This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

Fire Stations in Oregon Any location where fire fighters are stationed or based out of, or where equipment that such personnel use in carrying out their jobs is stored for ready use. Fire Departments not having a permanent location are included, in which case their location has been depicted at the city/town hall or at the center of their service area if a city/town hall does not exist. This dataset includes those locations primarily engaged in forest or grasslands fire fighting, including fire lookout towers if the towers are in current use for fire protection purposes. This dataset includes both private and governmental entities. Fire fighting training academies are also included. This dataset is comprised completely of license free data. The Fire Station dataset and the EMS dataset were merged into one working file. TGS processed as one file and then separated for delivery purposes. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this field, the oldest record dates from 07/11/2006 and the newest record dates from 06/02/2008.

Fire Stations in New Mexico Any location where fire fighters are stationed or based out of, or where equipment that such personnel use in carrying out their jobs is stored for ready use. Fire Departments not having a permanent location are included, in which case their location has been depicted at the city/town hall or at the center of their service area if a city/town hall does not exist. This dataset includes those locations primarily engaged in forest or grasslands fire fighting, including fire lookout towers if the towers are in current use for fire protection purposes. This dataset includes both private and governmental entities. Fire fighting training academies are also included. This dataset is comprised completely of license free data. The Fire Station dataset and the EMS dataset were merged into one working file. TGS processed as one file and then separated for delivery purposes. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this field, the oldest record dates from 01/31/2005 and the newest record dates from 07/17/2008.

This data release contains data summarizing observations within and adjacent to the Tadpole Fire, which burned from 6 June to 4 July 2020. In particular, this monitoring data were focused on debris flows triggered on 8 September 2020 in four drainage basins (TAD1, TAD2, TAD3, and TAD4). Rainfall data (1a_rain_geophones.csv) are provided in a comma-separated value (CSV) file. The columns in the csv file are: Index, GaugeID (name of rain gauge), StormID (the storm number starting at the first record, where a new storm is defined by 8 hours with no rainfall), TimeStamp (local time), Bin Accum (mm) (The total accumulated rainfall between timesteps in units of millimeters), TotalAccum (mm) (the cumulative rainfall starting from the beginning of the record), 5-minute Intensity (mm/h) (the 5-minute rainfall intensity), 10-minute Intensity (mm/h) (the 10-minute rainfall intensity), 15-minute Intensity (mm/h) (the 15-minute rainfall intensity), 30-minute Intensity (mm/h) (the 30-minute rainfall intensity), and 60-minute Intensity (mm/h) (the 60-minute rainfall intensity). The location of the rain gage is: 32.955, -108.232. Rainfall data (1b_rain_only.csv) are provided in a comma-separated value (CSV) file. The columns in the csv file are: Index, GaugeID (name of rain gauge), StormID (the storm number starting at the first record, where a new storm is defined by 8 hours with no rainfall), TimeStamp (local time), Bin Accum (mm) (The total accumulated rainfall between timesteps in units of millimeters), TotalAccum (mm) (the cumulative rainfall starting from the beginning of the record), 5-minute Intensity (mm/h) (the 5-minute rainfall intensity), 10-minute Intensity (mm/h) (the 10-minute rainfall intensity), 15-minute Intensity (mm/h) (the 15-minute rainfall intensity), 30-minute Intensity (mm/h) (the 30-minute rainfall intensity), and 60-minute Intensity (mm/h) (the 60-minute rainfall intensity).The location of each rain gage station is: 32.956, -108.241. Geophone data (2_geophone.csv) are provided in a comma-separated value (CSV) file. The columns in the csv file are: TimeStamp (local time), GeophoneUp_mV (the upstream geophone data in millivolts), GeophoneDn_mV (the downstream geophone data in millivolts). The geophones are co-located with a rain gage at: 32.955, -108.232. Field measurement data (3_combined_data.csv) are provided in a comma-separated value (CSV) file. This dataset describes pieces of wood found within different debris flow deposits in four drainages TAD1-TAD4, and there were multiple debris flow deposits in each drainage. The columns in the csv file are: ID (a unique identifier for each wood piece). For example, if there is one piece of wood at a location in the channel TAD1, the wood piece was mapped as TAD1-1. However, in the case of a single debris flow deposit with multiple pieces of wood, a letter is appended for each additional wood piece, such as TAD1-1a, TAD1-1b, TAD1-1c, etc.), ID_base (a unique identifier for each deposit, which may contain multiple wood pieces), Latitude (the Latitude expressed in Decimal Degrees), Longitude the Longitude expressed in Decimal Degrees), Elevation (the elevation expressed in meters), Length (m) (the length of a wood piece in meters), Diameter (cm) (the diameter of the approximate middle of a wood piece in centimeters), Class (a description of the wood piece), Charred (%) (the percent of the wood piece that was charred by fire), Trapped Sediment (m3) (the total volume of sediment in a debris flow deposition cubic meters), Timing (this is a description of when the wood was deposited with respect to the debris flow. The options are Before, During, or After), Pinned (this indicates wood was pinned against an obstacle or not. If it is pinned, the item is named, otherwise it is labeled as no), Roots/Branches (here indicate either if the roots or branches where still attached to the wood, otherwise it is labeled as no), Orientation (in some locations, the qualitative orientation of the wood with respect to the flow direction is noted), Channel Width (m) (measurements of channel width in meters), Flow Depth (m) (measurements of flow depth in meters), Slope (deg) (the slope value in degrees obtained by selecting the raster slope value from a 1 m lidar underneath the observation point), Lidar Width (m) (the channel width in meters measured 1 meter above the lowest point in the channel), Drainage Area (m2) (the upstream contributing drainage area for at each measurement point), Notes (any notes from the site). Photographic data (4_GameCameraPhotos.zip) are presented from a camera located approximately 25 m from the geophones, focusing on the channel monitored by the geophones. The photos are in a .jpg format and are catalogued by date using the date format (ddMMMYYYY).

Abstract The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived. A line shapefile of the Hunter subregion boundary with line segments attributed with the biophysical feature/dataset that defines that section of the boundary. This dataset is derived from the …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived. A line shapefile of the Hunter subregion boundary with line segments attributed with the biophysical feature/dataset that defines that section of the boundary. This dataset is derived from the Bioregional Assessment areas and links to the source datasets are in the lineage field of this metadata statement. Purpose To identify the underlying source used to define the boundary. Mostly the Bioregion boundary was used but some sections are defined by geology and CMA boundaries.For report map purposes. Dataset History A polygon shapefile of the Hunter subregion was converted to a line shapefile. The subregion boundary was then compared with the datasets that the subregion metadata listed as boundary sources (see lineage). The subregion boundary line was split (ArcGIS Editor Split tool) into sections that coincided with the source boundary layers and attributed accordingly. Dataset Citation Bioregional Assessment Programme (2014) Hunter bioregion boundary definition sources. Bioregional Assessment Derived Dataset. Viewed 07 February 2017, http://data.bioregionalassessments.gov.au/dataset/3052c699-3b0d-4504-95e3-18598147c5ae. Dataset Ancestors Derived From Bioregional Assessment areas v02 Derived From Australian Coal Basins Derived From Natural Resource Management (NRM) Regions 2010 Derived From Bioregional Assessment areas v03 Derived From Bioregional Assessment areas v01 Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From GEODATA TOPO 250K Series 3 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Geological Provinces - Full Extent

This dataset is an authoritative inventory of new housing units constructed in the City of Saint Paul from 2010 through the end of Q1 2025. The data originates from two sources: the City's permitting system, and from the City's records on housing affordability. The dataset helps provide a deeper understanding of trends in market rate and affordable housing production. This dataset is updated quarterly, generally by the 15th of the month following the end of each quarter.For the purposes of this dataset, the delineation of "affordable units" is tied to the construction of the new units: does the project — its development financing or the regulatory framework under which it was built — require units be affordable upon the completion of construction?
This definition of affordability does not include units that are affordable only because of a post-construction subsidy or other similar subsequent commitment to affordability, such as through the city's Rental Rehab Loan Program or 4d Affordable Housing Incentive Program. It does, however, include units that are affordable under the terms of zoning district-based density bonuses for affordability. Projects built under a zoning-based density bonus currently comprise a very small portion of the larger total, and are identified in the Notes column of the associated table.This dataset will be updated quarterly, given the manual work currently involved in bringing it up-to-date. It is the product of work over five years across three City departments.Field definitions are available below. In addition to being available for download through the Open Information website, this data is perhaps more easily accessible in an interactive Housing Production Dashboard.This data is designed under a methodology specific to the City of Saint Paul. Other government entities use the same originating permit data, but somewhat divergent methodologies, which can produce very different results. We believe this particular methodology gives the fullest and most timely depiction of housing production available. For specific details, see the "Methodologies Compared" tab at the bottom of the Housing Production Dashboard.Technical detailsThis dataset is generally designed to have one record (row) per building project that creates new units. A project may be the result of one or more building permits. In cases when a project contains both subsidized / affordable and unsubsidized / market rate units, the project is split across two records (rows).

Fields (Columns) Defined

PropertyRSN: An internal unique identifier for the address point with which the permit is associated.

Property Address: The street address at which the permit work took place.

ParcelID: The county-assigned unique identifier for the parcel on which the permit work took place.

Type of Work: The kind of work undertaken at the site. CHOICES: New · Addition · Remodel

Residence Type: What is the physical form of the dwelling units that were created under this building permit? CHOICES: 2-Family/Duplex · Mixed (Commercial/Residential) · Residential (Multi-Fam) · Single Family DwellingDwelling Unit Type: The type of financial structure tied to the new dwelling units created under this permit. CHOICES:Market Rate Unit: Units that did not receive some sort of direct public subsidy or assistance outside normal market sources.Affordable Unit: Units that contractually ensure affordability / access for those in need, at the level of 80% of Area Median Income (AMI) and below. This definition does include units that are affordable under the terms of zoning-based density bonuses, which comprise a very small portion of the overall total. This demarcation of affordable units does not include units that received financial assistance in preparing the site for redevelopment, for activities such as pollution remediation. Further, the affordability included here are only those contractually included at the closing of the development financing of the project, and does not include units restricted as affordable at a later date, such as through the City's 4(d) Affordable Housing Incentive Program, or the Rental Rehab Loan Program.

Commercial to Housing Conversion: The units shown were produced by converting formerly commercial space (including retail, commercial, institutional and industrial type uses) into residential space (including single family, duplex, 3-4 unit, multifamily and congregate-type residential uses). CHOICES:Yes: The housing units shown were converted from commercial space.No: The housing units shown were not converted from commercial space.Project Permit Issue Date: The date the first permit was issued for the project that created the new dwelling units.

Project Permit Issue Year: The year the first permit was issued for the project that created the new dwelling units.

Existing Dwelling Units: The number of dwelling units that existed just prior to the start of the project under the definition of "dwelling unit" in the International Building Code.

New Dwelling Units: The number of new dwelling units created under the building permit(s) under the definition of "dwelling unit" in the International Building Code.

Total Final Dwelling Units: The number of dwelling units existing upon completion of the associated building permit(s), under the definition of "dwelling unit" in the International Building Code.

Notes: This field contains notes on specific unique circumstances. In particular, a few building permits produced both subsidized / affordable and unsubsidized / market rate dwelling units. To make building permits in this scenario function as needed within data systems, we split such permits into two lines, one for each type of unit, and made a notation in this field to reflect that division.

This dataset is about book subjects, has 1 rows. and is filtered where the books includes When parents split up : divorce explained to young people. It features 10 columns including book subject, number of authors, number of books, earliest publication date, and latest publication date. The preview is ordered by number of books (descending).

HSIP Local Emergency Operations Centers in the United States "The physical location at which the coordination of information and resources to support domestic incident management activities normally takes place. An Emergency Operations Center may be a temporary facility or may be located in a more central or permanently established facility, perhaps at a higher level of organization within a jurisdiction. Emergency Operations Centers may be organized by major functional disciplines (e.g., fire, law enforcement, and medical services), by jurisdiction (e.g., Federal, State, regional, county, city, tribal), or some combination thereof." (Excerpted from the National Incident Management System) The GFI source for this layer contains State and Federal Emergency Operations Centers in addition to local Emergency Operations Centers. This dataset contains these features as well. In cases where an Emergency Operations Center has a mobile unit, TechniGraphics captured the location of the mobile unit as a separate record. This record represents where the mobile unit is stored. If this location could not be verified, a point was placed in the approximate center of the Emergency Operations Centers service area. Effort was made by TechniGraphics to verify whether or not each Emergency Operations Center has a generator on-site and whether or not the Emergency Operations Center is located in a basement. This information is indicated by the values in the [GENERATOR] and [BASEMENT] fields respectively. In cases where more than one record existed for a geographical area (e.g., county, city), TechniGraphics verified whether or not one of the records represented an alternate location. This was indicated by appending "-ALTERNATE" to the value in the [NAME] field. Some Emergency Operations Centers are located at private residences. The [TYPE] field was manually evaluated during the delivery process to compare the records in which the [NAME] field contained "-ALTERNATE". In cases where these values contradicted information that was verified by TechniGraphics (e.g. [NAME] contained "-ALTERNATE" and [TYPE] = "PRIMARY"), the value in the [TYPE] field was changed to match the type indicated by the [NAME] of the verified record. TechniGraphics did not change values in this field if the type was not verified. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard HSIP fields that TechniGraphics populated. Double spaces were replaced by single spaces in these same fields. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this attribute, the oldest record dates from 08/28/2009 and the newest record dates from 11/18/2009.Homeland Security Use Cases: Use cases describe how the data may be used and help to define and clarify requirements. 1. A resource for preparing, mitigating, responding to and recovering from an emergency. 2. A list of resources to draw upon by surrounding areas when local resources have temporarily been overwhelmed by a disaster. 3. A resource for Emergency Management planning purposes. 4. A resource for catastrophe response to aid in the retrieval of equipment by outside responders in order to deal with the disaster. 5. A resource for situational awareness planning and response for Federal Government events.

Purpose and Scope

The AstroChat dataset is a collection of 901 dialogues, synthetically generated, tailored to the specific domain of Astronautics / Space Mission Engineering. This dataset will be frequently updated following feedback from the community. If you would like to contribute, please reach out in the community discussion.

Intended Use

The dataset is intended to be used for supervised fine-tuning of chat LLMs (Large Language Models). Due to its currently limited size, you should use a pre-trained instruct model and ideally augment the AstroChat dataset with other datasets in the area of (Science Technology, Engineering and Math).

Quickstart

To be completed

DATASET DESCRIPTION

Access

• Manual download from Hugging face hub: https://huggingface.co/datasets/patrickfleith/AstroChat
• Or with python: python from datasets import load_dataset dataset = load_dataset("patrickfleith/AstroChat")

Structure

901 generated conversations between a simulated user and AI-assistant (more on the generation method below). Each instance is made of the following field (column): - id: a unique identifier to refer to this specific conversation. Useeful for traceability purposes, especially for further processing task or merge with other datasets. - topic: a topic within the domain of Astronautics / Space Mission Engineering. This field is useful to filter the dataset by topic, or to create a topic-based split. - subtopic: a subtopic of the topic. For instance in the topic of Propulsion, there are subtopics like Injector Design, Combustion Instability, Electric Propulsion, Chemical Propulsion, etc. - persona: description of the persona used to simulate a user - opening_question: the first question asked by the user to start a conversation with the AI-assistant - messages: the whole conversation messages between the user and the AI assistant in already nicely formatted for rapid use with the transformers library. A list of messages where each message is a dictionary with the following fields: - role: the role of the speaker, either user or assistant - content: the message content. For the assistant, it is the answer to the user's question. For the user, it is the question asked to the assistant.

Important See the full list of topics and subtopics covered below.

Metadata

Dataset is version controlled and commits history is available here: https://huggingface.co/datasets/patrickfleith/AstroChat/commits/main

Generation Method

We used a method inspired from Ultrachat dataset. Especially, we implemented our own version of Human-Model interaction from Sector I: Questions about the World of their paper:

Ding, N., Chen, Y., Xu, B., Qin, Y., Zheng, Z., Hu, S., ... & Zhou, B. (2023). Enhancing chat language models by scaling high-quality instructional conversations. arXiv preprint arXiv:2305.14233.

Step-by-step description

• Defined a set of user persona
• Defined a set of topics/ disciplines within the domain of Astronautics / Space Mission Engineering
• For each topics, we defined a set of subtopics to narrow down the conversation to more specific and niche conversations (see below the full list)
• For each subtopic we generate a set of opening questions that the user could ask to start a conversation (see below the full list)
• We then distil the knowledge of an strong Chat Model (in our case ChatGPT through then api with gpt-4-turbo model) to generate the answers to the opening questions
• We simulate follow-up questions from the user to the assistant, and the assistant's answers to these questions which builds up the messages.

Future work and contributions appreciated

• Distil knowledge from more models (Anthropic, Mixtral, GPT-4o, etc...)
• Implement more creativity in the opening questions and follow-up questions
• Filter-out questions and conversations which are too similar
• Ask topic and subtopic expert to validate the generated conversations to have a sense on how reliable is the overall dataset

Languages

All instances in the dataset are in english

Size

901 synthetically-generated dialogue

USAGE AND GUIDELINES

License

AstroChat © 2024 by Patrick Fleith is licensed under Creative Commons Attribution 4.0 International

Restrictions

No restriction. Please provide the correct attribution following the license terms.

Citation

Patrick Fleith. (2024). AstroChat - A Dataset of synthetically generated conversations for LLM supervised fine-tuning in the domain of Space Mission Engineering and Astronautics (Version 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11531579

Update Frequency

Will be updated based on feedbacks. I am also looking for contributors. Help me create more datasets for Space Engineering LLMs :)

Have a feedback or spot an error?

Use the ...

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

Asset database for the Hunter subregion on 24 February 2016 (V2.5) supersedes the previous version of the HUN Asset database V2.4 (Asset database for the Hunter subregion on 20 November 2015, GUID: 0bbcd7f6-2d09-418c-9549-8cbd9520ce18). It contains the Asset database (HUN_asset_database_20160224.mdb), a Geodatabase version for GIS mapping purposes (HUN_asset_database_20160224_GISOnly.gdb), the draft Water Dependent Asset Register spreadsheet (BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20160224.xlsx), a data dictionary (HUN_asset_database_doc_20160224.doc), and a folder (NRM_DOC) containing documentation associated with the Water Asset Information Tool (WAIT) process as outlined below. This version should be used for Materiality Test (M2) test.

The Asset database is registered to the BA repository as an ESRI personal goedatabase (.mdb - doubling as a MS Access database) that can store, query, and manage non-spatial data while the spatial data is in a separate file geodatabase joined by AID/ElementID.

Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. A report on the WAIT process for the Hunter is included in the zip file as part of this dataset.

Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Hunter subregion are found in the "AssetList" table of the database.

Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of "HUN_asset_database_doc_20160224.doc ", located in this filet.

The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset.

Detailed information describing the database structure and content can be found in the document "HUN_asset_database_doc_20160224.doc" located in this file.

Some of the source data used in the compilation of this dataset is restricted.

The public version of this asset database can be accessed via the following dataset: Asset database for the Hunter subregion on 24 February 2016 Public 20170112 v02 (https://data.gov.au/data/dataset/9d16592c-543b-42d9-a1f4-0f6d70b9ffe7)

Dataset History

OBJECTID\tVersionID\tNotes\tDate_

1\t1\tInitial database.\t29/08/2014

3\t1.1\tUpdate the classification for seven identical assets from Gloucester subregion\t16/09/2014

4\t1.2\tAdded in NSW GDEs from Hunter - Central Rivers GDE mapping from NSW DPI (50 635 polygons).\t28/01/2015

5\t1.3\tNew AIDs assiged to NSW GDE assets (Existing AID + 20000) to avoid duplication of AIDs assigned in other databases.\t12/02/2015

6\t1.4\t"(1) Add 20 additional datasets required by HUN assessment project team after HUN community workshop

       (2) Turn off previous GW point assets (AIDs from 7717-7810 inclusive) 

       (3) Turn off new GW point asset (AID: 0)

       (4) Assets (AIDs: 8023-8026) are duplicated to 4 assets (AID: 4747,4745,4744,4743 respectively) in NAM subregion . Their AID, Asset Name, Group, SubGroup, Depth, Source, ListDate and Geometry are using   

         values from that NAM assets.

      (5) Asset (AID 8595) is duplicated to 1 asset ( AID 57) in GLO subregion . Its AID, Asset Name, Group, SubGroup, Depth, Source, ListDate and Geometry are using values from that GLO assets.

      (6) 39 assets (AID from 2969 to 5040) are from NAM Asset database and their attributes were updated to use the latest attributes from NAM asset database 

     (7)The databases, especially spatial  database, were changed such as duplicated attributes fields in spatial data were removed and only ID field is kept. The user needs to join the Table Assetlist or Elementlist to 

        the spatial data"\t16/06/2015

7\t2\t"(1) Updated 131 new GW point assets with previous AID and some of them may include different element number due to the change of 77 FTypes requested by Hunter assessment project team

      (2) Added 104 EPBC assets, which were assessed and excluded by ERIN

      (3) Merged 30 Darling Hardyhead assets to one (asset AID 60140) and deleted another 29 

      (4) Turned off 5 assets from community workshop (60358 - 60362) as they are duplicated to 5 assets from 104 EPBC excluded assets

     (5) Updated M2 test results

     (6) Asset Names (AID: 4743 and 4747) were changed as requested by Hunter assessment project team (4 lower cases to 4 upper case only). Those two assets are from Namoi asset database and their asset names 

       may not match with original names in Namoi asset database.

     (7)One NSW WSP asset (AID: 60814) was added in as requested by Hunter assessment project team. The process method (without considering 1:M relation) for this asset is not robust and is different to other NSW 

      WSP assets. It should NOT use for other subregions. 

     (8) Queries of Find_All_Used_Assets and Find_All_WD_Assets in the asset database can be used to extract all used assts and all water dependant assts"\t20/07/2015

8\t2.1\t"(1) There are following six assets (in Hun subregion), which is same as 6 assets in GIP subregion. Their AID, Asset Name, Group, SubGroup, Depth, Source and ListDate are using values from GIP assets. You will

         not see AIDs from AID_from_HUN in whole HUN asset datable and spreadsheet anymore and you only can see AIDs from AID_from_GIP ( Actually (a) AID 11636 is GIP got from MBC (B) only AID, Asset Name 

         and ListDate are different and changed)

      (2) For BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx, (a) Extracted long ( >255 characters) WD rationale for 19 assets (AIDs:  

         8682,9065,9073,9087,9088,9100,9102,9103,60000,60001,60792,60793,60801,60713,60739,60751,60764,60774,60812 ) in tab "Water-dependent asset register" and 37 assets (AIDs: 

         5040,8651,8677,8682,8650,8686,8687,8718,8762,9094,9065,9067,9073,9077,9081,9086,9087,9088,9100,9102,9103,60000,60001,60739,60742,60751,60713,60764,60771,

         60774,60792,60793,60798,60801,60809,60811,60812) in tab "Asset list" in 1.30 Excel file (b) recreated draft BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx 

      (3) Modified queries (Find_All_Asset_List and Find_Waterdependent_asset_register) for (2)(a)"\t27/08/2015

9\t2.2\t"(1) Updated M2 results from the internal review for 386 Sociocultural assets

      (2)Updated the class to Ecological/Vegetation/Habitat (potential species distribution) for assets/elements from sources of WAIT_ALA_ERIN, NSW_TSEC, NSW_DPI_Fisheries_DarlingHardyhead"\t8/09/2015

10\t2.3\t"(1) Updated M2 results from the internal review

           \\*\tChanged "Assessment team do not say No" to "All economic assets are by definition water dependent"

          \\*\tChanged "Assessment team say No" : to "These are water dependent, but excluded by the project team based on intersection with the PAE is negligible"

          \\*\tChanged "Rivertyles" to "RiverStyles""\t22/09/2015

11\t2.4\t"(1) Updated M2 test results for 86 assets from the external review

      (2) Updated asset names for two assets (AID: 8642 and 8643) required from the external review

      (3) Created Draft Water Dependent Asset Register file using the template V5"\t20/11/2015

12\t2.5\t"Total number of registered water assets was increased by 1 (= +2-1) due to:

              Two assets changed M2 test from "No" to "Yes" , but one asset assets changed M2 test from "Yes" to "No" 

             from the review done by Ecologist group."\t24/02/2016

Dataset Citation

Bioregional Assessment Programme (2015) Asset database for the Hunter subregion on 24 February 2016. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/a39290ac-3925-4abc-9ecb-b91e911f008f.

Dataset Ancestors

• Derived From GW Element Bores with Unknown FTYPE Hunter NSW Office of Water 20150514

• Derived From Travelling Stock Route Conservation Values

• Derived From Spatial Threatened Species and Communities (TESC) NSW 20131129

• Derived From NSW Wetlands

• Derived From [Climate Change Corridors Coastal North East

This digital dataset represents historical geochemical and other information for 58 sample results of produced water from 56 sites in the Orcutt and Oxnard oil fields in Santa Barbara and Ventura Counties, respectively, in southern California. Produced water is a term used in the oil industry to describe water that is produced as a byproduct along with the oil and gas. The locations from which these historical samples were collected include 20 wells (12 in the Oxnard oil field and 8 in the Orcutt oil field). The top and bottom perforations are known for all except one (Dataset ID 33) of these wells. Additional sample sites include 13 storage tanks, and 13 unidentifiable sources. Two of the storage tanks (Dataset IDs 8 and 54), are associated with one and two identifiable wells, respectively. Historical samples from other storage tanks and unidentifiable sample sources may also represent pre- or post-treated composite samples of produced water from single or multiple wells. Historical sample descriptions provide further insight about the site type associated with several of the samples. Eleven sites, including one well (Dataset ID 30), are classified as "injectate" based on the sample description combined with the designated well use at the time of sample collection (WD, water disposal). Two samples collected from wells in Orcutt (Dataset IDs 4 and 7), both oil wells with known perforation intervals, and one sample from an unidentified site (Dataset ID 56) are described as zone or formation samples. Three other samples collected from two wells (Dataset ID’s 46 and 49) in Oxnard were identified as water source wells which access groundwater for use in the production of oil. The numerical water chemistry data were compiled by the U.S. Geological Survey (USGS) from scanned laboratory analysis reports available from the California Geologic Energy Management Division (CalGEM). Sample site characteristics, such as well construction details, were attributed using a combination of information provided with the scanned laboratory analysis reports and well history files from CalGEM Well Finder. The compiled data are divided into two separate data files described as follows: 1) a summary data file identifying each site by name, the site location, basic construction information, and American Petroleum Institute (API) number (for wells), the number of chemistry samples, period of record, sample description, and the geologic formation associated with the origin of the sampled water, or intended destination (formation into which water was to intended to be injected for samples labeled as injectate) of the sample; and 2) a data file of geochemistry analyses for selected water-quality indicators, major and minor ions, nutrients, and trace elements, parameter code and (or) method, reporting level, reporting level type, and supplemental notes. A data dictionary was created to describe the geochemistry data file and is provided with this data release.

The Natural Resource Management (NRM) Regions dataset is maintained for the purpose of authoritative reporting on the Australian Government's NRM investments. The dataset is designed to cover all Australian territory where Australian Government funded NRM projects might take place and includes major islands, external territories, and state and coastal waters in addition to the NRM regional boundaries. Whilst the boundaries of NRM Regions are defined by legislation in some states and territories, this dataset should not be used to represent legal boundaries in any way. It is an administrative dataset developed for the purpose of reporting and public information. It should be noted that from time to time the states and/or territories may revise their regional boundaries in accordance with local needs and therefore alterations to either the attribution or boundaries of the data may occur in the future.Current VersionAs part of Phase Two of the National Landcare Program (NLP) the Australian Government's natural resource management (NRM) investments will be delivered with Regional Delivery Partners (RDPs) across 56 management units. These replace the previous NLP management units used in NLP Phase One. They are officially referred to as Regional Delivery Partners for Environmental Protection, Sustainable Agriculture and Natural Resource Management Services 2022. The spatial data for RDP management units are derived from the NRM Regions spatial data, as described below.The 2022 dataset defines NRM Region boundaries and Regional Delivery Partner management units in a single dataset, thereby overcoming version control issues with the previous approach of publishing separate data layers for each.To handle a variety of required derivations, a fundamental set of 64 NRM Region map objects was first defined. This can then be compiled using various queries on non-spatial attributes. For example, as set out below, we can define 56 continental NRM Regions and 8 off-shore NRM regions, or island sub components of NRM regions located on the continent. Across these a total of 56 RDP management units can also be defined.To identify those NRM regions located on the Australian continent, a "continental" field (yes/no) has been included, for the first time, in the 2022 dataset. It allows differentiation between off-shore and continental regions, and accommodates that some NRM regions (ie one each in NSW and Tasmania) have both a continental part (eg North Coast, NSW) and an off-shore part (eg North Coast - Lord Howe Island).In accordance with the Australian Government’s Remote Indigenous Procurement Policy (RIPP) and its application to NRM regional investment, we have identified 16 RDP management units with more than 50% overlap with RIPP areas, as defined by the Australian Bureau of Statistics. A RIPP field (yes/no) is included in the attribute table. The data structure allows either NRM Regions, RDP management units and those RDPs overlapping RIPPs to be mapped from the single dataset using the NRM_REGION, RDP_NAME and RIPP fields respectively. NRM_ID, RDP_ID and RIPP fields may also be used.The 2022 version updates the previous version (2020). In total, the 2022 version dataset comprises 64 NRM map objects for 62 NRM regions. These comprise 56 mainland regions (of which two have associated islands as separate map objects), the Torres Strait NRM region, and a further five external territories. Four of these external territories are islands and one is classified as Marine NRM. Using the RDP_NAME or RDP_ID fields to map Regional Delivery Partner management units will result in 56 RDP management unitsThese comprise: 54 mainland RDP management units (two of which have island components); Torres Strait; and a "Marine NRM" management unit. The Marine NRM unit combines Australia's Territorial Sea (from 3 nautical miles to 12 nautical miles) and Australia's Exclusive Economic Zone (to 200 nautical miles) as well as Ashmore and Cartier Islands, Christmas Island, Cocos Keeling Islands and Heard and McDonald Islands. It excludes coastal waters (to 3 nautical miles) which are part of the terrestrial RDP management units. It also excludes the Australian Antarctic Territory and Norfolk Island.The 2022 version was derived from the former NRM regions series (latest version was 2020), originally established in 2006 as the "Natural Heritage Trust II (NHT2) Region Boundaries" dataset. Changes to the 2020 version in creating the 2022 version include the following.Natural Resource Management Regions- 'Adelaide and Mount Lofty Ranges' NRM_Region split into 'Green Adelaide' and 'Hills ad Fleurieu'- Added two new NRM_IDs (4011 for Green Adelaide and 4012 for Hills ad Fleurieu)Regional Delivery Partner management units- Changed 'National Landcare Program Management Units' to 'Regional Delivery Partners for Environmental Protection, Sustainable Agriculture and Natural Resource Management Services' - 'Adelaide and Mount Lofty Ranges' split into 'Green Adelaide' and 'Hills ad Fleurieu'- Added two new RDP_IDs (4011 for Green Adelaide and 4012 for Hills ad Fleurieu)- 'Torres Strait', 'Green Adelaide' and 'Marine NRM' added * to match note *management unit covered through other financial arrangements- 'South West Queensland', 'Maranoa Balonne and Border Rivers' and 'Condamine' combined into 'Southern Queensland' with light grey dotted line to denote NRM borders.- Added the following fields to differentiate RDPs from NRMs- -RDP_ID- -RDP_NAME- -RDP_DESC (Previously AREA_DESC)- -RIPP- 'Torres Strait' and 'Green Adelaide' symbology changed to grey hatched filling. - The management units are coloured based on their overlap with the remote Indigenous Procurement Policy area (RIPP).- Remote management units are orange – to be incl. the management unit needed to have more than 50% overlap with the RIPP. - Non-remote management units are green- *Management units covered through other financial arrangements management units are grey with hatchingPrevious VersionsThe 2020 version NLP Management Units dataset contained 58 separate map objects. These comprised: 56 mainland Management Units; a separate object for Lord Howe Island (part of North Coast, NSW Management Unit); and a "Marine NRM" Management Unit which combined Australia's Territorial Sea (from 3 nautical miles to 12 nautical miles) and Australia's Exclusive Economic Zone (to 200 nautical miles). It excluded coastal waters (to 3 nautical miles) which are part of the terrestrial NLP Management Units. It also excluded Ashmore & Cartier Islands, Australian Antarctic Territory, Christmas Island, Cocos & Keeling Islands, Macquarie Island, Heard & MacDonald Islands and Norfolk Island, and those parts of Australia's Territorial Sea and Exclusive Economic Zone that surround these locations.The 2020 version was derived from the former NRM regions series, originally established in 2006 as the "Natural Heritage Trust II (NHT2) Region Boundaries" dataset. The 2017 version, from which the 2020 version was developed, was itself an update to 2016 v2 in which changes were made to boundaries of six of Western Australia’s seven NRM regions, and region names in Qld, Tas and WA. AttributesThe principle data fields in the 2022 version dataset are:-STATE -NRM_REGION-NRM_ID-NRM_DESC (Previously AREA_DESC)-RDP_ID-RDP_NAME-RDP_DESC (Previously AREA_DESC)-RIPP-CONTINENTALNRM_ID and NRM_REGION Names grouped by state/territory are as follows:New South Wales (11 regions + 1 extra map object for Lord Howe Island)1010 Central Tablelands1020 Central West1030 Greater Sydney1040 Hunter1050 Murray1060 North Coast1061 North Coast - Lord Howe Island1070 North West NSW1080 Northern Tablelands1090 Riverina1100 South East NSW1110 WesternVictoria (10 regions)2010 Corangamite2020 East Gippsland2030 Glenelg Hopkins2040 Goulburn Broken2050 Mallee2060 North Central2070 North East2080 Port Phillip and Western Port2090 West Gippsland2100 WimmeraQueensland (15 regions) 3010 Burnett Mary3020 Cape York3030 Condamine3040 Co-operative Management Area3050 Desert Channels3060 Fitzroy3070 Burdekin3080 Northern Gulf3090 Maranoa Balonne and Border Rivers3100 Mackay Whitsunday3110 South East Queensland3120 South West Queensland3130 Southern Gulf3140 Wet Tropics3150 Torres StraitSouth Australia (9 regions) 4011 Green Adelaide4012 Hills and Fleurieu4020 Alinytjara Wilurara4030 Eyre Peninsula4040 Kangaroo Island4050 Northern and Yorke4060 South Australian Arid Lands4070 South Australian Murray Darling Basin4080 Limestone CoastWestern Australia (7 regions)5010 Northern Agricultural Region5020 Peel-Harvey Region5030 Swan Region5040 Rangelands Region5050 South Coast Region5060 South West Region5070 Avon River BasinTasmania (3 regions + 1 extra map object for Macquarie Island)6010 North West NRM Region6020 North NRM Region6030 South NRM Region6031 South NRM Region - Macquarie IslandsNorthern Territory (1 region)7010 Northern TerritoryAustralian Capital Territory (1 region)8010 ACTExternal Territories (5 regions) 9010 Ashmore and Cartier Islands9020 Christmas Island9030 Cocos Keeling Islands9040 Heard and McDonald Islands9060 Marine NRM

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This database is an initial Asset database for the Central West subregion on 29 April 2015. This dataset contains the spatial and non-spatial (attribute) components of the Central West subregion Asset List as one .mdb files, which is readable as an MS Access database and a personal geodatabase. Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. All reports received associated with the WAIT process for Central West are included in the zip file as part of this dataset. Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Central West subregion are found in the "AssetList" table of the database. In this version of the database only M1 has been assessed. Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of "CEN_asset_database_doc_20150429.doc ", located in the zip file as part of this dataset. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document "CEN_asset_database_doc_20150429.doc" located in the zip file. Some of the source data used in the compilation of this dataset is restricted.

Dataset History

This is initial asset database.

The Bioregional Assessments methodology (Barrett et al., 2013) defines a water-dependent asset as a spatially distinct, geo-referenced entity contained within a bioregion with characteristics having a defined cultural indigenous, economic or environmental value, and that can be linked directly or indirectly to a dependency on water quantity and/or quality.

Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. Elements are initially included in database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet materiality test 2 (M2) - assets considered to be water dependent.

Elements may be represented by a single, discrete spatial unit (polygon, line or point), or a number of spatial units occurring at more than one location (multipart polygons/lines or multipoints). Spatial features representing elements are not clipped to the preliminary assessment extent - features that extend beyond the boundary of the assessment extent have been included in full. To assist with an assessment of the relative importance of elements, area statements have been included as an attribute of the spatial data. Detailed attribute tables contain descriptions of the geographic features at the element level. Tables are organised by data source and can be joined to the spatial data on the "ElementID" field

Elements are grouped into Assets, which are the objects used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy.

The "Element_to_asset" table contains the relationships and identifies the elements that were grouped to create each asset.

Following delivery of the first pass asset list, project teams make a determination as to whether an asset (comprised of one or more elements) is water dependent, as assessed against the materiality tests detailed in the BA Methodology. These decisions are provided to ERIN by the project team leader and incorporated into the Assetlist table in the Asset database. The Asset database is then re-registered into the BA repository.

The Asset database dataset (which is registered to the BA repository) contains separate spatial and non-spatial databases.

Non-spatial (tabular data) is provided in an ESRI personal geodatabase (.mdb - doubling as a MS Access database) to store, query, and manage non-spatial data. This database can be accessed using either MS Access or ESRI GIS products. Non-spatial data has been provided in the Access database to simplify the querying process for BA project teams. Source datasets are highly variable and have different attributes, so separate tables are maintained in the Access database to enable the querying of thematic source layers.

Spatial data is provided as an ESRI file geodatabase (.gdb), and can only be used in an ESRI GIS environment. Spatial data is represented as a series of spatial feature classes (point, line and polygon layers). Non-spatial attribution can be joined from the Access database using the AID and ElementID fields, which are common to both the spatial and non-spatial datasets. Spatial layers containing all the point, line and polygon - derived elements and assets have been created to simplify management of the Elementlist and Assetlist tables, which list all the elements and assets, regardless of the spatial data geometry type. i.e. the total number of features in the combined spatial layers (points, lines, polygons) for assets (and elements) is equal to the total number of non-spatial records of all the individual data sources.

Dataset Citation

Department of the Environment (2013) Asset database for the Central West subregion on 29 April 2015. Bioregional Assessment Derived Dataset. Viewed 08 February 2017, http://data.bioregionalassessments.gov.au/dataset/5c3f9a56-7a48-4c26-a617-a186c2de5bf7.

Dataset Ancestors

• Derived From Macquarie Marshes Vegetation 1991-2008 VIS_ID 3920

• Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014)

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From Travelling Stock Route Conservation Values

• Derived From NSW Wetlands

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Birds Australia - Important Bird Areas (IBA) 2009

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From NSW Office of Water Surface Water Offtakes - NIC v1 20131024

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From Ramsar Wetlands of Australia

• Derived From Native Vegetation Management (NVM) - Manage Benefits

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

• Derived From Climate Change Corridors (Dry Habitat) for North East NSW

• Derived From Great Artesian Basin and Laura Basin groundwater recharge areas

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From [New South Wales NSW Regional CMA Water Asset

Fire Stations in the United States Any location where fire fighters are stationed or based out of, or where equipment that such personnel use in carrying out their jobs is stored for ready use. Fire Departments not having a permanent location are included, in which case their location has been depicted at the city/town hall or at the center of their service area if a city/town hall does not exist. This dataset includes those locations primarily engaged in forest or grasslands fire fighting, including fire lookout towers if the towers are in current use for fire protection purposes. This dataset includes both private and governmental entities. Fire fighting training academies are also included. TGS has made a concerted effort to include all fire stations in the United States and its territories. This dataset is comprised completely of license free data. The HSIP Freedom Fire Station dataset and the HSIP Freedom EMS dataset were merged into one working file. TGS processed as one file and then separated for delivery purposes. Please see the process description for the breakdown of how the records were merged. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this field, the oldest record dates from 01/03/2005 and the newest record dates from 01/11/2010.Homeland Security Use Cases: Use cases describe how the data may be used and help to define and clarify requirements. 1. An assessment of whether or not the total fire fighting capability in a given area is adequate. 2. A list of resources to draw upon by surrounding areas when local resources have temporarily been overwhelmed by a disaster - route analysis can determine those entities that are able to respond the quickest. 3. A resource for Emergency Management planning purposes. 4. A resource for catastrophe response to aid in the retrieval of equipment by outside responders in order to deal with the disaster. 5. A resource for situational awareness planning and response for Federal Government events.

This digital dataset contains historical geochemical and other information for 200 samples of produced water from 182 sites in 25 oil fields in Los Angeles and Orange Counties, southern California. Produced water is a term used in the oil industry to describe water that is produced as a byproduct along with the oil and gas. The locations from which these historical samples have been collected include 152 wells. Well depth and (or) perforation depths are available for 114 of these wells. Sample depths are available for two additional wells in lieu of well or perforation depths. Additional sample sites include four storage tanks, and two unidentifiable sample sources. One of the storage tank samples (Dataset ID 57) is associated with a single identifiable well. Historical samples from other storage tanks and unidentifiable sample sources may also represent pre- or post-treated composite samples of produced water from single or multiple wells. Historical sample descriptions provide further insight about the site type associated with some of the samples. Twenty-four sites, including 21 wells, are classified as "injectate" based on the sample description combined with the designated well use at the time of sample collection (WD, water disposal or WF, water flood). Historical samples associated with these sites may represent water that originated from sources other than the wells from which they were collected. For example, samples collected from two wells (Dataset IDs 86 and 98) include as part of their description “blended and treated produced water from across the field”. Historical samples described as formation water (45 samples), including 38 wells with a well type designation of OG (oil/gas), are probably produced water, representing a mixture of formation water and water injected for enhanced recovery. A possible exception may be samples collected from OG wells prior to the onset of production. Historical samples from four wells, including three with a sample description of "formation water", were from wells identified as water source wells which access groundwater for use in the production of oil. The numerical water chemistry data were compiled by the U.S. Geological Survey (USGS) from scanned laboratory analysis reports available from the California Geologic Energy Management Division (CalGEM). Sample site characteristics, such as well construction details, were attributed using a combination of information provided with the scanned laboratory analysis reports and well history files from CalGEM Well Finder. The compiled data are divided into two separate data files described as follows: 1) a summary data file identifying each site by name, the site location, basic construction information, and American petroleum Institute (API) number (for wells), the number of chemistry samples, period of record, sample description, and the geologic formation associated with the origin of the sampled water, or intended destination (formation into which water was to intended to be injected for samples labeled as injectate) of the sample; and 2) a data file of geochemistry analyses for selected water-quality indicators, major and minor ions, nutrients, and trace elements, parameter code and (or) method, reporting level, reporting level type, and supplemental notes. A data dictionary was created to describe the geochemistry data file and is provided with this data release.

The Protected Areas Database of the United States (PAD-US) is a geodatabase, managed by USGS GAP, that illustrates and describes public land ownership, management and other conservation lands, including voluntarily provided privately protected areas. The State, Regional and LCC geodatabases contain two feature classes. The PADUS1_3_FeeEasement feature class and the national MPA feature class. Legitimate and other protected area overlaps exist in the full inventory, with Easements loaded on top of Fee. Parcel data within a protected area are dissolved in this file that powers the PAD-US Viewer. As overlaps exist, GAP creates separate analytical layers to summarize area statistics for "GAP Status Code" and "Owner Name". Contact the PAD-US Coordinator for more information. The lands included in PAD-US are assigned conservation measures that qualify their intent to manage lands for the preservation of biological diversity and to other natural, recreational and cultural uses; managed for these purposes through legal or other effective means. The geodatabase includes: 1) Geographic boundaries of public land ownership and voluntarily provided private conservation lands (e.g., Nature Conservancy Preserves); 2) The combination land owner, land manager, management designation or type, parcel name, GIS Acres and source of geographic information of each mapped land unit 3) GAP Status Code conservation measure of each parcel based on USGS National Gap Analysis Program (GAP) protection level categories which provide a measurement of management intent for long-term biodiversity conservation 4) IUCN category for a protected area's inclusion into UNEP-World Conservation Monitoring Centre's World Database for Protected Areas. IUCN protected areas are defined as, "A clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values" and are categorized following a classification scheme available through USGS GAP; 5) World Database of Protected Areas (WDPA) Site Codes linking the multiple parcels of a single protected area in PAD-US and connecting them to the Global Community. As legitimate and other overlaps exist in the combined inventory GAP creates separate analytical layers to obtain area statistics for "GAP Status Code" and "Owner Name". PAD-US version 1.3 Combined updates include: 1) State, local government and private protected area updates delivered September 2011 from PAD-US State Data Stewards: CO (Colorado State University), FL (Florida Natural Areas Inventory), ID (Idaho Fish and Game), MA (The Commonwealth's Office of Geographic Information Systems, MassGIS), MO (University of Missouri, MoRAP), MT (Montana Natural Heritage Program), NM (Natural Heritage New Mexico), OR (Oregon Natural Heritage Program), VA (Department of Conservation and Recreation, Virginia Natural Heritage Program). 2) Select local government (i.e. county, city) protected areas (3,632) across the country (to complement the current PAD-US inventory) aggregated by the Trust for Public Land (TPL) for their Conservation Almanac that tracks the conservation finance movement across the country. 3) A new Date of Establishment field that identifies the year an area was designated or otherwise protected, attributed for 86% of GAP Status Code 1 and 2 protected areas. Additional dates will be provided in future updates. 4) A national wilderness area update from wilderness.net 5) The Access field that describes public access to protected areas as defined by data stewards or categorical assignment by Primary Designation Type. . The new Access Source field documents local vs. categorical assignments. See the PAD-US Standard Manual for more information: gapanalysis.usgs.gov/padus 6) The transfer of conservation measures (i.e. GAP Status Codes, IUCN Categories) and documentation (i.e. GAP Code Source, GAP Code Date) from PAD-US version 1.2 or categorical assignments (see PAD-US Standard) when not provided by data stewards 7) Integration of non-sensitive National Conservation Easement Database (NCED) easements from August 2011, July 2012 with PAD-US version 1.2 easements. Duplicates were removed, unless 'Stacked' = Y and multiple easements exist. 8) Unique ID's transferred from NCED or requested for new easements. NCED and PAD-US are linked via Source UID in the PAD-US version 1.3 Easement feature class. 9) Official (member and eligible) MPAs from the NOAA MPA Inventory (March 2011, www.mpa.gov) translated into the PAD-US schema with conservation measures transferred from PAD-US version 1.2 or categorically assigned to new protected areas. Contact the PAD-US Coordinator for documentation of categorical GAP Status Code assignments for MPAs. 10) Identified MPA records that overlap existing protected areas in the PAD-US Fee feature class (i.e. PADUS Overlap field in MPA feature class). For example, many National Wildlife Refuges and National Parks are also MPAs and are represented in the PAD-US MPA and Fee feature classes.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Asset Database v2 in Cooper subregions supersedes the previous version of the Cooper Asset database, Asset database for the Cooper subregion on 27 March 2015, a9ff5fc2-00c0-4da5-91af-94def934243f. The Asset database is registered to the BA repository as an ESRI personal goedatabase (.mdb - doubling as a MS Access database) that can store, query, and manage non-spatial data while the spatial data is in a separate file geodatabase joined by AID/ElementID. Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. All reports received associated with the WAIT process for Cooper are included in the zip file as part of this dataset. Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Cooper subregion are found in the "AssetList" table of the database. In this version of the database only M1 has been assessed. Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of "COO_asset_database_doc_20150814.doc ", located in the zip file as part of this dataset. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document "COO_asset_database_doc_20150814.doc" located in the zip file. Some of the source data used in the compilation of this dataset is restricted. Dataset History VersionID Date Notes 1.0 27/03/2015 Initial database 2.0 14/08/2015 "(1) Updated the database for M2 test results provided from COO assessment team and created the draft BA-LEB-COO-130-WaterDependentAssetRegister-AssetList-V20150814.xlsx (2) updated the group, subgroup, class and depth for (up to) 2 NRM WAIT assets to cooperate the feedback to OWS from relevant SA NRM regional office (whose staff missed the asset workshop). The AIDs and names of those assets are listed in table LUT_changed_asset_class_20150814 in COO_asset_database_20150814.mdb (3) As a result of (2), added one new asset separated from one existing asset. This asset and its parent are listed in table LUT_ADD_1_asste_20150814 in COO_asset_database_20150814.mdb. The M2 test result for this asset is inherited from its parent in this version (5) Added Appendix C in COO_asset_database_doc_201500814.doc is about total elements/assets in current Group and subgroup (6)Added Four SQL queries (Find_All_Used_Assets, Find_All_WD_Assets, Find_Amount_Asset_in_Class and Find_Amount_Elements_in_Class) in COO_asset_database_20150814.mdb.mdb for total assets and total numbers (7)The databases, especially spatial database (COO_asset_database_20150814Only.gdb), were changed such as duplicated attribute fields in spatial data were removed and only ID field is kept. The user needs to join the Table Assetlist or Elementlist to the relevant spatial data" Dataset Citation Bioregional Assessment Programme (2014) Asset database for the Cooper subregion on 14 August 2015. Bioregional Assessment Derived Dataset. Viewed 27 November 2017, http://data.bioregionalassessments.gov.au/dataset/5c3697e6-8077-4de7-b674-e0dfc33b570c. Dataset Ancestors Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204 Derived From Queensland QLD - Regional - NRM - Water Asset Information Tool - WAIT - databases Derived From Matters of State environmental significance (version 4.1), Queensland Derived From Geofabric Surface Network - V2.1 Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From South Australia SA - Regional - NRM Board - Water Asset Information Tool - WAIT - databases Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From National Groundwater Information System (NGIS) v1.1 Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From Queensland QLD Regional CMA Water Asset Information WAIT tool databases RESTRICTED Includes ALL Reports Derived From Queensland wetland data version 3 - wetland areas. Derived From SA Department of Environment, Water and Natural Resources (DEWNR) Water Management Areas 141007 Derived From South Australian Wetlands - Groundwater Dependent Ecosystems (GDE) Classification Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores v3 03122014 Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From Ramsar Wetlands of Australia Derived From Permanent and Semi-Permanent Waterbodies of the Lake Eyre Basin (Queensland and South Australia) (DRAFT) Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores and NGIS v4 28072014 Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From Great Artesian Basin and Laura Basin groundwater recharge areas Derived From Lake Eyre Basin (LEB) Aquatic Ecosystems Mapping and Classification Derived From Australia - Species of National Environmental Significance Database Derived From Asset database for the Cooper subregion on 27 March 2015 Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release)

Soccer Segmentation (SoS)

SoS is a semantic segmentation dataset for the RoboCup Humanoid League. It is semi-synthetically generated using Blender and real HDRs from past RoboCup fields. There are 47,575 samples in the dataset. The dataset is split into ten partitions for easier download. A sample in the dataset is defined by the tuple containing the raw image, segmentation mask and metadata. Each folder contains full samples, that is the raw image, mask and metadata for one… See the full description on the dataset page: https://huggingface.co/datasets/NUbots/SoccerSegmentation.

Very little information is known about the distribution and abundance of snow petrels at the regional scale. This dataset contains locations of bird nests, mostly snow petrels, mapped in the Windmill Islands during the 2002-2003 season. Location of nests were recorded with handheld GPS receivers connected to a pocket PC and stored as a shapefile using Arcpad (ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of data fields is provided in the detailed description of the shapefiles.

Two observers conducted the surveys using distinct methodologies, Frederique Olivier (FO) and Drew Lee (DL). Three separate nest location files (ArcView point shapefiles) were produced and correspond to each of the survey methodologies used. Methodology 1 was the use of 200*200 m grid squares in which exhaustive searches were conducted (FO). Methodology 2 was the use of 2 transects within each the 200*200 m grid squares; methodology 3 was the use of 4 small quadrats (ca 25 m) located within the 200*200m grid squares (DL). Nests mapped in a non-systematic manner (not following a specific methodology) are clearly identified within each dataset. Datasets were kept separate due to the uncertainties caused by GPS errors (the same nest may have different locations due to GPS error).

Three separate shapefiles describe survey methodologies: - one polygon shapefile locates the 200*200 grid sites searched systematically (FO) - one polygon shapefile locates the small quadrats (DL) - one line shapefile locates line transects (DL)

Spatial characteristics, date of survey, search effort, number of nests found and other parameters are recorded for the grid sites, transect and quadrats.

See the word document in the file download for more information.

This work has been completed as part of ASAC project 1219 (ASAC_1219).

The fields in this dataset are:

Species Activity Type Entrances Slope Remnants Latitude Longitude Date Snow Eggchick Cavitysize Cavitydepth Distnn Substrate Comments SitedotID Aspect Firstfred Systematic/Edge/Incidental RecordCode

The full dataset, including a word document providing further information about the dataset, is publicly available for download from the provided URL.

Also available for download from another URL is polygon data representing flying bird nesting areas. The polygon data was derived from the flying bird nest locations by the Australian Antarctic Data Centre for displaying on maps.

Jails and Prisons (Correctional Institutions).

The Jails and Prisons sub-layer is part of the Emergency Law Enforcement Sector and the Critical Infrastructure Category. A Jail or Prison consists of any facility or location where individuals are regularly and lawfully detained against their will. This includes Federal and State prisons, local jails, and juvenile detention facilities, as well as law enforcement temporary holding facilities. Work camps, including camps operated seasonally, are included if they otherwise meet the definition. A Federal Prison is a facility operated by the Federal Bureau of Prisons for the incarceration of individuals. A State Prison is a facility operated by a state, commonwealth, or territory of the US for the incarceration of individuals for a term usually longer than 1 year. A Juvenile Detention Facility is a facility for the incarceration of those who have not yet reached the age of majority (usually 18 years). A Local Jail is a locally administered facility that holds inmates beyond arraignment (usually 72 hours) and is staffed by municipal or county employees. A temporary holding facility, sometimes referred to as a "police lock up" or "drunk tank", is a facility used to detain people prior to arraignment. Locations that are administrative offices only are excluded from the dataset. This definition of Jails is consistent with that used by the Department of Justice (DOJ) in their "National Jail Census", with the exception of "temporary holding facilities", which the DOJ excludes.

Locations which function primarily as law enforcement offices are included in this dataset if they have holding cells.

If the facility is enclosed with a fence, wall, or structure with a gate around the buildings only, the locations were depicted as "on entity" at the center of the facility. If the facility's buildings are not enclosed, the locations were depicted as "on entity" on the main building or "block face" on the correct street segment.

Personal homes, administrative offices, and temporary locations are intended to be excluded from this dataset; however, some personal homes of constables are included due to the fact that many constables work out of their homes.

TGS has made a concerted effort to include all correctional institutions.

This dataset includes non license restricted data from the following federal agencies: Bureau of Indian Affairs; Bureau of Reclamation; U.S. Park Police; Federal Bureau of Prisons; Bureau of Alcohol, Tobacco, Firearms and Explosives; U.S. Marshals Service; U.S. Fish and Wildlife Service; National Park Service; U.S. Immigration and Customs Enforcement; and U.S. Customs and Border Protection.

This dataset is comprised completely of license free data.

The Law Enforcement dataset and the Correctional Institutions dataset were merged into one working file. TGS processed as one file and then separated for delivery purposes.

With the merge of the Law Enforcement and the Correctional Institutions datasets, NAICS Codes & Descriptions were assigned based on the facility's main function which was determined by the entity's name, facility type, web research, and state supplied data. In instances where the entity's primary function is both law enforcement and corrections, the NAICS Codes and Descriptions are assigned based on the dataset in which the record is located (i.e., a facility that serves as both a Sheriff's Office and as a jail is designated as [NAICSDESCR]="SHERIFFS' OFFICES (EXCEPT COURT FUNCTIONS ONLY)" in the Law Enforcement layer and as [NAICSDESCR]="JAILS (EXCEPT PRIVATE OPERATION OF)" in the Correctional Institutions layer).

Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries.

"#" and "*" characters were automatically removed from standard fields that TGS populated. Double spaces were replaced by single spaces in these same fields.

Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results.

All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics.

The currentness of this dataset is indicated by the [CONTDATE] field. Based on the values in this field, the oldest record dates from 06/27/2006 and the newest record dates from 10/22/2009