This data set is part of an ongoing project to consolidate interagency fire perimeter data. The record is complete from the present back to 2020. The incorporation of all available historic data is in progress.The InFORM (Interagency Fire Occurrence Reporting Modules) FODR (Fire Occurrence Data Records) are the official record of fire events. Built on top of IRWIN (Integrated Reporting of Wildland Fire Information), the FODR starts with an IRWIN record and then captures the final incident information upon certification of the record by the appropriate local authority. This service contains all wildland fire incidents from the InFORM FODR incident service that meet the following criteria:Categorized as a Wildfire (WF) or Prescribed Fire (RX) recordIs Valid and not "quarantined" due to potential conflicts with other recordsNo "fall-off" rules are applied to this service.Service is a real time display of data.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes:ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.CalculatedAcresA measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire. More specifically, the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands. The minimum size must be 0.1.ContainmentDateTimeThe date and time a wildfire was declared contained. ControlDateTimeThe date and time a wildfire was declared under control.CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.CreatedOnDateTimeDate/time that the Incident record was created.IncidentSizeReported for a fire. The minimum size is 0.1.DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.EstimatedCostToDateThe total estimated cost of the incident to date.FinalAcresReported final acreage of incident.FinalFireReportApprovedByTitleThe title of the person that approved the final fire report for the incident.FinalFireReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.FinalFireReportApprovedDateThe date that the final fire report was approved for the incident.FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. FireOutDateTimeThe date and time when a fire is declared out. FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.IncidentNameThe name assigned to an incident.IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.InitialResponseDateTimeThe date/time of the initial response to the incident. More specifically when the IC arrives and performs initial size up. IsFireCauseInvestigatedIndicates if an investigation is underway or was completed to determine the cause of a fire.IsFSAssistedIndicates if the Forest Service provided assistance on an incident outside their jurisdiction.IsReimbursableIndicates the cost of an incident may be another agency’s responsibility.IsTrespassIndicates if the incident is a trespass claim or if a bill will be pursued.LocalIncidentIdentifierA number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year.ModifiedBySystemArcGIS Server username of system that last modified the IRWIN Incident record.ModifiedOnDateTimeDate/time that the Incident record was last modified.PercentContainedIndicates the percent of incident area that is no longer active. Reference definition in fire line handbook when developing standard.POOCityThe closest city to the incident point of origin.POOCountyThe County Name identifying the county or equivalent entity at point of origin designated at the time of collection.POODispatchCenterIDA unique identifier for the dispatch center that intersects with the incident point of origin. POOFipsThe code which uniquely identifies counties and county equivalents. The first two digits are the FIPS State code and the last three are the county code within the state.POOJurisdictionalAgencyThe agency having land and resource management responsibility for a incident as provided by federal, state or local law. POOJurisdictionalUnitNWCG Unit Identifier to identify the unit with jurisdiction for the land where the point of origin of a fire falls. POOJurisdictionalUnitParentUnitThe unit ID for the parent entity, such as a BLM State Office or USFS Regional Office, that resides over the Jurisdictional Unit.POOLandownerCategoryMore specific classification of land ownership within land owner kinds identifying the deeded owner at the point of origin at the time of the incident.POOLandownerKindBroad classification of land ownership identifying the deeded owner at the point of origin at the time of the incident.POOProtectingAgencyIndicates the agency that has protection responsibility at the point of origin.POOProtectingUnitNWCG Unit responsible for providing direct incident management and services to a an incident pursuant to its jurisdictional responsibility or as specified by law, contract or agreement. Definition Extension: - Protection can be re-assigned by agreement. - The nature and extent of the incident determines protection (for example Wildfire vs. All Hazard.)POOStateThe State alpha code identifying the state or equivalent entity at point of origin.PredominantFuelGroupThe fuel majority fuel model type that best represents fire behavior in the incident area, grouped into one of seven categories.PredominantFuelModelDescribes the type of fuels found within the majority of the incident area. UniqueFireIdentifierUnique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = POO protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters) FORIDUnique identifier assigned to each incident record in the FODR database.

The Jellyfish Database Initiative (JeDI) is a scientifically-coordinated global database dedicated to gelatinous zooplankton (members of the Cnidaria, Ctenophora and Thaliacea) and associated environmental data. The database holds 476,000 quantitative, categorical, presence-absence and presence only records of gelatinous zooplankton spanning the past four centuries (1790-2011) assembled from a variety of published and unpublished sources. Gelatinous zooplankton data are reported to species level, where identified, but taxonomic information on phylum, family and order are reported for all records. Other auxiliary metadata, such as physical, environmental and biometric information relating to the gelatinous zooplankton metadata, are included with each respective entry. JeDI has been developed and designed as an open access research tool for the scientific community to quantitatively define the global baseline of gelatinous zooplankton populations and to describe long-term and large-scale trends in gelatinous zooplankton populations and blooms. It has also been constructed as a future repository of datasets, thus allowing retrospective analyses of the baseline and trends in global gelatinous zooplankton populations to be conducted in the future.

References:

Lucas, C.J., et al. 2014. Gelatinous zooplankton biomass in the global oceans: geographic variation and environmental drivers. Global Ecol. Biogeogr. (DOI: 10.1111/geb.12169)

Condon, R. H., et al. 2013. Recurrent jellyfish blooms are a consequence of global oscillations. PNAS vol. 110(3) 1000-1005. www.pnas.org/cgi/doi/10.1073/pnas.1210920110)

Condon, R. H., et al. 2012.Questioning the Rise of Gelatinous Zooplankton in the World’s Oceans. BioScience vol. 62(2) 160-169. (doi:10.1525/bio.2012.62.2.9)

Analysis of IP holdings (active patents and trademarks) can shed light on technology and innovation at the corporate level. Insight is achieved from a variety of analyses, for example:

How does the corporate IP portfolio of a given company compare to its competitors?

Who are new entrants in the sector with similar technologies, based on their intellectual property filings?

How has a company's IP filing activity changed over time? Are patents and trademarks being filed into the similar classes as done previously, or into new or different classes, indicating a shift to new products or services, or innovation into potential new areas and technologies.

Coverage includes Intellectual Property registries from the USA, Canada and Europe.

Description of the Data Set

This dataset is taken from the Arkiv Digital AD AB image and index database. When a child was born he or she was registered in a church record book called Birth and Christening records by the priest. They registered the name of the child, when the child was born and baptized, where the child was living and information about the father and mother of the child. The index is based on manual annotation of images from several books between the year 1800 to 1840.

The dataset consists of 191,301 index rows and 15,000 images and has been divided into train: 133,941 index rows and 10,500 images eval: 28,303 index rows and 2,250 images test: 29,057 index rows and 2,250 images

Swedish county (län)

Gävleborgs län - 23 982 index rows Gotlands län - 9 925 index rows Norrbottens län - 12 198 index rows Västerbottens län - 16 118 index rows Västernorrlands län - 21 014 index rows Västmanlands län - 21 141 index rows Älvsborgs län - 52 988 index rows Örebro län - 33 935 index tows

Description of the index columns

• id: Arkiv Digital AD AB ID in database
• index_aid: Index AID (Arkiv Digital AD AB external ID)
• county: County where the child was born or registered (usually not in the image)
• parish: Parish where the child was born or registered (can be written at the top of the page or entirely missing from the image)
• child_first_name: Given name of the child
• birth_date: Date of birth, format YYYYMMDD (on the image it is usually written DD/MM with the year on top of page)
• baptism_date: Date of baptism, format YYYYMMDD (on the image it usually written DD/MM with the year on top of page)
• birth_place: Place of birth
• father_title: Title or occupation of the father
• father_first_name: Given name of the father
• father_last_name: Surname of the father
• father_age: Age of the father when the child was born <== (available only in the master dataset SHIBRm)
• mother_title: Title or occupation of the mother
• mother_first_name: Given name of the mother
• mother_last_name: Surname of the mother
• mother_age: Age of the mother when the child was born
• image_aid: Image AID (Arkiv Digital AD AB external ID)
• image_path: Relative path to the image (images/)

Use of the Materials

The users of the SHIBR Data Set must agree that: - The use of the data set is restricted to research purpose only - No redistribution of the dataset is allowed - In any resultant publications of research that uses the dataset, due credits will be provided to:

Abbas Cheddad, Hüseyin Kusetogullari, Agrin Hilmkil, Lena Sundin, Amir Yavariabdi, Mustapha Aouache, Johan Hall; "SHIBR-The Swedish Historical Birth Records: A Semi-Annotated Dataset," Neural Computing & Applications, Springer, 2021.

Motivation

Increasing heat stress due to climate change poses significant risks to human health and can lead to widespread social and economic consequences. Evaluating these impacts requires reliable datasets of heat stress projections.

Data Record

We present a global dataset projecting future dry-bulb, wet-bulb, and wet-bulb globe temperatures under 1-4°C global warming scenarios (at 0.5°C intervals) relative to the preindustrial era, using outputs from 16 CMIP6 global climate models (GCMs) (Table 1). All variables were retrieved from the historical and SSP585 scenarios which were selected to maximize the warming signal.

The dataset was bias-corrected against ERA5 reanalysis by incorporating the GCM-simulated climate change signal onto the ERA5 baseline (1950-1976) at a 3-hourly frequency. It therefore includes a 27-year sample for each GCM under each warming target.

The data is provided at a fine spatial resolution of 0.25° x 0.25° and a temporal resolution of 3 hours, and is stored in a self-describing NetCDF format. Filenames follow the pattern "VAR_bias_corrected_3hr_GCM_XC_yyyy.nc", where:

• "VAR" represents the variable (Ta, Tw, WBGT for dry-bulb, wet-bulb, and wet-bulb globe temperature, respectively),

• "GCM" denotes the CMIP6 GCM name,

• "X" indicates the warming target compared to the preindustrial period,

• "yyyy" represents the year index (0001-0027) of the 27-year sample

Table 1 CMIP6 GCMs used for generating the dataset for Ta, Tw and WBGT.

Data Access

An inventory of the dataset is available in this repository. The complete dataset, approximately 57 TB in size, is freely accessible via Purdue Fortress' long-term archive through Globus at Globus Link. After clicking the link, users may be prompted to log in with a Purdue institutional Globus account. You can switch to your institutional account, or log in via a personal Globus ID, Gmail, GitHub handle, or ORCID ID. Alternatively, the dataset can be accessed by searching for the universally unique identifier (UUID): "6538f53a-1ea7-4c13-a0cf-10478190b901" in Globus.

Dataset Validation

We validate the bias-correction method and show that it significantly enhances the GCMs' accuracy in reproducing both the annual average and the full range of quantiles for all metrics within an ERA5 reference climate state. This dataset is expected to support future research on projected changes in mean and extreme heat stress and the assessment of related health and socio-economic impacts.

For a detailed introduction to the dataset and its validation, please refer to our data descriptor currently under review at Scientific Data. We will update this information upon publication.

QuoteWay offers extensive Email Address Data, perfect for businesses seeking high-quality consumer information. This Email Address Data includes approximately 1.3M records, specifically gathered for life insurance purposes and currently resting for about 3 months without contact. Our B2C Contact Data boasts 96% valid postal codes (address data), 98% valid emails, and 70% valid phone numbers.

Our Canadian B2C Email Address Data can be used the following ways: - Marketing Campaigns: Utilize our Email Data to launch targeted marketing campaigns with accurate contact information. - Sales Lead Generation: Enhance your sales lead generation efforts by accessing verified B2C Contact Data. - Customer Profiling: Create detailed customer profiles on our Audience Data using our comprehensive B2C Contact Data. - Direct Mail Campaigns: Execute effective direct marketing campaigns with our B2C Contact Data's 96% valid postal codes and address data. - Email Marketing: Boost your email marketing success rates with 98% valid email data from our B2C Contact Data, allowing for direct marketing. - Telemarketing: Improve telemarketing outcomes with our audience data using 70% valid phone numbers in our B2C Contact Data. - Market Research: Conduct thorough market research with reliable B2C Contact Data for accurate consumer insights.

Key Benefits of our Email Address Data: - High Accuracy: Our B2C Contact Data is verified with 96% valid postal codes, 98% valid email data, and 70% valid phone numbers. - Large Dataset: Access a substantial dataset of approximately 1.3M records. - Fresh Data: The B2C Contact Data has been resting for about 3 months, ensuring it is not overused. - Versatile Use: Suitable for various applications such as marketing, sales, and research. - Compliance Ready: Our audience data is ready to use under QuoteWay Canada Inc., ensuring compliance and ease of use. - Life Insurance Focused: The data was initially collected for life insurance purposes, providing a unique consumer segment. - Reliable Source:QuoteWay's commitment to quality ensures that our B2C Contact Data is reliable and effective.

By leveraging QuoteWay's Email Address Data, businesses can achieve greater accuracy and success in their consumer outreach efforts. The comprehensive and verified nature of our B2C Contact Data makes it an invaluable resource for any organization looking to enhance its contact database.

About this Dataset

This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2015-2016 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 3301 new records, modifications to the spatial location and/or attribution of 19127 records, and the retention of 8636 records from the previous PSS datasets that may or may not be closed (see STATUS field). The ADDRESS2 and DISTRICT_ID fields, previously populated with NOT AVAILABLE, have been removed. This feature class does not have a relationship class.

Category

Education

Keywords

private schools,school,Education

Row Count

31064

Price

$299.00

A dataset containing 57808 species occurrences available in GBIF matching the query: TaxonKey: Limosa Brisson, 1760 HasGeospatialIssue: false Geometry: POLYGON((-141.28 3.34,-141.28 35.82,-86.18 35.82,-86.18 3.34,-141.28 3.34)). The dataset includes 57808 records from 45 constituent datasets: 20 records from Ornithology Collection Non Passeriformes - Royal Ontario Museum. 1 records from DMNS Bird Collection (Arctos). 1 records from UAM Bird Collection (Arctos). 18 records from Museum of Comparative Zoology, Harvard University. 17 records from CAS Ornithology (ORN). 53595 records from EOD - eBird Observation Dataset. 387 records from iNaturalist Research-grade Observations. 1 records from Angelo State Natural History Collections (ASNHC) - Ornithology Collection. 6 records from naturgucker. 30 records from SBMNH Vertebrate Zoology. 87 records from LACM Vertebrate Collection. 4 records from Natural History Museum (London) Collection Specimens. 17 records from Macaulay Library Audio and Video Collection. 4 records from Base de datos de aves mexicanas del Natural History Museum, Tring, Inglaterra. 8 records from Inventario y monitoreo del Canal de Infiernillo para el comanejo de los recursos marinos en el territorio Seri, Golfo de California. 7 records from Avifauna de la laguna Madre de Tamaulipas. 326 records from Riqueza específica, distribución y abundancia de aves acuáticas en la ensenada de La Paz, Baja California Sur, México. 1 records from Aves de las reservas de la biosfera de Durango: La Michilía y Mapimí. 1 records from Actualización y enriquecimiento de las bases de datos del proyecto de evaluación y análisis geográfico de la diversidad faunística de Chiapas. 1 records from Riqueza específica, distribución y abundancia de aves terrestres y marinas en Isla San José, Golfo de California, Baja California Sur, México. 1 records from Programa de erradicación de los roedores introducidos en la Isla Rasa, Baja California: un plan de restructuración ecológica. 1 records from NMNH Extant Specimen and Observation Records. 313 records from Great Backyard Bird Count. 5 records from UWBM Ornithology Collection. 2 records from SEAMAP - marine mammals, birds and turtles. 1 records from Paleobiology Database. 18 records from Birds Specimens. 47 records from SDNHM Birds Collection. 1 records from CNAV/Coleccion Nacional de Aves. 6 records from Vertebrate Zoology Division - Ornithology, Yale Peabody Museum. 14 records from UCLA Donald R. Dickey Bird and Mammal Collection. 42 records from Colección Ornitológica del Museo de Zoología 'Alfonso L . Herrera', México (MZFC, UNAM). 1 records from MZFC/Coleccion de Aves. 22 records from WFVZ Bird Collections. 1 records from PSM Vertebrates Collection. 1 records from MLZ Bird Collection (Arctos). 2 records from AMNH Bird Collection. 1 records from Queensland Museum provider for OZCAM. 3 records from Cowan Tetrapod Collection - Birds. 8 records from UMMZ Birds Collection. 3 records from DMNH Birds. 3 records from MSB Bird Collection (Arctos). 33 records from MVZ Bird Collection (Arctos). 2 records from Borror Lab of Bioacoustics (BLB), Ohio State University. 1 records from CUMV Bird Collection (Arctos). Data from some individual datasets included in this download may be licensed under less restrictive terms.

A dataset containing 30187 species occurrences available in GBIF matching the query: TaxonKey: Animalia DecimalLatitude: > 64.965374146614 or < 66.8638084359169 DecimalLongitude: > -168.194138701141 or < -162.059675645218. The dataset includes 30187 records from 95 constituent datasets: 13 records from International Barcode of Life project (iBOL). 1 records from UMNH Mammals Collection (Arctos). 57 records from MVZ Mammal Collection (Arctos). 1 records from University of Guelph Insect Collection (DEBU). 58 records from Ornithology Collection Non Passeriformes - Royal Ontario Museum. 264 records from CHAS Ornithology (Arctos). 7 records from KUBI Mammalogy Collection. 61 records from UAM Fish Collection (Arctos). 789 records from DMNS Bird Collection (Arctos). 871 records from USGS ASC - Changing Arctic Ecosystems - Alaska - Birds. 1 records from CUMV Mammal Collection (Arctos). 7141 records from UAM Mammal Collection (Arctos). 2 records from Eggs Specimen. 5 records from UAM Herbarium (ALA), Cryptogam Collection (Arctos). 1 records from CRCM Vertebrate Collection. 600 records from BenthosChukchiFN762_1976_Falk5. 7 records from Field Museum of Natural History (Zoology) Mammal Collection. 284 records from CAS Invertebrate Zoology (IZ). 97 records from UAM Bird Collection (Arctos). 136 records from Museum of Comparative Zoology, Harvard University. 5 records from Ichthyology Collection - Royal Ontario Museum. 8403 records from EOD - eBird Observation Dataset. 70 records from iNaturalist Research-grade Observations. 167 records from Invertebrates Collection of the Swedish Museum of Natural History. 7 records from Arctic benthic invertebrate collection of the Zoological Institute of the Russian Academy of Science. 1 records from Circumpolar Seabird Monitoring Plan. 52 records from CAS Ichthyology (ICH). 3 records from DMNS Mammal Collection (Arctos). 2 records from TNHC Ichthyology Collection. 4 records from Illinois Natural History Survey Insect Collection. 2 records from CAS Mammalogy (MAM). 2 records from UMMZ Mammal Collection. 9 records from ZINRAS_Arctic_Benthos. 5 records from SBMNH Vertebrate Zoology. 65 records from MSB Parasite Collection (Arctos). 17 records from LACM Vertebrate Collection. 202 records from Macaulay Library Audio and Video Collection. 2430 records from UAM Insect Collection (Arctos). 111 records from UAM Invertebrate Collection (Arctos). 1 records from KNWR Entomology Collection. 206 records from NMNH Extant Specimen Records. 4 records from Great Backyard Bird Count. 22 records from Canadian Museum of Nature Bird Collection. 14 records from Canadian Museum of Nature Fish Collection. 8 records from UWBM Mammalogy Collection (Arctos). 161 records from UWBM Ornithology Collection. 71 records from UWFC Ichthyology Collection. 1105 records from NODC WOD01 Plankton Database. 14 records from Canadian Museum of Nature - Fish Collection (OBIS Canada). 2 records from Provincial Museum of Alberta, Edmonton, AB, Canada. Birds (Aves). 1 records from Bombus of Canada. 53 records from Paleobiology Database. 3 records from NBM birds. 17 records from SDNHM Birds Collection. 748 records from Arctic Ocean Diversity. 2155 records from KWP Lepidoptera Collection (Arctos). 9 records from Vertebrate Zoology Division - Ornithology, Yale Peabody Museum. 4 records from Invertebrate Zoology Division, Yale Peabody Museum. 2 records from UCLA Donald R. Dickey Bird and Mammal Collection. 10 records from MAL. 61 records from University of Alberta E. H. Strickland Entomological Museum (UASM). 172 records from WFVZ Bird Collections. 63 records from PSM Vertebrates Collection. 3 records from KUBI Ichthyology Collection. 4 records from KU Museum of Invertebrate Paleontology. 8 records from Ophiuroidea collections of the Zoological Institute Russian Academy of Sciences. 1118 records from UAM Earth Sciences Collection (Arctos). 7 records from MVZ Egg and Nest Collection (Arctos). 4 records from USGS Alaska Science Center Polar Bear Maternal Dens. 10 records from Queensland Museum provider for OZCAM. 1 records from Snow Entomological Museum Collection. 26 records from Geographically tagged INSDC sequences. 6 records from Kittlitzs_murrelet. 97 records from MSB Mammal Collection (Arctos). 30 records from MSB Host Collection (Arctos). 1 records from USGS ASC - Yukon Kuskokwim River Delta - Birds - 1992-2002. 329 records from Paleobiology Database. 31 records from UMMZ Birds Collection. 926 records from Macrobenthos Chukchi Sea, 1986. 1 records from DMNH Birds. 3 records from DMNS Egg Collection (Arctos). 14 records from MSB Bird Collection (Arctos). 153 records from Zooplankton Bering Strait Tiglax 1991. 132 records from Arctic Marine Fish Museum Specimens. 4 records from Australian Museum provider for OZCAM. 13 records from NOAA Deep Sea Corals Research and Technology Program. 5 records from World distribution of the aquatic Oligochaeta. 2 records from SIO Marine Vertebrate Collection. 155 records from MVZ Bird Collection (Arctos). 1 records from UAIC Ichthyological Collection. 52 records from UAM Insect Observations (Arctos). 31 records from Borror Lab of Bioacoustics (BLB), Ohio State University. 43 records from Invertebrates (Type Specimens) of the Swedish Museum of Natural History. 1 records from Ornithology Collection Passeriformes - Royal Ontario Museum. 117 records from CHAS Oology Collection (Arctos). Data from some individual datasets included in this download may be licensed under less restrictive terms.

Labour Relations in the United States: 1800

An abridged data format, created by Daan Jansen (IISH) and continuing on earlier work by Joris Kok (IISH), is being offered as an alternative in October 2020. This new version of the dataset includes only records that contain labour relations, leaving out all population data. This update also involved (depending on the dataset in question, substantial) data cleaning, separating male and female individuals, and removing any duplicate records. Hence, the aggregated number of people mentioned in these updated datasets should equal the total population.

This clean dataset is a refined version of our company datasets, consisting of 35M+ data records.

It’s an excellent data solution for companies with limited data engineering capabilities and those who want to reduce their time to value. You get filtered, cleaned, unified, and standardized B2B data. After cleaning, this data is also enriched by leveraging a carefully instructed large language model (LLM).

AI-powered data enrichment offers more accurate information in key data fields, such as company descriptions. It also produces over 20 additional data points that are very valuable to B2B businesses. Enhancing and highlighting the most important information in web data contributes to quicker time to value, making data processing much faster and easier.

For your convenience, you can choose from multiple data formats (Parquet, JSON, JSONL, or CSV) and select suitable delivery frequency (quarterly, monthly, or weekly).

Coresignal is a leading public business data provider in the web data sphere with an extensive focus on firmographic data and public employee profiles. More than 3B data records in different categories enable companies to build data-driven products and generate actionable insights. Coresignal is exceptional in terms of data freshness, with 890M+ records updated monthly for unprecedented accuracy and relevance.

RampedUp can help with many data initiatives. Below are several popular use-cases: how we can help with several widely used Use-Cases: • Enrichment – Append data you are missing on for the contacts in your database. • Validation – Assess accuracy of the contacts and emails in your database. • Recovery – Track down contacts that have changed jobs and recover them at their new jobs so you can continue the relationship at the new company. • Compliance – Flag contacts in regions that need an opt-ins, allowing you to ensure that you are compliant with region laws such as CASL, GDPR and CCPA. • Net New Leads – Build out an Ideal Buyer Profile based on your data to quantify the number of additional look-alike, key decision makers we can supply • Digital Advertising - Get a higher audeince match rate using RampedUp • Personal Email Addresses –Identify personal emails in your dataset and quantify those that we can append with professional information

At Thomson Data, we help businesses clean up and manage messy B2B databases to ensure they are up-to-date, correct, and detailed. We believe your sales development representatives and marketing representatives should focus on building meaningful relationships with prospects, not scrubbing through bad data.

Here are the key steps involved in our B2B data cleansing process:

1. Data Auditing: We begin with a thorough audit of the database to identify errors, gaps, and inconsistencies, which majorly revolve around identifying outdated, incomplete, and duplicate information.

2. Data Standardization: Ensuring consistency in the data records is one of our prime services; it includes standardizing job titles, addresses, and company names. It ensures that they can be easily shared and used by different teams.

3. Data Deduplication: Another way we improve efficiency is by removing all duplicate records. Data deduplication is important in a large B2B dataset as multiple records from the same company may exist in the database.

4. Data Enrichment: After the first three steps, we enrich your data, fill in the missing details, and then enhance the database with up-to-date records. This is the step that ensures the database is valuable, providing insights that are actionable and complete.

What are the Key Benefits of Keeping the Data Clean with Thomson Data’s B2B Data Cleansing Service? Once you understand the benefits of our data cleansing service, it will entice you to optimize your data management practices, and it will additionally help you stay competitive in today’s data-driven market.

Here are some advantages of maintaining a clean database with Thomson Data:

1. Better ROI for your Sales and Marketing Campaigns: Our clean data will magnify your precise targeting, enabling you to strategize for effective campaigns, increased conversion rate, and ROI.

2. Compliant with Data Regulations:
   The B2B data cleansing services we provide are compliant to global data norms.

3. Streamline Operations: Your efforts are directed in the right channel when your data is clean and accurate, as your team doesn’t have to spend their valuable time fixing errors.

To summarize, we would again bring your attention to how accurate data is essential for driving sales and marketing in a B2B environment. It enhances your business prowess in the avenues of decision-making and customer relationships. Therefore, it is better to have a proactive approach toward B2B data cleansing service and outsource our offerings to stay competitive by unlocking the full potential of your data.

Send us a request and we will be happy to assist you.

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

This data set contains a summary of information about candidate campaigns and political committees by election year. For candidate campaigns and single-year/election committees, a single record is provided that covers all activity of the campaign for the given election year. Information for continuing political committees is summarized by calendar/reporting year. The data set covers that prior 16 years plus the current election year. The data are compiled from the campaign reports deposit (C3), campaign summary reports (C4), campaign registrations (C1/C1pc) and candidate declarations and elections data provided to the PDC by the Washington Secretary of State. Records are updated in near real-time, typically less than 2 minutes from the time the campaign submits new data. This dataset is a best-effort by the PDC to provide a complete set of records as described herewith. The PDC provides access to the original reports for the purpose of record verification. Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements. CONDITION OF RELEASE: This publication and or referenced documents constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

The World War II Enlistment and Casualty Records data set contains individual-level information on soldiers who were drafted or volunteered for service in the U.S. armed forces during World War II. The repository consists of three files: The digitized list of fallen soldiers who served in the U.S. Army or Army Air Force by name, state, and county of residence (300,131 observations) The digitized list of fallen soldiers who served in the U.S. Navy, Marine Corps, or Coast Guard by name, state, and county of residence (65,507 observations) The World War II Army and Army Air Force Enlistment records which were merged with the list of fallen soldiers (8,293,187 observations)

This dataset represents a group of paper records (a "series") within the Harvard School of Public Health Harvard Prevention Research Center records, 1992-2003 (inclusive), 1994-2003 (bulk), which can be accessed on-site at the Center for the History of Medicine at the Francis A. Countway Library of Medicine in Boston, Massachusetts. The series consists primarily of raw data fitness questionnaires completed by students during the Harvard Prevention Research Center's randomized control trial of the Planet Health curriculum. Questionnaires were called Fitness Folders during the first year of the trial (1995-1996) and FitChecks during the following year (1996-1997), although both collected the same information: amount of time spent on physical ("fit") activities and stationary ("sit") activities; fitness goals; and activity self-evaluation. Questionnaires were completed at middle schools in several Massachusetts cities and towns, including Cambridge, Framingham, Lynn, and Somerville. Subseries also includes: analyzed questionnaire data; blank questionnaires and questionnaire instructions; administrative and program planning correspondence; and fitness folder drafts and related production correspondence. Attached to the "Fitcheck and Fitness Folder Records, 1994-1997" dataset are files, digitized from their original paper copies, that serve as examples of the records that may be found in the series. Additional data and associated records are accessible onsite at the Center for the History of Medicine per the conditions governing access described below. Conditions Governing Access to Original Collection Materials: The series represented by this dataset includes health information that is restricted for 80 years from the date of record creation, and Harvard University records that are restricted for 50 years from the date of record creation. Researchers should contact Public Services for more information. The Harvard School of Public Health Harvard Prevention Research Center records were processed with grant funding from the Andrew W. Mellon Foundation, as awarded and administered by the Council on Library and Information Resources (CLIR) in 2016. View the Harvard Prevention Research Center Records finding aid for a full collection inventory of both paper and digital records, and for more information about accessing and using the collection.

This database comprises occurrence data, including both specimen and observation data, obtained from the whole distribution range (native and invaded) for the freshwater snail Pomacea canaliculata (Caenogastropoda: Ampullariidae). This species is native from lower Del Plata basin in South America but, together with other congeners collectively known as "apple snails", were introduced to many regions outside their natural ranges where they rapidly spread out, causing serious damage to aquatic crops and also to biodiversity and functioning of natural wetlands.

The aim of this publication is to provide an open access, updated and accurate database of P. canaliculata records worldwide, available for use in ecological studies and pest management, focusing on discriminate misidentifications with other apple snails. This database includes 718 records of P. canaliculata from 29 countries distributed in Africa, South America, North America, Asia and Pacific Islands, and were reported from the early 20th century until present day.

The records reported here were compiled from different sources: - Our personal records which include samples collected during the past 25 years covering a large area of many provinces in Argentina. - Available bibliography, searching for any reliable report mentioning geographic coordinates or at least a precise locality, excluding those with doubtful identity or not determined records such as “Pomacea” or “Pomacea sp.”. - By request to several researchers with expertise in this species around the world to provide us records and also their expert opinion to discard records corresponding to other congeners (especially the often-confounded P. maculata).