Logical Observation Identifiers Names and Codes (LOINC) is a database and universal standard for identifying medical laboratory observations. This dataset shows the codes which maps to source organization. Mapping each entity-specific code to its corresponding universal code can represent a significant investment of both human and financial capital.

281 Global import shipment records of Logic Ic with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This data package shows the information of Logical Observation Identifiers Names Codes Map to Source Organization, Codes Source Organization and Names and Codes.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. In order to avoid two repetitive ground field efforts, the sampling plan was devised from a combination of both vegetation maps. Using OR logic, overlays were created using both maps as input for each class, and random samples were developed for each class in excess of 30 polygons. Where there were less than 30 polygons sample sites were selected non-randomly from each polygon (i.e. a 100% sample). A total of 512 ground sampling sites were developed from a total of 21 vegetation and land cover classes which are represented on both vegetation maps. Using GIS tools, an ASCII file was generated with ground coordinates representing each of these sites. The 512 sets of coordinates were appropriately re-formatted and directly downloaded as waypoints in three North American Rockwell PLGR GPS receivers. During the week of August 4, 1997 three field crews of two persons each worked together at the monument in a coordinated effort to identify vegetation/cover types at each of the sites. The field crews had a paper map showing the location of the plots and the polygon boundaries (but not attributes) overlaid on topographic data. One team member operated the GPS receiver to navigate to the site, and the other identified the vegetation/cover type and provided a general physical description of the site environs. Sites were considered to be circular with a radius of 50 m. from the coordinate point. Where 2 or more vegetation/cover types occurred, or there was a mosaic of types, all were described within the 50 m. radius of the site coordinate.

Environmental Sensitivity Index (ESI) maps are an integral component in oil-spill contingency planning and assessment. They serve as a source of information in the event of an oil spill incident. ESI maps are a product of the Hazardous Materials Response Division of the Office of Response and Restoration (OR&R).ESI maps contain three types of information: shoreline habitats (classified according to their sensitivity to oiling), human-use resources, and sensitive biological resources. Most often, this information is plotted on 7.5 minute USGS quadrangles, although in Alaska, USGS topographic maps at scales of 1:63,360 and 1:250,000 are used, and in other atlases, NOAA charts have been used as the base map. Collections of these maps, grouped by state or a logical geographic area, are published as ESI atlases. Digital data have been published for most of the U.S. shoreline, including Alaska, Hawaii and Puerto Rico.

By mapping states to themselves, they create a steady state for the network that can not be accessed by any other state. Note, that in principle, initial states can converge towards multiple different steady states. This behavior is captured easily by just adding these states to all of the corresponding steady state lists.

44 Global import shipment records of Logic with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The HYDRO1k data sets are the result of the cooperative project at the U.S. Geological Survey's (U.S.G.S.) EROS Center. The goal of the project is the development of a globally consistent hydrologic derivative data set. The effort has been led by U.S.G.S. scientists in collaboration with the United Nations Environment Programme/Global Resource Information Database (UNEP/GRID) located in Sioux Falls, South Dakota.

Development of the HYDRO1k database was made possible by the completion of the 30 arc-second digital elevation model at EROS in 1996, entitled GTOPO30. This data set, with its nominal cell size of 1 km, has been and will continue to be applied by many scientists and researchers to hydrologic and land form studies. Inevitably, these studies require development, at a minimum, of a standard suite of derivative products. The HYDRO1k package provides, for each continent, a suite of six raster and two vector data sets. These data sets cover many of the common derivative products used in hydrologic analysis. The raster data sets are the hydrologically correct DEM, derived flow directions, flow accumulations, slope, aspect, and a compound topographic (wetness) index. The derived streamlines and basins are distributed as vector data sets.

In addition to displaying earthquakes by magnitude, this service also provide earthquake impact details. Impact is measured by population as well as models for economic and fatality loss. For more details, see: PAGER Alerts. Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cache-able relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cache-able.Update Frequency: Events are updated as frequently as every 5 minutes and are available up to 30 days with the following exceptions:

Events with a Magnitude LESS than 4.5 are retained for 7 daysEvents with a Significance value, 'sig' field, of 600 or higher are retained for 90 days In addition to event points, ShakeMaps are also provided. These have been dissolved by Shake Intensity to reduce the Layer Complexity.The specific layers provided in this service have been Time Enabled and include: Events by Magnitude: The event’s seismic magnitude value.Contains PAGER Alert Level: USGS PAGER (Prompt Assessment of Global Earthquakes for Response) system provides an automated impact level assignment that estimates fatality and economic loss.Contains Significance Level: An event’s significance is determined by factors like magnitude, max MMI, ‘felt’ reports, and estimated impact.Shake Intensity: The Instrumental Intensity or Modified Mercalli Intensity (MMI) for available events.For field terms and technical details, see: ComCat DocumentationAlternate SymbologiesVisit the Classic USGS Feature Layer item for a Rainbow view of Shakemap features.RevisionsAug 14, 2024: Added a default Minimum scale suppression of 1:6,000,000 on Shake Intensity layer.Jul 11, 2024: Updated event popup, setting 'Tsunami Warning' text to 'Alert Possible' when flag is present. Also included hyperlink to tsunami warning center.Feb 13, 2024: Updated feed logic to remove Superseded eventsThis map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to USGS source for official guidance.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The data in this layer is is updated weekly from Wellogic, the Michigan Department of Environment, Great Lakes, and Energy statewide groundwater database. This layer is filtered to only show Type II public wells. Type II wells are noncommunity wells and serve 25 or more individuals or 15 or more service connections on an average daily basis for 60 or more days per year. Examples include restaurants, schools, hotels, campgrounds, churches, and day care centers.Although the derived data in these files represents the best readily available data, the six files do not represent a complete database of all wells or well records in existence. Until January 1, 2000 not all water well records for new wells in Michigan were entered into Wellogic. For wells drilled before 2000 the rate of inclusion is highly variable from one county to another. Further, there is a quality control check on location that may exclude a limited number of wells from Wellogic from this complete layer.NOTE: This data download does not contain the associated lithology files. Download this data on our Wellogic Water Wells by County website. If you have questions concerning this data, please contact:Wellogic@Michigan.gov------------------------------------------------------------------------------------------Field Definitions:WELLID : Wellogic ID number (unique identifying number, first 2 digits represent county number)PERMIT_NUM : Well permit number as assigned by local health departmentWELL_TYPE : Type of wellOTH = OtherHEATP = Heat pumpHOSHLD = HouseholdINDUS = IndustrialIRRI = IrrigationTESTW = Test wellTY1PU = Type I publicTY2PU = Type II publicTY3PU = Type III publicTYPE_OTHER : Type of well if WELL_TYPE is 'OTH'WEL_STATUS : Status of wellOTH = OtherACT = ActiveINACT = InactivePLU = Plugged/AbandonedSTATUS_OTH : Status of well if WEL_STATUS is 'OTH'WSSN : Water Supply Serial Number, only if public wellWELL_NUM : Individual well number/name, only if public wellDRILLER_ID : Water Well Drilling Contractor Registration Number as assigned by State of MichiganDRILL_METH : Method used to drill the well boreholeOTH = OtherAUGBOR = Auger/BoredCABTOO = Cable ToolCASHAM = Casing HammerDRIVEN = Driven HandHOLROD = Hollow RodJETTIN = JettedMETH_OTHER : Method used to drill if DRILL_METH is 'OTH'CASE_TYPE : Well casing typeOTH = OtherUNK = UnknownPVCPLA = PVC PlasticSTEBLA = Steel-blackSTEGAL = Steel-GalvanizedCASE_OTHER : Well casing type is CASE_TYPE is 'OTH'CASE_DIA : Well Casing Diameter (in inches)CASE_DEPTH : Depth of Casing (in feet)SCREEN_FRM : Depth of top of screen (in feet)SCREEN_TO : Depth of bottom of screen (in feet)SWL : Depth of Static Water Level (in feet)FLOWING : Naturally flowing well (Y or N)AQ_TYPE : Aquifer typeDRIFT = Well draws water from the glacial driftROCK = Well draws water from the bedrockDRYHOL = Dry hole, well did not produce waterUNK = UnknownTEST_DEPTH : Depth of drawdown when the well was developed (in feet)TEST_HOURS : Duration of pumping when the well was developed (in hours)TEST_RATE : Rate of water flow when the well was developed (in Gallons per Minute)TEST_METHD : Method used to develop the wellUNK = UnknownOTH = OtherAIR = AirBAIL = BailerPLUGR = PlungerTSTPUM = Test PumpTEST_OTHER : Method used to develop the well if TEST_METHD is 'OTH'GROUT : Whether the well was grouted or notPMP_CPCITY : Capacity of the pump installed in the well (in Gallons per minute)METHD_COLL : Method of collection of the latitude/longitude coordinates001 = Address Matching-House Number002 = Address Matching-Street Centerline004 = Address Matching-Nearest Intersection012 = GPS Carrier Phase Static Relative Position Tech.013 = GPS Carrier Phase Kinematic Relative Position Tech.014 = GPS Code Measurement Differential (DGPS)015 = GPS Precise Positioning Service016 = GPS Code Meas. Std. Positioning Service SA Off017 = GPS Std. Positioning Service SA On018 = Interpolation-Map019 = Interpolation-Aerial Photo020 = Interpolation-Satellite Photo025 = Classical Surveying Techniques027 = Section centroid028 = TownRange centroid036 = Quarter-Quarter-Quarter centroidELEV_METHD : Method of collection of the elevation003 = GPS Code Measurement Differential (DGPS)005 = GPS Code Meas. Std. Positioning Svc. SA Off007 = Classical Surveying Techniques014 = Topographic Map InterpolationOTH = OtherUNK = UnknownWITHIN_CO: Whether the well is within the stated countyWITHIN_SEC: Whether the well is within the stated land survey sectionLOC_MATCH: Whether the well is within the stated Tier/RangeSEC_DIST: Whether the well point is within 200 feet of the stated land survey sectionELEV_DEM: Elevation in feet above mean sea levelELEV_DIF: Absolute difference, in feet, between ELEVATION and ELEV_DEMLANDSYS: The Land System Group polygon that the well falls withinDEPTH_FLAG:1: WELL_DEPTH = 02: WELL_DEPTH < 25ft or WELL_DEPTH > 1000ftELEV_FLAG:1: ELEVATION (Wellogic Field) =02: ELEVATION (Wellogic Field) < 507ft OR > 1980ft3: ELEVATION (Wellogic Field) < DEM min OR > DEM max4: ELEV_DIF > 20 ftSWL_FLAG:1: SWL = 02: SWL >= WELL_DEPTH in a Bedrock well OR SWL >= SCREEN_BOT in a Glacial well3: SWL > 900ftSPC_CPCITY: Specific Capacity = (TEST_RATE / TEST_DEPTH). Only calculated if TEST_METHD = BAIL, PLUGR or TSTPUMAQ_CODE:N: No Lithology Record associated with the well recordB: Blank (AQTYPE = null) noted among the strataD: Drift (Glacial) WellR: Rock WellU: Unknown Lithology noted among the strata*PROCESSING NOTE– This evaluation reads the [AQTYPE] field for each stratum from the LITHOLOGY table, beginning at the top and looping down to each subjacent stratum. If the previous stratum = ‘R’ANDthe bottommost stratum = ‘R’, then [AQ_CODE] is set to ‘R’. If the previous stratum = ‘R’ANDthe next stratum = ‘D’, then [AQ_CODE] is set to ‘D’ and [AQ_FLAG] is set to ‘L’. IfaType= ‘R’ANDscreendepth> 0 R’AND screendepth<=welldepth, then [AQ_CODE] is set to ‘D’ and [AQ_FLAG] is set to ‘S’. IfaType= ‘R’ANDwelldepth <= topofrock,then [AQ_CODE] is set to ‘D’ and [AQ_FLAG] is set to ‘D’.

mapping of ethical vocabulary

Annual summary of data on mapping ethical vocabulary

Subdivision Plats and Annexations Maps For the City of Loveland, Colorado.

To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms from a vast space of 10141 genotypes (instruction sequences), which can form 512 different phenotypes. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. The likely common cause is robustness to genotypic change. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable.

description: Environmental Sensitivity Index (ESI) maps are an integral component in oil-spill contingency planning and assessment. They serve as a source of information in the event of an oil spill incident. ESI maps contain three types of information: shoreline habitats (classified according to their sensitivity to oiling), sensitive biological resources, and human-use resources. Most often, this information is plotted on 7.5 minute U.S. Geological Survey (USGS) quadrangles, although in the Alaska ESI maps, USGS topographic maps at scales of 1:63,360 and 1:250,000 are used, and in other ESI maps, NOAA charts have been used as the base map. Collections of these maps, grouped by state or a logical geographic area, are published as ESI atlases. Digital data have been published for most of the U.S. shoreline, including Alaska, Hawaii, and Puerto Rico.; abstract: Environmental Sensitivity Index (ESI) maps are an integral component in oil-spill contingency planning and assessment. They serve as a source of information in the event of an oil spill incident. ESI maps contain three types of information: shoreline habitats (classified according to their sensitivity to oiling), sensitive biological resources, and human-use resources. Most often, this information is plotted on 7.5 minute U.S. Geological Survey (USGS) quadrangles, although in the Alaska ESI maps, USGS topographic maps at scales of 1:63,360 and 1:250,000 are used, and in other ESI maps, NOAA charts have been used as the base map. Collections of these maps, grouped by state or a logical geographic area, are published as ESI atlases. Digital data have been published for most of the U.S. shoreline, including Alaska, Hawaii, and Puerto Rico.

Collection description

None

Subject species

homo sapiens

Map type

Other

This Guide is designed to assist you with using ArcGIS Online (AGOL)'s Map Viewer.An ArcGIS web map is an interactive display of geographic information. Web maps are made by adding and combining layers. The layers are made from data, they are logical collections of geographic data.Map Viewer can be used to view, explore and create web maps in ArcGIS Online.

description: Environmental Sensitivity Index (ESI) maps are an integral component in oil-spill contingency planning and assessment. They serve as a source of information in the event of an oil spill incident. ESI maps contain three types of information: shoreline habitats (classified according to their sensitivity to oiling), sensitive biological resources, and human-use resources. Most often, this information is plotted on 7.5 minute USGS quadrangles, although in the Alaska ESI maps, USGS topographic maps at scales of 1:63,360 and 1:250,000 are used, and in other ESI maps, NOAA charts have been used as the base map. Collections of these maps, grouped by state or a logical geographic area, are published as ESI atlases. Digital data have been published for most of the U.S. shoreline, including Alaska, Hawaii, and Puerto Rico.; abstract: Environmental Sensitivity Index (ESI) maps are an integral component in oil-spill contingency planning and assessment. They serve as a source of information in the event of an oil spill incident. ESI maps contain three types of information: shoreline habitats (classified according to their sensitivity to oiling), sensitive biological resources, and human-use resources. Most often, this information is plotted on 7.5 minute USGS quadrangles, although in the Alaska ESI maps, USGS topographic maps at scales of 1:63,360 and 1:250,000 are used, and in other ESI maps, NOAA charts have been used as the base map. Collections of these maps, grouped by state or a logical geographic area, are published as ESI atlases. Digital data have been published for most of the U.S. shoreline, including Alaska, Hawaii, and Puerto Rico.

OMOP2OBO Mappings - N3C OMOP to OBO Working group

This repository stores OMOP2OBO mappings which have been processed for use within the National COVID Cohort Collaborative (N3C) Enclave. The version of the mappings stored in this repository have been specifically formatted for use within the N3C Enclave.

N3C OMOP to OBO Working Group: https://covid.cd2h.org/ontology

Accessing the N3C-Formatted Mappings

You can access the three OMOP2OBO HPO mapping files in the Enclave from the Knowledge store using the following link: https://unite.nih.gov/workspace/compass/view/ri.compass.main.folder.1719efcf-9a87-484f-9a67-be6a29598567.

The mapping set includes three files, but you only need to merge the following two files with existing data in the Enclave in order to be able to create the concept sets:

OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv

OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv

The first file OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv, contains columns for the OMOP concept ids and codes as well as specifies information like whether or not the OMOP concept’s descendants should be included when deriving the concept sets (defaults to FALSE). The other file OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv, contains details on the mapping’s label (i.e., the HPO curie and label in the concept_set_id field) and its provenance/evidence (the specific column to access for this information is called intention).

Creating Concept Sets

Merge these files together on the column named codeset_id and then join them with existing Enclave tables like concept and condition_occurrence to populate the actual concept sets. The name of the concept set can be obtained from the OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv file and is stored as a string in the column called concept_set_id. Although not ideal (but is the best way to approach this currently given what fields are available in the Enclave), to get the HPO CURIE and label will require applying a regex to this column.

An example mapping is shown below (highlighting some of the most useful columns):

codeset_id: 900000000 concept_set_id: [OMOP2OBO] hp_0002031-abnormal_esophagus_morphology concept: 23868 code: 69771008 codeSystem: SNOMED includeDescendants: False intention:

Mixed - This mapping was created using the OMOP2OBO mapping algorithm (https://github.com/callahantiff/OMOP2OBO).

The Mapping Category and Evidence supporting the mappings are provided below, by OMOP concept:

23868

Mapping Category: Automatic Exact - Concept

Mapping Provenance

OBO_DbXref-OMOP_ANCESTOR_SOURCE_CODE:snomed_69771008 | OBO_DbXref-OMOP_CONCEPT_SOURCE_CODE:snomed_69771008 | CONCEPT_SIMILARITY:HP_0002031_0.713

Release Notes - v2.0.0

Preparation

In order to import data into the Enclave, the following items are needed:

Obtain API Token, which will be included in the authorization header (stored as GitHub Secret)

Obtain username hash from the Enclave

OMOP2OBO Mappings (v1.5.0)

Data

Concept Set Container (concept_set_container): CreateNewConceptSet

Concept Set Version (code_sets): CreateNewDraftOMOPConceptSetVersion

Concept Set Expression Items (concept_set_version_item): addCodeAsVersionExpression

Script

n3c_mapping_conversion.py

Generated Output

Need to have the codeset_id filled from self-generation (ideally, from a conserved range) prior to beginning any of the API steps. The current list of assigned identifiers is stored in the file named omop2obo_enclave_codeset_id_dict_v2.0.0.json. Note that in order to accommodate the 1:Many mappings the codeset ids were re-generated and rather than being ampped to HPO concepts, they are mapped to SNOMED-CT concepts. This creates a cleaner mapping and will easily scale to future mapping builds.

To be consistent with OMOP tools, specifically Atlas, we have also created Atlas-formatted json files for each mapping, which are stored in the zipped directory named atlas_json_files_v2.0.0.zip. Note that as mentioned above, to enable the representation of 1:Many mappings the filenames are no longer named after HPO concepts they are now named with the OMOP concept_id and label and additional fields have been added within the JSON files that includes the HPO ids, labels, mapping category, mapping logic, and mapping evidence.

File 1: concept_set_container

Generated Data: OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_container.csv

Columns:

concept_set_id

concept_set_name

intention

assigned_informatician

assigned_sme

project_id

status

stage

n3c_reviewer

alias

archived

created_by

created_at

File 2: concept_set_expression_items

Generated Data: OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv

Columns:

codeset_id

concept_id

code

codeSystem

ontology_id

ontology_label

mapping_category

mapping_logic

mapping_evidence

isExcluded

includeDescendants

includeMapped

item_id

annotation

created_by

created_at

File 3: concept_set_version

Generated Data: OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv

Columns:

codeset_id

concept_set_id

concept_set_version_title

project

source_application

source_application_version

created_at

atlas_json

most_recent_version

comments

intention

limitations

issues

update_message

status

has_review

reviewed_by

created_by

provenance

atlas_json_resource_url

parent_version_id

is_draft

Generated Output:

OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_container.csv

OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv

OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv

atlas_json_files_v2.0.0.zip

omop2obo_enclave_codeset_id_dict_v2.0.0.json

The inference rules of the logic of the proposed scheme.