A collection of 5 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

Brain areas related to CS cue presentation (Shocked contrast)

These data represent a set of capture histories of rainbow trout (Oncorhynchus mykiss or RBT) captured in the Colorado River (CR) and(or) detected on the multiplexer array in the Little Colorado River (LCR). Capture trips to the Colorado River occurred in April 2012, July 2012, September 2012, January 2013, April 2013, July 2013, September 2013, January 2014, April 2014, July 2014, and September 2014. Rainbow trout were detected on the PIT array system (MUX) from October 2013 - April 2014.

List of mines and quarries in the UK including information about operational status, products, lithostratigraphy, chronostratigraphy, pit and operator addresses, minerals planning authority. Digital data has been sold from the BritPits database, since 1994, this has been customised to suit purchasers. Use is also made of sets of operational workings data by Bureau Services who pay royalties and get updates. Older data on operators tends to be incomplete as it was not recorded. Updating is ongoing to update litho- and chronostrat data. Originally, only details of currently active sites were included in the database but, because of the importance of former workings for waste disposal and as Sites of Special Scientific Interest, information is now collected on both inactive and closed operations. The data is held in a relational database using an Oracle server and a Microsoft Access front-end. The database can be used for many purposes: mailing lists, route planning, market intelligence/analysis, and resource planning, and data has been supplied to a wide range of customers.

SAND_GRAVEL_PITS_ABANDONED_IN is a point shapefile that shows the locations of abandoned sand and gravel pits in Indiana. It is derived from an unpublished memorandum report of the Indiana Geological Survey (IGS): Hasenmueller, W. A., 2001, Preliminary database of abandoned sand and gravel pits, Coal and Industrial Minerals Section Memorandum Report 98, Indiana Geological Survey, Bloomington, 3 p., and a CD-ROM. NOTE: This shapefile includes 2,275 of 2,515 sand and gravel pit locations from the IGS Abandoned Sand and Gravel Pit Database (titled "PrelimSdGvDB.mdb") that are located in Indiana and for which the documented location is to the nearest quarter quarter section. The 240 pit locations that were excluded did not have adequate quarter quarter section information that could be processed by Geographix software. The following discussion is derived from Memorandum Report 98: "The Microsoft Access 2000 database PrelimSdGvDB.mdb on the enclosed CD contains information about abandoned sand and gravel pits in Indiana. The data contained in this database represents a preliminary stage in a comprehensive compilation of abandoned sand and gravel pit data stored in the files of the Mineral Resources Section of the Indiana Geological Survey (IGS). The data were taken from a single Mineral Resources Section file titled "DATA TABULATION CARDS: Sand and Gravel Pits" which documents abandoned sand and gravel pits examined by IGS geologists in the late 1940s and early 1950s. Although these data have been assembled into a simple flat-file database they should not be regarded as a final compilation of the above mentioned file nor should they be regarded as a compilation of all abandoned sand and gravel pit information filed in IGS Mineral Resources Section files. The Mineral Resources Section is providing this preliminary database in response to a request from the Energy Resources Section of the IGS."

SAND_GRAVEL_PITS_ABANDONED_IN is a point shapefile that shows the locations of abandoned sand and gravel pits in Indiana. It is derived from an unpublished memorandum report of the Indiana Geological Survey (IGS): Hasenmueller, W. A., 2001, Preliminary database of abandoned sand and gravel pits, Coal and Industrial Minerals Section Memorandum Report 98, Indiana Geological Survey, Bloomington, 3 p., and a CD-ROM. NOTE: This shapefile includes 2,275 of 2,515 sand and gravel pit locations from the IGS Abandoned Sand and Gravel Pit Database (titled "PrelimSdGvDB.mdb") that are located in Indiana and for which the documented location is to the nearest quarter quarter section. The 240 pit locations that were excluded did not have adequate quarter quarter section information that could be processed by Geographix software. The following discussion is derived from Memorandum Report 98: "The Microsoft Access 2000 database PrelimSdGvDB.mdb on the enclosed CD contains information about abandoned sand and gravel pits in Indiana. The data contained in this database represents a preliminary stage in a comprehensive compilation of abandoned sand and gravel pit data stored in the files of the Mineral Resources Section of the Indiana Geological Survey (IGS). The data were taken from a single Mineral Resources Section file titled "DATA TABULATION CARDS: Sand and Gravel Pits" which documents abandoned sand and gravel pits examined by IGS geologists in the late 1940s and early 1950s. Although these data have been assembled into a simple flat-file database they should not be regarded as a final compilation of the above mentioned file nor should they be regarded as a compilation of all abandoned sand and gravel pit information filed in IGS Mineral Resources Section files. The Mineral Resources Section is providing this preliminary database in response to a request from the Energy Resources Section of the IGS."

Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set contains snow pit data collected over sea ice in the Barrow, Alaska, USA area and nearby at the Navy Ice Camp in the main pack ice of the Arctic Ocean.

The BCPITTAGS database is used to store data from an Oncorhynchus mykiss (steelhead/rainbow trout) population dynamics study in Big Creek, a coastal stream along the Big Sur coast in Monterey County, California. The Landscape Ecology team at the Fisheries Ecology Division in Santa Cruz, CA is investigating the life history of this relatively small O. mykiss population to determine its significance in the persistence of the larger South-Central California Coast Steelhead Distinct Population Segment (DPS), which includes anadromous O. mykiss populations from the Pajaro River up to (but not including) the Santa Maria River, to see how these small coastal streams with little human-related impacts may contribute to DPS viability and resiliency. The database stores data from mark-recapture surveys, fish movement data collected via instream PIT tag readers, and stream environmental data. The data will be assimilated into a stage-structured population model, where stages include life history stage and location. Movement and survival rates will be determined and analyzed using data from the stationary PIT tag readers and mobile tracking devices.

The PIT-tagging project on NEA mackerel was initiated in 2011 by the Institute of Marine Research (IMR) in Bergen, Norway, with the main purpose to use the data as a basis for stock assessment and exploring migratory behaviour. RFID is a technology that uses radio waves to transfer data from an electronic tag through a reader for the purpose of identifying and tracking the object. The RFID tags used for tagging mackerel are passive, commonly called PIT-tags (Passive Integrated Transponders), specifically developed for tagging fish and animals. They are made of biocompatible glass, the specific type used for mackerel is ISO FDX-B 134,3 kHz, 3.85x23mm glass tags. The mackerel have been PIT-tagged during a month-long survey at spawning grounds off Ireland-Hebrides in May on annual basis from 2011 onwards, and data from experiments off Iceland and Norway are also available. The mackerel are typically captured by jigging from rented purse seine vessels, kept for up to a maximum of 30 minutes in 1 m diameter round tanks with running water, before they are measured (length and weight), tag injected into the abdomen and released through pipes with running water. All PIT-tagging experiments are approved by the Norwegian Animal Research Authority. During tagging, there is a combined PC-reader system, where each unique tag ID is recorded together with the body length and other details, which are synchronized with the IMR database over internet. IMR has developed monitoring systems where antennas are specially designed for pipes (round antennas) or conveyor belt systems (flat antennas) to detect the tagged fish as they are pumped from fishing vessels or during processing inside factories producing mackerel for human consumption. Such systems have over the years been established in Norway, Iceland, Faroes and Scotland, scanning a large proportion of total landings. These antennas are connected with readers and PCs, and detections of tagged fish are automatically updated in real-time to the database at IMR over internet. The PIT-tag time series is available through a series of APIs, JSON links, to both data of the fish released and recaptured, as well as detailed data on the catches scanned and biological data of both released fish and scanned fish. All these data are needed to estimate numbers of mackerel released per year class, and numbers scanned and recaptured per year class in the years after release, which serve as the basis for use in the stock assessment.

Comprehensive dataset of 20 Gravel pits in Finland as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This layer shows location of drainage pits within the Metropolitan Region and is provided for information only. A Pit is defined as a node in the drainage network. It includes street gullies, side entry pits, soak wells, junction pits, and gross pollutant traps etc. It can be owned by Main Roads WA or others.Data dictionary:FieldAliasTypeDescriptionPit_NoPit NoString“Road_No” & ”.“ & Unique Database Number e.g. H015.569Asset_OwnerAsset OwnerStringOwner of the asset e.g. Main RoadsRoad_No Road NoStringMRWA Road NumberAsset_StatusAsset StatusStringFunctionality of asset (Operational or Non-operationalData_StatusData StatusStringField survey verification e.g. Verified / Asset Captured via Desktop onlyPit_TypePit TypeStringType of Pit (largely as per D-SPEC)FSLFSLDoubleCover level, Finished Surface Level (FSL) of Pit in metres AHDDepthDepthDoubleNatural or FSL to bottom of pit in mmMapping_CommentsMapping CommentsStringAny additional comments that relate to this pipe section including assumptionsConfidence_RatingConfidence RatingStringA confidence level assigned based on the source data used to digitise the featureDisclaimer:1. Please note that Main Roads WA drainage data is provided for information only and the data may be inaccurate, incomplete or out-of-date. Please use as a reference only and verification is to be undertaken on site.2. Confidence Rating has been assigned to individual assets to provide an indication of the accuracy of the data. However please note that this is only an estimated rating which should be used as a reference only.3. Asset Owner – Please note that Main Roads WA drainage data includes multiple organisations’ interface drainage and, as a result, the ‘Asset Owner’ attribute is only to be used as a reference only as it may be inaccurate and should not be referred to as the source of truth.4. Asset Types:a. Interfacing Structuresi. Note that the drainage data includes interface points between Main Roads WA drainage structures and structures owned by other asset owners. However not all assets owned by other asset owners have been included. Please seek clarification from the respective asset owners if required.b. Ghost Structure / Nodei. Ghost Node Structure represents a physical structure where the asset type is not known and Ghost Node represents a node only that is not owned by Main Roads WA. Any assets owned by other organisations beyond a direct connection can be represented by the use of a Ghost Link. Note that you are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes.Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material: “The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability.”Creative Commons CC BY 4.0 https://creativecommons.org/licenses/by/4.0/

Comprehensive dataset of 1 Gravel pits in State of Rondônia, Brazil as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 2 Gravel pits in Province of Potenza, Italy as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This dataset includes all surface, snowpit, crust, AWS, and near-surface thermistor data used for analyses and publication "Surface formation, preservation, and history of low-porosity crusts at the WAIS Divide site, West Antarctica" DOI: https://doi.org/10.5194/tc-12-1-2018

This data is a spatial representation of stormwater drainage pits within the City of Charles Sturt Council. Stormwater Pits are the inflowing element where the stormwater enters the water diversion network. The Stormwater infrastructure provides the community with infrastructure to transfer stormwater to receiving bodies such as River Torrens, West Lakes and the Gulf, to minimise suburban flooding and in some cases to encourage the capture of stormwater for re-use. Charles Sturt has over 13,500 of pits. This data was originally captured by internal audit in 2002 and is continually maintained by the Asset Planning Management team to ensure the data represents an accurate representation of the asset network

This is an ongoing Bonneville Power Administration funded project to annually collect, PIT tag, and release wild Chinook salmon parr in up to 15 streams of the Salmon River drainage in Idaho and subsequently monitor these fish through in-stream monitoring sites and downstream dams. The overall study objectives are to assess the migrational characteristics and estimate parr-to-smolt survival for...

This dataset contains linework of lineaments mapped on 4 <1-m-resolution lidar datasets and the 10-m-resolution National Elevation Dataset digital elevation models in the Pit River region of northeastern California. Lineaments are classified by confidence in tectonic origin, map certainty, and the ages of the bedrock and surficial deposits they cross.

This dataset brings together comprehensive race data from the Formula 1 seasons 2018 to 2024. It includes detailed telemetry on pit stops, lap times, stint lengths, weather conditions, driver behavior, tire compounds, and more. Whether you're analyzing strategy shifts, driver aggression, or pit efficiency, this dataset is a goldmine for motorsport enthusiasts, data scientists, and machine learning practitioners.

Perfect for:

Building race prediction models

Evaluating pit strategies

Understanding tire and stint dynamics

Creating data-driven visual dashboards

Pls do upvote if you love the work !!!

This report displays the data communities reported to HUD about the nature of and amount of persons who are homeless as part of HUD's Point-in-Time (PIT) Count. This data is self-reported by communities to HUD as part of its competitive Continuum of Care application process. The website allows users to select PIT data from 2005 to present. Users can use filter by CoC, states, or the entire nation.

United Kingdom Production Industries Turnover (PIT): Manufacturing data was reported at 49,413.700 GBP mn in Feb 2025. This records an increase from the previous number of 47,630.800 GBP mn for Jan 2025. United Kingdom Production Industries Turnover (PIT): Manufacturing data is updated monthly, averaging 35,618.150 GBP mn from Jan 1998 (Median) to Feb 2025, with 326 observations. The data reached an all-time high of 56,561.800 GBP mn in Mar 2023 and a record low of 25,389.200 GBP mn in Aug 2003. United Kingdom Production Industries Turnover (PIT): Manufacturing data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s United Kingdom – Table UK.C001: Industrial Turnover Value: Production. [COVID-19-IMPACT]

This dataset includes Point-in-Time (PIT) data collected in Cambridge between 2012 and 2024. The PIT count is a count of sheltered and unsheltered homeless persons on a single night in January. The U.S. Department of Housing and Urban Development (HUD) requires that communities receiving funding through the Continuum of Care (CoC) Program conduct an annual count of homeless persons on a single night in the last 10 days of January, and these data contribute to national estimates of homelessness reported in the Annual Homeless Assessment Report to the U.S. Congress. This dataset is comprised of data submitted to, and stored in, HUD’s Homelessness Data Exchange (HDX).

This dataset includes basic counts and demographic information of persons experiencing homelessness on each PIT date from 2012-2024. The dataset contains four rows for each year, including one row for each housing type: Emergency Shelter, Transitional Housing, or Unsheltered. The dataset also includes housing inventory counts of the number of shelter and transitional housing units available on each of the PIT count dates.

Information about persons staying in emergency shelters and transitional housing units is exported from the Homeless Management Information System (HMIS), which is the primary database for recording client-level service records. Information about persons in unsheltered situations is compiled by first conducting an overnight street count of persons observed sleeping outdoors on the PIT night to establish the total number of unsheltered persons. Demographic information for unsheltered persons is then extrapolated by utilizing assessment data collected by street outreach workers during the 7 days following the PIT count.