This dataset supports the Biomarker: Gastrointestinal Viruses page on the Tempe Wastewater BioIntel Program site.Wastewater collection areas are comprised of merged sewage drainage basins that flow to a shared testing location for the Tempe Wastewater BioIntel Program. The wastewater collection areas represent a geographic area for which virus activity is tested. People infected with a virus excrete the virus in their feces in a process known as “shedding”. The municipal wastewater treatment system (sewage system) collects and aggregates these bathroom contributions across communities. The process begins at sampling site where, over a period of 24 hours, a wastewater sample is collected along the sewer line. After the sample is acquired, it is immediately transferred to a lab where scientists prepare the sample. The laboratory analysis seeks to determine if there is a signal (or detectable presence) of the biomarker in the wastewater. Please see the Tempe Wastewater BioIntel Program site for more information on the wastewater testing process at https://wastewater.tempe.gov/. About the data: These data illustrate a trend of the signal of the weekly average or weekly results of Tempe wastewater biomarker groups. The dashboard and collection area map do not depict the number of individuals infected. Each collection area includes at least one sampling location, which collects wastewater from across the collection area. It does not reflect the specific location where the deposit occurs. While testing can successfully quantify the results, research has not yet determined the relationship between these values and the number of people who are contributing to the signals. The influence of this data on community health decisions in the future is unknown. Data collection is being used to depict overall weekly trends and should not be interpreted without a holistic assessment of public health data. The purpose of this weekly data is to support research as well as to identify overall trends of the genome copies in each liter of wastewater per collection area. We share this information with the public with the disclaimer that only the future can tell how much “diagnostic value” we can and should attribute to the numeric measurements we obtain from the sewer. However, we know what we measure is real and we share that info with our community. Data are shared as the testing results become available. As results may not be released at the same time, testing results for each area may not yet be seen for a given day or week. The dashboard presents the weekly averages. Data are collected from 2-7 days per week. For Collection Area 1, Tempe's wastewater co-mingles with wastewater from a regional sewage line. Tempe's sewage makes up most of Collection Area 1 samples. For Collection Area 3, Tempe's wastewater co-mingles with wastewater from a regional sewage line. For analysis and reporting, Tempe’s wastewater is separated from regional sewage. Week start date represents the starting date of the testing week, which starts on Mondays and ends on Sundays. Additional Information:Source: The Translational Genomics Research Institute (TGen), part of City of Hope, is an Arizona-based, nonprofit medical research institute.Contact: Kimberly SoteloContact email: kimberly_sotelo@tempe.govPreparation Method: Initial values are provided by TGen. Tempe makes additional calculations to determine the weekly averages or weekly results for each biomarker.Publish Frequency: Weekly or as data becomes availablePublish Method: ManualData Dictionary

iitd-gi/gic0 dataset hosted on Hugging Face and contributed by the HF Datasets community

SushantGautam/ImageCLEFmed-MEDVQA-GI-2024-Dev dataset hosted on Hugging Face and contributed by the HF Datasets community

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The Ring Scan Endoscopic Ultrasound (EUS) System market is experiencing robust growth, driven by the increasing prevalence of gastrointestinal cancers and the rising demand for minimally invasive diagnostic and therapeutic procedures. The market's expansion is fueled by technological advancements leading to improved image quality, smaller probe sizes, and enhanced functionalities. These advancements facilitate earlier and more accurate diagnosis, resulting in improved patient outcomes and reduced hospital stays. Furthermore, the growing adoption of EUS in various applications, including gastroenterology, pulmonology, and hepatology, contributes significantly to market expansion. Hospitals and clinics are the primary users, with a projected higher growth rate in the forward-viewing ring scan systems segment, owing to their superior visualization capabilities. While the market faces restraints such as high equipment costs and the need for skilled professionals, the overall growth trajectory remains positive, fueled by increasing healthcare spending, particularly in developing economies. Key players like Olympus, Stryker, and Pentax Medical are actively involved in research and development, introducing innovative products and expanding their market presence through strategic partnerships and acquisitions. The competition is intense, requiring companies to focus on delivering high-quality products and exceptional customer support to maintain their market share. The global Ring Scan EUS System market is expected to witness considerable growth over the forecast period (2025-2033). The increasing adoption of minimally invasive surgical procedures, the growing geriatric population susceptible to gastrointestinal diseases, and rising healthcare expenditure in developing nations all contribute to market expansion. Regional variations exist, with North America and Europe currently holding a substantial market share due to advanced healthcare infrastructure and high adoption rates. However, the Asia-Pacific region is projected to show significant growth potential in the coming years, driven by rising healthcare awareness and increasing investments in medical technology. The competitive landscape is characterized by established players and emerging companies vying for market dominance through product differentiation, strategic alliances, and technological advancements. To effectively navigate this dynamic environment, companies are focusing on research and development, regulatory approvals, and strategic market expansion strategies.

This digitally compiled map includes geology, oil and gas fields, and geologic provinces of Europe. The oil and gas map is part of a worldwide series released on CD-ROM by the World Energy Project of the U.S. Geological Survey. For data management purposes the world is divided into eight energy regions corresponding approximately to the economic regions of the world as defined by the U.S. Department of State. Europe (Region 4) including Turkey (Region 2) includes Albania, Andorra, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Liechtenstein, Luxembourg, The Former Yugoslav Republic of Macedonia, Malta, Monaco, Netherlands, Norway, Poland, Portugal, Romania, San Marino, Serbia and Montenegro, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, Vatican City, Faroe Islands, Gibraltar, Guernsey, Jersey, Isle of Man, Svalbard

The global component of the OOI design includes a network of moorings at critical, yet under-sampled, high-latitude locations such as within the Irminger Sea in the North Atlantic. Moorings located in the Irminger Sea support sensors for measurement of air-sea fluxes of heat, moisture, and momentum, and physical, biological, and chemical properties throughout the water column. The Global Irminger Sea Array is a combination of fixed platforms (moorings), moored profilers to address the requirement to sample the full water column, and mobile platforms (gliders) that provide simultaneous spatial and temporal sampling capabilities. The array is composed of a Surface Mooring containing fixed instruments situated on the buoy and through the upper 1500 m of the water column, a subsurface Global Profiler Mooring hosting one wire-following profiler operating from ~240 m to 2400 m, and two Flanking Moorings that form a triangular array ~20 km on a side. These Flanking Moorings have their uppermost flotation at ~40 m depth and instruments at discrete depths along the mooring line to a depth of 2700 m. Open-Ocean Gliders sample within and around the triangular array equipped with acoustic modems to relay data from the Flanking Moorings to shore via satellite telemetry. Profiling Gliders sample the upper water column near the Apex Profiler Mooring. acknowledgment=Funding provided by the National Science Foundation. Glider deployed by Woods Hole Oceanographic Institution cdm_data_type=TrajectoryProfile cdm_profile_variables=time, latitude, longitude, profile_id, time_uv, lat_uv, lon_uv, u, v cdm_trajectory_variables=trajectory, wmo_id contributor_name=Paul Matthias,Peter Brickley,Sheri White,Diana Wickman,Colin Dobson,John Kerfoot contributor_role=CGSN Program Manager,CGSN Operations Engineer,CGSN Operations Engineer,CGSN Glider Lead,CGSN Glider Pilot Lead,Data Management Conventions=Unidata Dataset Discovery v1.0, COARDS, CF-1.6 deployment_number=2 Easternmost_Easting=-39.05232237934008 featureType=TrajectoryProfile geospatial_lat_max=59.94600527748892 geospatial_lat_min=59.82561660208935 geospatial_lat_units=degrees_north geospatial_lon_max=-39.05232237934008 geospatial_lon_min=-39.328965913857516 geospatial_lon_units=degrees_east geospatial_vertical_max=970.1056 geospatial_vertical_min=0.009905766 geospatial_vertical_positive=down geospatial_vertical_units=m history=2018-02-12T19:21:31Z: Data Source /Users/kerfoot/datasets/ooi/deployments/dac/data/GI05MOAS-GL484/D00002/nc-source/deployment0002_GI05MOAS-GL484-04-CTDGVM000-telemetered-ctdgv_m_glider_instrument_20150817T104454.682040-20150826T235906.440430.nc 2018-02-12T19:22:54Z: this is fake history

2018-09-19T13:32:48Z (local files) 2018-09-19T13:32:48Z http://localhost:8080/erddap/tabledap/gi_484-20150817T1025.ncCF 2018-10-10T20:34:22Z (local files) 2018-10-10T20:34:22Z https://data.ioos.us/gliders/erddap/tabledap/gi_484-20150817T1025.ncCF id=gi_484-20150817T1025_e698_69e3_e309 infoUrl=http://data.ioos.us/gliders/erddap/ institution=Ocean Observatories Initiative ioos_dac_checksum=1465b087b1fef7e81a638db18eec2e9c ioos_dac_completed=False keywords_vocabulary=GCMD Science Keywords Metadata_Conventions=Unidata Dataset Discovery v1.0, COARDS, CF-1.6 metadata_link=http://ooi.visualocean.net/sites/view/GI05MOAS naming_authority=org.oceanobservatories Northernmost_Northing=59.94600527748892 platform_id=gi_484 platform_type=Slocum Glider processing_level=Raw Slocum glider data. No QC performed program=Ocean Observatories Initiative project=Ocean Observatories Initiative references=http://oceanobservatories.org/,http://ooi.visualocean.net/regions/view/GI sea_name=North Atlantic Ocean source=Observational data from a profiling glider sourceUrl=(local files) Southernmost_Northing=59.82561660208935 standard_name_vocabulary=CF Standard Name Table v27 subsetVariables=trajectory, wmo_id, time, latitude, longitude, profile_id, time_uv, lat_uv, lon_uv, u, v time_coverage_end=2015-08-26T23:37:03Z time_coverage_start=2015-08-17T11:30:04Z Westernmost_Easting=-39.328965913857516

Financial overview and grant giving statistics of American College Of Gastroenterology Inc

This map serves as the baseline for the green infrastructure apps that visualize areas that are relatively undisturbed by development or agriculture.The habitat cores shown were derived using a model built by the Green Infrastructure Center Inc. and adapted by Esri.The Asset Finder app uses this web map as its basis.This web map provides an easily accessible data base of intact core habitat areas across the continental United States, appropriate in scale to support Green Infrastructure Planning at local, regional and national scales, using the best available national data. The results are intended to be supplemented or replaced with more current or higher resolution data when available, while still supporting Green Infrastructure planning initiatives at the regional level.Using a methodology outlined by the Green Infrastructure Center, Inc. Esri staff created a national intact habitat cores database for the lower 48 United States.The methodology identified, using nationally available datasets, intact or minimally disturbed areas at least 100 acres in size and with a minimum width of 200 meters.The identification of intact areas relied upon the 2011 National Land Cover Database. Potential cores areas were selected from land cover categories not containing the word “developed” or those categories associated with agriculture uses (crop, hay and pasture lands). The resulting areas were tested for size and width requirements, and then converted into unique polygons.These polygons were then overlaid with a diverse assortment of physiographic, biologic and hydrographic layers to use in computing a “core quality index”.These layers included:Number of endemic species (Mammals, Fish, Reptiles, Amphibians, Trees) (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16, www.pnas.org/cgi/doi/10.1073/pnas.1418034112)Priority Index areas: Endemic species, small home range size and low protection status. (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16, www.pnas.org/cgi/doi/10.1073/pnas.1418034112)Unique ecological systems (based upon work by Aycrig, Jocelyn L, et. al. (2013) Representation of Ecological Systems within the Protected Areas Network of the Continental United States. PLos One 8(1):e54689). New data constructed by Esri staff, using TNC Ecological Regions as summary areas.Ecologically relevant landforms (Theobald DM, Harrison-Atlas D, Monahan WB, Albano CM (2015) Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning. PLoS ONE 10(12): e0143619. doi:10.1371/journal.pone.0143619 ,http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0143619Local Landforms (produced 3/2016) by Deniz Basaran and Charlie Frye, Esri, 30 m* resolution."Improved Hammond’s Landform Classification and Method for Global 250-m Elevation Data" by Karagulle, Deniz; Frye, Charlie; Sayre, Roger; Breyer, Sean; Aniello, Peter; Vaughan, Randy; Wright, Dawn, has been successfully submitted online and is presently being given consideration for publication in Transactions in GIS.*we scaled the neighborhood windows from the 250-meter method described in the paper, and then applied that to 30-meter data in the U.S.National Elevation Dataset, USGS, 30 m resolution, http://viewer.nationalmap.gov/launch/NWI – National Wetlands Inventory “ Classification of Wetlands and Deepwater Habitats of the United States”. U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC. FWS/OBS-79/31 , U.S. Fish and Wildlife Service, Division of Habitat and Resouce Conservation (prepared 10/2015)NLCD 2011 – National LandCover Database 2011http://www.mrlc.gov/nlcd2011.php (downloaded 1/2016) Homer, C.G., et. al. 2015,Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354 NHDPlusV2 –https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plusReceived from Charlie Frye, ESRI 3/2016. Produced by the EPA with support from the USGS.gSSURGO –Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Available online at http://websoilsurvey.nrcs.usda.gov/. Accessed 3/2016, 30 m resolutionGAP Level 3 Ecological System Boundaries (downloaded 4/ 2016)http://gapanalysis.usgs.gov/gaplandcover/data/download/NOAA CCAP Coastal Change Analysis Program Regional Land Cover and Change–downloaded by state (3/2016) from: https://coast.noaa.gov/ccapftp/#/ Description: https://coast.noaa.gov/dataregistry/search/collection/info/ccapregional30 m resolution, 2010 edition of dataNHD USGS National Hydrography Dataset http://nhd.usgs.gov/data.htmlTNC Terrestrial Ecoregionshttp://maps.tnc.org/gis_data.html#TNClands (downloaded 3/2016)2015 LCC Network Areashttps://www.sciencebase.gov/catalog/item/55b943ade4b09a3b01b65d78Evaluation:The creation of a national core quality index is a very ambitious objective, given the extreme variability in ecosystem conditions across the United States. The additional attributes were intended to provide flexibility in accommodating regional or local environmental differences across the U.S.Scripts for constructing local cores and scoring them using the Green Infrastructure Center’s methodology are available on esri.com/greeninfrastructureTwo general approaches were used in the developing core quality index values. The first (default) follows the guidance of the Green Infrastructure Center’s scoring approach developed for the southeastern US where size of the core is the primary determinant of quality. The second; Bio-Weights puts more emphasis on bio-diversity and uniqueness ecosystem type and de-emphasizes slightly the importance of core size. This is to compensate for the very large intact core habitat areas in the west and southwest which also have comparatively low biodiversity values.Scoring values:Default Weights0.4, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.1, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.05, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)Bio-Weights0.2, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.25, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.1, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)

Optimized Hot Spot Analysis using the Getis-Ord Gi* Statistic. The hot spot results include the z-scores and p-values calculations, the number of neighbors and the statistically significant bins of the Gi statistic. The display shows hot spots (areas above the mean) and cold spots (areas below the mean). US Census American Community Survey (ACS) 2017, 5-year estimates of the key demographic characteristics of Block Groups geographic level in Orange County, California. The US Census geodemographic data are based on the 2017 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.