the vector magnetic field, the line-of-sight magnetic field, continuum intensity, doppler velocity, error maps and bitmaps. The data segments are not full-disk; rather, they are partial-disk, automatically-identified active region patches. SHARPs are calculated every 12 minutes. Often, there is more than one active region on the solar disk at any given time. Thus, SHARPs are indexed by two prime keys: time, T_REC, and HMI Active Region Patch Number, HARPNUM.

Note that HARPNUMs in the nrt and defnitive data series will be different.