A Comprehensive List of Open Data Portals from Around the World

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The Crude Oil Analysis (COA) database contains the digital data compilation of 9,076 crude oil analyses from samples collected from 1920 through 1983 from the United States and around the world and analyzed by the United States Bureau of Mines (National Institute for Petroleum and Energy Research, 1995). Two laboratories (Bartlesville, Oklahoma, and Laramie, Wyoming) performed routine crude oil analyses by a standardized method, and the data were originally reported in more than 50 reports by the Bureau of Mines. Analyses include specific gravity, API gravity, pour point, viscosity, sulfur content, nitrogen content, and color of the crude oil, as well as the bulk properties of the distillation cuts. The data were digitized in the late 1970s and a database retrieval system was implemented in 1980 and made available to the public. The Department of Energy (DOE) updated this system in 1995-96 with public access through a dial-up bulletin board system. The database was operated by the National Institute for Petroleum and Energy Research (NIPER) in Bartlesville, Oklahoma. A stand-alone version of the database (COADB) was available in 1995 in the form of a series of tables in Foxpro (.dbf) format. In 1998, an updated version of COADB was available on the NIPER website that included a Microsoft Access 97 version of the database called "coadb.mdb". The file contains more tables than the original 1995 version but we believe the number of oil samples and the amount of raw data are the same. The additional tables contain text translations for codes used in other tables regarding color, county, laboratory, formation, geologic age, lithology, and state name. Sample location information is generally inadequate to identify the specific well in most cases. The sample location information lacks lease name and in many cases well number and section-township-range. In rare cases, the latitude and longitude are given. A 2002 version was provided by the National Energy Technology Laboratory.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to

establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data

Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered.
Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review:

Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection.
Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation.

See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

NOTE: A more current version of the Protected Areas Database of the United States (PAD-US) is available: PAD-US 2.0 https://doi.org/10.5066/P955KPLE. The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

The SCImago Journal & Country Rank is a publicly available portal that includes the journals and country scientific indicators developed from the information contained in the Scopus® database (Elsevier B.V.). These indicators can be used to assess and analyze scientific domains. Country rankings may also be compared or analysed separately.

✅Collected by: SCImagoJR Country Data Collector Notebook

💬Also have a look at
💡 UNIVERSITIES & Research INSTITUTIONS Rank - SCImagoIR
💡 Scientific JOURNALS Indicators & Info - SCImagoJR

• 27 major thematic subject areas as well as 309 specific subject categories according to Scopus® Classification.
• Citation data is drawn from over 34,100 titles from more than 5,000 international publishers
• SCImago is a research group from the Consejo Superior de Investigaciones Científicas (CSIC), University of Granada, Extremadura, Carlos III (Madrid) and Alcalá de Henares, dedicated to information analysis, representation and retrieval by means of visualisation techniques.

☢️❓The entire dataset is obtained from public and open-access data of ScimagoJR (SCImago Journal & Country Rank)
ScimagoJR Country Rank
SCImagoJR About Us

Available indicators:

• Documents: Number of documents published during the selected year. It is usually called the country's scientific output.

• Citable Documents: Selected year citable documents. Exclusively articles, reviews and conference papers are considered.

• Citations: Number of citations by the documents published during the source year, --i.e. citations in years X, X+1, X+2, X+3... to documents published during year X. When referred to the period 1996-2021, all published documents during this period are considered.

• Citations per Document: Average citations per document published during the source year, --i.e. citations in years X, X+1, X+2, X+3... to documents published during year X. When referred to the period 1996-2021, all published documents during this period are considered.

• Self Citations: Country self-citations. Number of self-citations of all dates received by the documents published during the source year, --i.e. self-citations in years X, X+1, X+2, X+3... to documents published during year X. When referred to the period 1996-2021, all published documents during this period are considered.

• H index: The h index is a country's number of articles (h) that have received at least h- citations. It quantifies both country's scientific productivity and scientific impact and it is also applicable to scientists, journals, etc.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

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 Galilee dataset contains v13+V12 of the GAL Asset database (GAL_asset_database_20160104.mdb), a Geodatabase version for GIS mapping purposes (GAL_asset_database_20160104_GISONLY.gdb), the draft Water Dependent Asset Register spreadsheet (BA-LEB-GAL-130-WaterDependentAssetRegister-AssetList-v20160104.xlsx), the draft Receptor Register spreadsheet (BA-LEB-GAL-140-ReceptorRegister-v20160104.xlsx), a data dictionary (GAL_asset_database_doc_20160104.doc), a folder (Indigenous_doc) containing documentation associated with Indigenous water asset project, and a folder (NRM_DOC) containing documentation associated with the Water Asset Information Tool (WAIT) process as outlined below

This database supersedes Asset database for the Galilee subregion on 10 September 2015 (GUID: c22a13bf-07ea-4eaa-960d-79d488a50496).

The updating in this V13+V12 GAL asset database 201600104 includes:

(1) Total number of registered water assets was increased by 79 due to: (a) The 9 assets changed their M2 test to "Yes" from the review done by Ecologist group. (b) 69 indigenous water assets from OWS were added.

(2) GAL receptor was included

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 separated file geodatabase joined by AID/Element ID/BARID. 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 "GAL_asset_database_doc_20160104.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 "GAL_asset_database_doc_20160104.doc" located in the zip file.

The public version of this asset database can be accessed via the following dataset: Asset database for the Galilee subregion on 04 January 2016 Public (https://data.gov.au/data/dataset/eb4cf797-9b8f-4dff-9d7a-a5dfbc8d2bed)

Purpose

For creation of asset list for bioregional assessment

The public version of this asset database can be accessed via the following dataset: Asset database for the Galilee subregion on 04 January 2016 Public (https://data.gov.au/data/dataset/eb4cf797-9b8f-4dff-9d7a-a5dfbc8d2bed)

Dataset History

VersionID\tDate\tNotes

1.0\t23/12/2013\tInitial database

1.01\t3/02/2014\tupdated 207 Names in table AssetList using AssetName in table NRM_Water_Asset for those recodes from source WAIT_Burdekin

1.01\t3/02/2014\tremoved the space at the beginning of Unnamed)_South Australian Arid Lands_66329 and (Unnamed)_South Australian Arid Lands_57834

1.01\t20/02/2014\tThe database is not changed. About 36 self intersect polygons in spatial data were fixed. New shapefile name for polygon is Galilee_AssetList_geoPolygon20140220.shp

3.0\t23/04/2014\t"Updated universally changing ""AssetID"" to ""ElementID"" and changing the name of the ""AssetList"" table to ""ElementList"". A table to include Queensland threatened species data has also been added, and ElementIDs added to the ""ElementList"" table."

2.0\t23/04/2014\tErrors found after handover to CSIRO. Updated immediately to v3.0.

4.0\t24/04/2014\tQueensland threatened species data updated to new sequence of ElementIDs. New spatial data provided \[NAME\]

8\t5/05/2014\tIt is generally ready except calcification and asset area

9.0\t28/11/2014\t"Add additional datasets such as QLD_DERM PR Waterbodies, QLD_DERM PR Waterbodies QLD RegionalEcosystems as request

Update GDEsub, GDEsur, QLD_ DNRM_ECON_GW QLD_ DNRM_ECON_SW as request"

10\t22/05/2015\tUpdated database tables of AssetDecisions and AssetList for M2 and M3 test results

11 10/09/2015

(1) AID 70360 added for potential distribution of Largetooth Sawfish (Pristis pristis (Pristis microdon)). Attributes in additional attribute look-up table tbl_Species_EPBC_PristisPristis.

(2) The (brief) explanation for M2 decisions has been updated based on advice from the project team, replacing detailed explanations which were truncated in the Assetlist and AssetDecisions tables. The detailed explanation is retained in the DecisionReason field of the AssetDecisions table. Note there are no changes to decision outcomes or numbers of assets on the asset register;

(3) The draft BA-LEB-GAL-130-WaterDependentAssetRegister-AssetList-V20150910.xlsx as has been updated as an output of this database. The brief M2_decision replaces the extended decision rationale that was included in the last version of the spreadsheet.

(4) x15 elements associated with the (single) asset named "No_Asset" (AID = 0) were removed from the database (deleted from the AssetList, Element_to_asset and ElementList tables, and also from the element and asset polygon layers). These polygons were exact duplicates of other elements from the same source dataset and had been previously grouped as "No_Asset". This action will not affect the asset count for the asset list or the water dependent asset register.

12\t24/12/2015\t"Area calculations were removed from the spatial data and added to the assetList and elementList tables in

                    this .mdb database. Area calculations were included for assets and elementlist line features.

                    A total of 69 Indigenous elements were added to the ElementList table in the database, translating into an 

                     additional 69 indigenous assets which were added to the AssetList table. 

                     Of these 69 indigenous assets: 

                         40 intersect the PAE and are included in the ""asset list"".

                         5 did not intersect the PAE, so did not pass ""M1"". These are retained in the AssetList table but 

                          are ""switched off"" at M1 (i.e. M1 = 'No'). These are not considered part of the ""asset list"".

                         24 have no meaningful spatial component. These were added to the AssetList table, but ""switched 

                          off"" at ""M0"" (i.e. not fit for purpose, M0 = 'No') and therefore are not consodered part of 

                          the ""asset list""."

13\t4/01/2016\t"(1)(a) Added table ReceptorList in GAL_asset_database_20160104.mdb, using the data file from GAL project

                      team (b) Created draft BA-LEB-GAL-140-ReceptorRegister-v20160104.xlsx (c) Added table 

                       tbl_Receptors in GAL_asset_database_20160104.mdb and GM_GAL_ReceptorList_pt (created by ERIN 

                       using the location data from GAL project team) in GAL_asset_database_20160104_GISONLY.gdb,; (d)

                       Add SQL query "Find_used_Receptor_a" and "Find_used_Receptor_b" for extracting all used receptor for 

                        the register.

                     (2)(a)Updated M2 test for GAL from GAL_Species_TEC_decisions_reveiw_23112015.(b) Created draftBA-

                        LEB-GAL-130-WaterDependentAssetRegister-AssetList-v20160104.xlsx"

Dataset Citation

Bioregional Assessment Programme (2013) Asset database for the Galilee subregion on 04 January 2016. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/12ff5782-a3d9-40e8-987c-520d5fa366dd.

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 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 [Environmental Asset Database

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.

This data set holds the publicly-available version of the database of water-dependent assets that was compiled for the bioregional assessment (BA) of the Gloucester subregion as part of the Bioregional Assessment Technical Programme. Though all life is dependent on water, for the purposes of a bioregional assessment, a water-dependent asset is an asset potentially impacted by changes in the groundwater and/or surface water regime due to coal resource development. The water must be other than local rainfall. Examples include wetlands, rivers, bores and groundwater dependent ecosystems.

Under the Bioregional Assessment Technical Programme (BATP), a spatial assets database was developed for each bioregion and / or subregion. A single asset is represented spatially in the asset database by single or multiple spatial features (point, line or polygon). Individual points, lines or polygons are termed elements.

The spatial elements that represent assets were identified by regional natural resource management organisations and collated by ERIN into state databases using the Water Asset Information Tool (WAIT). These data were supplemented with additional information from appropriate Australian and state and territory government databases. The materiality of each asset was tried against a series of tests and the consequent decisions were also included in the asset database. Each asset database is therefore a rich collation of information about the assets and how they have been assessed.

All assets and elements also have associated attribute data; these are stored in attribute tables, including associated lookup tables (LUTs). Some data in the asset database are not associated with spatial data; typically these data relate to the database itself e.g. versioning information

This dataset contains the unrestricted publicly-available components of spatial and non-spatial (attribute) data of the (restricted) Asset database for the Gloucester subregion on 12 February 2016 (72a47bec-1393-49d6-b379-0e48551d26a9). The database is provided primarily as an ESRI File geodatabase (.gdb), which is able to be opened in readily available open source software such as QGIS. Other formats include the Microsoft Access database (.mdb in ESRI Personal Geodatabase format), industry-standard ESRI Shapefiles and tab-delimited text files of all the attribute tables.

The restricted version of the Gloucester Asset database has a total count of 4029 Elements ( including 11 aspatial elements) and 229 Assets (including 11 aspatial Assets ) . In the public version of the Asset Gloucester database 789 (19%)> Elements (spatial features) have been removed from the Element List and spatial Element Layer(s) and 42 Assets (19%) have been removed from the spatial Asset Layer(s)

The elements/assets removed from the restricted Asset Database are from the following data sources:

1) Species Profile and Threats Database (SPRAT) Metadata only) (7276dd93-cc8c-4c01-8df0-cef743c72112)

2) Australia, Register of the National Estate (RNE) (Internal 878f6780-be97-469b-8517-54bd12a407d0)

3) Communities of National Environmental Significance Database - RESTRICTED - Metadata only (c01c4693-0a51-4dbc-bbbd-7a07952aa5f6)

4) Fish Biodiversity Hotspot sampling data

These important assets are included in the bioregional assessment, but are unable to be publicly distributed by the Bioregional Assessment Programme due to restrictions in their licensing conditions. Please note that many of these data sets are available directly from their custodian.For more precise details please see the associated explanatory Data Dictionary document enclosed with this dataset.

Purpose

Used for Gloucester subregion for bioregional assessments

Dataset History

The public version of the asset database retains all of the unrestricted components of the Asset database for the Gloucester subregion on 12 February 2016

• any material that is unable to be published or redistributed to a third party by the BA Programme has been removed from the database. The data presented corresponds to the assets published Gloucester subregion product 1.3: Description of the water-dependent asset register and asset list for the Gloucester subregion on 12 February 2016, and the associated Water-dependent asset register and asset list for the Gloucester subregion on 12 February 2016.

Individual spatial features or elements are initially included in database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). In accordance to BA submethodology M02: Compiling water-dependent assets, individual spatial elements are then grouped into assets which are evaluated by project teams to determine whether they meet materiality test 2 (M2), which are assets that are considered to be water dependent.

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 assessment team and incorporated into the AssetList table in the Asset database.

Development of the Asset Register from the Asset database:

Decisions for M0 (fit for BA purpose), M1 (PAE) and M2 (water dependent) determine which assets are included in the "asset list" and "water-dependent asset register" which are published as Product 1.3.

The rule sets are applied as follows:

M0 M1 M2 Result

No n/a n/a Asset is not included in the asset list or the water-dependent asset register

(≠ No) No n/a Asset is not included in the asset list or the water-dependent asset register

(≠ No) Yes No Asset included in published asset list but not in water dependent asset register

(≠ No) Yes Yes Asset included in both asset list and water-dependent asset register

Assessment teams are then able to use the database to assign receptors and impact variables to water-dependent assets and the development of a receptor register as detailed in BA submethodology M03: Assigning receptors to water-dependent assets and the receptor register is then incorporated into the asset database.

At this stage of its development, the Asset database for the Gloucester subregion on 12 February 2016 Public, which this document describes, does contain receptor information, but it was removed from this public version

The source metadata was updated to meet the purpose of the Bioregional Assessment Programme

Dataset Citation

Bioregional Assessment Programme (2014) Asset database for the Gloucester subregion on 12 February 2016 Public v02. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/5def411c-dbc4-4b75-b509-4230964ce0fa.

Dataset Ancestors

• Derived From Standard Instrument Local Environmental Plan (LEP) - Heritage (HER) (NSW)

• Derived From NSW Office of Water GW licence extract linked to spatial locations - GLO v5 UID elements 27032014

• Derived From Asset database for the Gloucester subregion on 21 August 2015

• Derived From Gloucester digitised coal mine boundaries

• Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014

• Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv4 UID 14032014

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

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Asset database for the Gloucester subregion on 12 September 2014

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From National Groundwater Information System (NGIS) v1.1

• Derived From Groundwater Entitlement Data GLO NSW Office of Water 20150320 PersRemoved

• Derived From Asset database for the Gloucester subregion on 29 October 2015

• Derived From Geofabric Surface Cartography - V2.1

• Derived From Groundwater Entitlement Data Gloucester - NSW Office of Water 20150320

• Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports

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

DPVweb provides a central source of information about viruses, viroids and satellites of plants, fungi and protozoa. Comprehensive taxonomic information, including brief descriptions of each family and genus, and classified lists of virus sequences are provided. The database also holds detailed, curated, information for all sequences of viruses, viroids and satellites of plants, fungi and protozoa that are complete or that contain at least one complete gene. For comparative purposes, it also contains a single representative sequence of all other fully sequenced virus species with an RNA or single-stranded DNA genome. The start and end positions of each feature (gene, non-translated region and the like) have been recorded and checked for accuracy. As far as possible, nomenclature for genes and proteins are standardized within genera and families. Sequences of features (either as DNA or amino acid sequences) can be directly downloaded from the website in FASTA format. The sequence information can also be accessed via client software for PC computers (freely downloadable from the website) that enable users to make an easy selection of sequences and features of a chosen virus for further analyses. The public sequence databases contain vast amounts of data on virus genomes but accessing and comparing the data, except for relatively small sets of related viruses can be very time consuming. The procedure is made difficult because some of the sequences on these databases are incorrectly named, poorly annotated or redundant. The NCBI Reference Sequence project (1) provides a comprehensive, integrated, non-redundant set of sequences, including genomic DNA, transcript (RNA) and protein products, for major research organisms. This now includes curated information for a single sequence of each fully sequenced virus species. While this is a welcome development, it can only deal with complete sequences. An important feature of DPV is the opportunity to access genes (and other features) of multiple sequences quickly and accurately. Thus, for example, it is easy to obtain the nucleotide or amino acid sequences of all the available accessions of the coat protein gene of a given virus species or for a group of viruses. To increase its usefulness further, DPVweb also contains a single representative sequence of all other fully sequenced virus species with an RNA or single-stranded DNA (ssDNA) genome. Sponsors: This site is supported by the Association of Applied Biologists and the Zhejiang Academy of Agricultural Sciences, Hangzhou, People''s Republic of China.

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/dataset/9d16592c-543b-42d9-a1f4-0f6d70b9ffe7)

Dataset History

OBJECTID VersionID Notes Date_

1 1 Initial database. 29/08/2014

3 1.1 Update the classification for seven identical assets from Gloucester subregion 16/09/2014

4 1.2 Added in NSW GDEs from Hunter - Central Rivers GDE mapping from NSW DPI (50 635 polygons). 28/01/2015

5 1.3 New AIDs assiged to NSW GDE assets (Existing AID + 20000) to avoid duplication of AIDs assigned in other databases. 12/02/2015

6 1.4 "(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"  16/06/2015

7 2 "(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" 20/07/2015

8 2.1 "(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)"  27/08/2015

9 2.2 "(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"  8/09/2015

10 2.3 "(1) Updated M2 results from the internal review

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

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

          \* Changed "Rivertyles" to "RiverStyles""  22/09/2015

11 2.4 "(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"  20/11/2015

12 2.5 "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." 24/02/2016

Dataset Citation

Bioregional Assessment Programme (2015) Asset database for the Hunter subregion on 24 February 2016. Bioregional Assessment Derived Dataset. Viewed 09 October 2018, http://data.bioregionalassessments.gov.au/dataset/a39290ac-3925-4abc-9ecb-b91e911f008f.

Dataset Ancestors

• 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 Climate Change Corridors Coastal North East NSW

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

• **Derived

The mission of the USGS Gap Analysis Program (GAP) is providing state, regional and national assessments of the conservation status of native vertebrate species and natural land cover types and facilitating the application of this information to land management activities. The PAD-US geodatabase is required to organize and assess the management status (i.e. apply GAP Status Codes) of elements of biodiversity protection. The goal of GAP is to 'keep common species common' by identifying species and plant communities not adequately represented in existing conservation lands. Common species are those not currently threatened with extinction. By identifying their habitats, gap analysis gives land managers and policy makers the information they need to make better-informed decisions when identifying priority areas for conservation. In cooperation with UNEP-World Conservation Monitoring Centre, GAP ensures PAD-US also supports global analyses to inform policy decisions by maintaining World Database for Protected Areas (WDPA) Site Codes and data for International Union for the Conservation of Nature (IUCN) categorized protected areas in the United States. GAP seeks to increase the efficiency and accuracy of PAD-US updates by leveraging resources in protected areas data aggregation and maintenance as described in "A Map of the Future", published following the PAD-US Design Project (July, 2009). While PAD-US was originally developed to support the GAP Mission stated above, the dataset is robust and has been expanded to support the conservation, recreation and public health communities as well. Additional applications become apparent over time.

The Federal Emergency Management Agency (FEMA) creates and provides authoritative data related to flood insurance. Using that data, the Los Angeles County Department of Public Works has developed a public-facing web viewer for accessing flood zone information in the County of Los Angeles (Flood Zone Determination Website). Flood Zones are represented by letters for special flood hazard areas by FEMA. For example, Zone A areas have a 1 percent annual chance of flooding. This flood is also called the 100-year flood. Property owners with structures in Flood Zone A, which have a federally backed mortgage are required to obtain flood insurance.

Looking for more than just a current flood map? Visit Search All Products to access the FEMA website and obtain a full range of flood risk products for your community.

Purpose:

To provide flood zone information to the public.

Supplemental Information:

Data from Flood Insurance Rate Maps (FIRMs), where available digitally, can be found on the official FEMA’s National Flood Hazard Layer. The DFIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper Flood Insurance Rate Maps (FIRMs) FEMA Flood Maps can be obtained from the FEMA Flood Map Service Center (MSC) The National Flood Hazard Layer (NFHL) is a digital database that contains flood hazard mapping data from FEMAs National Flood Insurance Program (NFIP). This map data is derived from Flood Insurance Rate Map (FIRM) databases and Letters of Map Revision (LOMRs). The NFHL is for community officials and members looking to view effective regulatory flood hazard information in a Geographic Information Systems (GIS) application.

FEMA has additional information on the National Flood Insurance Program and Flood Hazard Mapping.

A set of six databases used in a study of the biogeography of Greater Caribbean reef fishes entitled:

Comparing biodiversity databases: Greater Caribbean reef-fishes as a case study

               Iliana Chollett, D. Ross Robertson

Database Authors: D Ross Robertson and Ernesto Peña, Smithsonian Tropical Research Institute, Panamá

This set of six databases contains georeferenced location records from six sources as described below.These six sources provided georeferenced records of occurrence of fishes found in the Greater Caribbean study area (6-330 N, 57-1000 W). Each occurrence record consists of a species name and associated latitude and longitude. Databases included in the comparisons made here are from five major online aggregators. Since their content overlaps to some extent, and OBIS, iDigBio and FishNet collaborate with GBIF, their data might be expected to produce similar biogeographic patterns. STRI includes a curated compendium of data from those five aggregators, enriched with data from many additional sources.

Only reef-associated fish species were included in the present analysis. These mostly represent demersal species known to occur on hard bottoms (coral, rock and oyster substrata), but also include species living on rubble, sand and vegetated bottoms within and around the immediate fringes of reefs, and pelagic species regularly found on reefs. All exotic and non-resident species and species other than reef-associated fishes were excluded from all databases prior to comparisons. Non-residents were defined as otherwise widespread species only rarely seen in the study area. Shore-fishes, including what are generally regarded as reef fishes, include those found in the waters of continental and insular shelves, i.e. between 0-200m. Reef-fish assemblages dominated by shallow-water taxa extend down to that depth in the study area (Baldwin et al. 2018). We used the shelf edge as a breakpoint and excluded records in areas deeper than 200m, identifying those areas using the General Bathymetric Chart of the Oceans (Kapoor, 1981; GEBCO Compilation Group, 2019).

Before the analyses, for all databases, duplicate records were deleted. Subsequently, records in the Pacific or on land were deleted. We used the Global Self-consistent, Hierarchical, High-resolution Geography Database (Wessel & Smith, 1996) to identify these areas. The spatial distribution of species-records in each database is shown in Figure 1 of the publication.

Global Biodiversity Information Facility (GBIF, https://www.gbif.org/): GBIF is an international network and research infrastructure aimed at providing open access to data about all types of life on earth. GBIF works through participant nodes using common standards and open-source tools that enable them to share information. Data from among the 49,000+ datasets hosted by GBIF that were used here range from those on museum specimens collected since the 18th century, to published scientific checklists, to curated local checklists produced by trained science sources such as the Atlantic and Gulf Rapid Assessment Program (https://www.agrra.org/),to geotagged smartphone photos (that act as vouchers allowing verification) shared by amateur and scientific naturalists through iNaturalist (https://www.inaturalist.org/), to unvouchered, unverified and unverifiable observation records from untrained divers such as those contributing to DiveBoard (http://www.diveboard.com). GBIF data are standardized in Darwin Core format. GBIF data were obtained from a polygon of the region of study and subject to taxonomic review and selection after downloading. GBIF data were obtained from a polygon of the study area and subject to taxonomic review after downloading (accessed through the GBIF portal, https://www.gbif.org/, on or about 2019-05-19).

Ocean Biogeographic Information System (OBIS, https://obis.org/): OBIS is a global open-access data and information clearing-house on marine biodiversity (OBIS, 2019) that was adopted as a project of the Intergovernmental Oceanographic Data and Information Exchange of the Intergovernmental Commission of UNESCO . Its range of sources is similar to that of GBIF. OBIS hosts data from organizations or programs that join it as one of 13 “nodes”, and harvest the data from the IPT (Integrated Publishing Toolkit), where providers publish their data. The IPT is developed and maintained by the GBIF, and OBIS is a major contributor of marine data to GBIF. Data are standardized in Darwin Core format. OBIS data were obtained for the region of study by downloading data on each family, then retaining only data inside the study area, which were then subject to taxonomic review and selection (accessed through the OBIS portal, https://obis.org/, on or about 2019-05-19).

Integrated Digitized Biocollections (iDigBio, https://portal.idigbio.org/portal/search): iDigBio is sponsored by the a US National Science Foundation and run by the University of Florida that provides digital data from public, non-federal, US collections. Data are standardized in a Darwin Core format, and provided “as is”. IDigBio joined the GBIF network in 2017. IDigBio records were downloaded from a polygon of the region of study and subject to taxonomic review and selection (accessed through the iDigBio portal, https://portal.idigbio.org/portal/search, on or about 2019-05-19).

FishNet2 (http://www.fishnet2.net/): FishNet2 is a collaborative effort that aggregates data on fish collections around the world to share and distribute data on specimen holdings from ~75 museums, universities and other institutions. FishNet2 distributes data in Darwin Core, and data are provided “as is”. FishNet2 is part of the network VerNet, which has contributed to GBIF since 2013 and became part of IDigBio in 2016. While FishNet2 has made substantial efforts to georeference location-record data it hosts, many hosted records still lack georeferencing. FishNet2 data were obtained from a polygon of the study area and subject to taxonomic review after downloading (accessed through the Fishnet2 Portal, www.fishnet2.org, 2019-05-19).

FishBase (http://www.fishbase.org): FishBase is a global biodiversity information system supervise by a consortium of nine non-USA international institutions, and hosts data on fin fishes and elasmobranchs (Froese & Pauly, 2009). Information presented in FishBase is extracted from the scientific literature, reports and museum or aggregator (GBIF) databases, and standardized by a team of specialists. Data from Fishbase were downloaded for the following ecosystems: Caribbean Sea, Gulf of Mexico, Southeast U.S. Continental Shelf, Atlantic Ocean, Sargasso Sea and Bermuda, and subject to taxonomic review and selection after downloading (2019-05-19).

Smithsonian Tropical Research Institute (STRI; https://biogeodb.stri.si.edu/caribbean/en/pages): The STRI database was compiled by DRR and Ernesto Peña at STRI’s Naos Marine Laboratory, and represents about 15 years accumulation of curated data (see below) from the following sources: data downloaded at roughly two year intervals from the five aggregators; data from online databases of various museums that supply aggregators (data directly downloaded from a museum sometimes differs from that available in an aggregator from the same museum), including the Swedish Museum of Natural History, the American Museum of Natural History, the Natural History Museum of Denmark, the Gulf Coast Research Laboratory, the Colombian Museum of Natural Marine History, the United States National Museum, and the United States Geological Survey; data from national aggregators of Colombia (Sistema de Información Sobre Biodiversidad de Colombia (https://sibcolombia.net/), and Sistema de Información Ambiental Marina de Colombia, https://siam.invemar.org.co/), Mexico (La Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, CONABIO; http://www.conabio.gob.mx/informacion/gis/), and Costa Rica (Museo de Zoologia de la Universidad de Costa Rica, http://museo.biologia.ucr.ac.cr/); verified (by DRR) underwater photographs of fishes taken at known locations; peer reviewed publications containing location information (species descriptions; taxonomic revisions of species, genera and families; regional and local checklists); fisheries reports; digital tagging data for species such as elasmobranchs; diving surveys and collections of local faunas by DRR (e.g. Robertson et al. 2019). In addition selected data from two sources that collect species lists at sites scattered throughout the Greater Caribbean are incorporated: from the Atlantic and Gulf Rapid Reef Assessment program (AGRRA, https://www.agrra.org/: Kramer & Lang, 2003) and from trained citizen scientists who contribute data on fishes to the Reef Environmental Education Foundation’s database (REEF: Pattengill-Semmens & Semmens, 2003). The bibliographic module (https://biogeodb.stri.si.edu/caribbean/en/library) of Robertson & VanTassel (2019) contains ~1700 publications linked to species names, among them the publications from which location data were extracted.

Data from the aggregators is presented “as is” and the aggregators themselves do not do data curation. Duplicates (and occasionally triplicates and quaduplicates) of the same museum record often are included from multiple sources (e.g. the original museum source, derivative checklists, an aggregator), sometimes with slightly different georeferenced coordinates. Data available in one year may subsequently disappear from an aggregator, and different data may be available for the same species under different names (e.g. the old and new names when a species is reassigned to another genus). Errors, sometimes large errors (Robertson, 2008), are common in aggregator data, from museums as well as other sources, and longstanding errors can seem to take on a

The mission of the USGS Gap Analysis Program (GAP) is providing state, regional and national assessments of the conservation status of native vertebrate species and natural land cover types and facilitating the application of this information to land management activities. The PAD-US geodatabase is required to organize and assess the management status (i.e. apply GAP Status Codes) of elements of biodiversity protection. The goal of GAP is to 'keep common species common' by identifying species and plant communities not adequately represented in existing conservation lands. Common species are those not currently threatened with extinction. By identifying their habitats, gap analysis gives land managers and policy makers the information they need to make better-informed decisions when identifying priority areas for conservation. In cooperation with UNEP-World Conservation Monitoring Centre, GAP ensures PAD-US also supports global analyses to inform policy decisions by maintaining World Database for Protected Areas (WDPA) Site Codes and data for International Union for the Conservation of Nature (IUCN) categorized protected areas in the United States. GAP seeks to increase the efficiency and accuracy of PAD-US updates by leveraging resources in protected areas data aggregation and maintenance as described in "A Map of the Future", published following the PAD-US Design Project (July, 2009). While PAD-US was originally developed to support the GAP Mission stated above, the dataset is robust and has been expanded to support the conservation, recreation and public health communities as well. Additional applications become apparent over time.

United States Forest Service lands within the Rio Grande River Basin. The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

The National Public Toilet Map shows the location of more than 17,000 public\r and private public toilet facilities across Australia. Details of toilet\r facilities can also be found along major travel routes and for shorter\r journeys as well. Useful information is provided about each toilet, such as\r location, opening hours, availability of baby change rooms, accessibility for\r people with disabilities and the details of other nearby toilets.\r \r Licence\r \r To download the National Public Toilet Map dataset, you must agree to the\r following terms and conditions:\r \r These are the terms and conditions (the Terms) upon which the Commonwealth of\r Australia represented by the Department of Finance and Deregulation (ABN 61\r 970 632 495) of Canberra A.C.T. (the Commonwealth, us, we or our as the\r context requires) makes available to you the Database referred to below. Your\r right to access the Database for the Permitted Purpose is conditional upon you\r first agreeing to the Terms. You may not access the Database if you do not\r accept the Terms.\r \r By accessing the database you will be deemed to have accepted the Terms.\r \r By accepting the Terms you warrant to us that you are of legal age and have\r capacity to form a binding contract with the Commonwealth.\r \r Before continuing you should print or save a local copy of the Terms for your\r records.\r \r 1. Definitions\r \r Commencement Date\r \r means the date you accept the Terms (or are deemed to accept the Terms).\r \r Database\r \r means the database (known as the National Public Toilet Database) owned by and\r provided on behalf of the Commonwealth including any updates provided by or on\r behalf of the Commonwealth, that records some or all of the following details\r for public toilets in Australia:\r \r (a) toilet name;\r \r (b) address;\r \r (c) latitude and longitude;\r \r (d) general toilet features;\r \r (e) location;\r \r (f) accessibility;\r \r (g) opening hours;\r \r (h) additional features (e.g. showers, baby change facilities etc);\r \r (i) notes (e.g. coin operated showers etc).\r \r Derivative Product\r \r means any product or service that you may design or build, or have designed or\r built on your behalf, that includes or otherwise incorporates the Database (or\r part of the Database).\r \r Intellectual Property Rights\r \r (or IPRs) means all intellectual property rights, including but not\r limited to all rights existing or arising in respect of the Database whether\r or not such rights are registered or capable of being registered.\r \r Permitted Purpose\r \r means the right to:\r \r (a) use, adapt, reproduce, publish and communicate to the public the Database\r in any format (including any part of the Database); and\r \r (b) design and build, or have designed and built on your behalf, any\r Derivative Products.\r \r Terms\r \r means the terms and conditions of this licence.\r \r 2. Commencement\r \r 2.1 The Terms commence on the Commencement Date and continue unless and until\r terminated in accordance with clause 8.\r \r 3. Grant of Licence\r \r 3.1 We grant you a non-exclusive, perpetual, royalty free, non-transferable\r and world-wide licence to access the Database for the Permitted Purpose.\r \r 3.2 You may not sublicense your rights under these Terms to any person. If you\r require another person to access the Database for the Permitted Purpose\r (including a person you engage to design or build a Derivative Product on your\r behalf), that person must obtain a copy of the Database from the\r data.australia.gov.au website and comply with the Terms of this licence.\r \r 4. Exclusion of liability\r \r 4.1 You agree that most of the data and information contained in the Database\r is provided by organisations and other entities on a voluntary and ad hoc\r basis.\r \r 4.2 We provide the Database to you on the basis that:\r \r (a) no warranty is given that the Database is accurate, complete or fit for\r any particular purpose, including the Permitted Purpose; and\r \r (b) you are responsible for and you accept all risks arising in connection\r with your access and disclosure of the Database; and\r \r (c) we may cease to make the Database available (or cease to make updates to\r the Database) at any time.\r \r 5. Currency of data\r \r 5.1 If a purpose for which you access the Database is to assist people to\r locate or know the features of public toilets, you must update your copy of\r the Database and any Derivative Products as soon as reasonably practicable\r after an update to the Database is made available on the data.australia.gov.au\r website (or successor site).\r \r 6. Intellectual Property Rights\r \r 6.1 You acknowledge and agree that the Database represents the Commonwealthas\r exclusive property and that the Commonwealth owns any and all IPRs in the\r Database.\r \r 7. User Disclaimer\r \r 7.1 You must ensure that if and when you make any Derivative Product available\r to third parties that you do so on terms that ensure users they understand\r that we do not guarantee, and accept no risk in respect of, the accuracy,\r currency or completeness of the Derivative Product.\r \r 8. Termination\r \r 8.1 We may by notice immediately terminate these Terms if you breach any\r obligation contained in these Terms.\r \r 8.2 In the event of termination of these Terms, you may continue to exercise\r your licensed rights in respect of your Derivative Products in existence prior\r to the date of expiry or termination but you have no right to access the Data\r \r base for any other purpose (including to update your Derivative Products).\r \r 9. General Provisions\r \r 9.1 These Terms are governed by, and are to be construed in accordance with,\r the law of the Australian Capital Territory.\r \r 9.2 We may vary these terms at any time by reasonable notice to you.\r \r

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets including Natural Resource Management regions, and Australian and state and territory government databases. 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.

This data set holds the publicly-available version of the database of water-dependent assets that was compiled for the bioregional assessment (BA) of the Clarence-Moreton subregion as part of the Bioregional Assessment Technical Programme. Though all life is dependent on water, for the purposes of a bioregional assessment, a water-dependent asset is an asset potentially impacted by changes in the groundwater and/or surface water regime due to coal resource development. The water must be other than local rainfall. Examples include wetlands, rivers, bores and groundwater dependent ecosystems.

A single asset is represented spatially in the asset database by single or multiple spatial features (point, line or polygon). Individual points, lines or polygons are termed elements.

This dataset contains the unrestricted publicly-available components of spatial and non-spatial (attribute) data of the (restricted) Asset database for the Clarence-Moreton bioregion on 24 February 2016 (6d11ffbc-ea57-49cb-8e00-f97761e0c5d6; see the lineage field for a direct link). The database is provided primarily as an ESRI File geodatabase (.gdb), which is able to be opened in readily available open source software such as QGIS. Other formats include the Microsoft Access database (.mdb in ESRI Personal Geodatabase format), industry-standard ESRI Shapefiles and tab-delimited text files of all the attribute tables.

The restricted version of the Clarence-Moreton Asset database has a total count of 294961 Elements and 2708 Assets. In the public version of the Asset Clarence-Moreton database 172 084 spatial Element features (\~58%) have been removed from the Element List and Element Layer(s) and 802 spatial Assets (\~30%) have been removed from the spatial Asset Layer(s)

The elements/assets removed from the restricted Asset Database are from the following data sources:

1) Species Profile and Threats Database (SPRAT) - RESTRICTED - Metadata only) (7276dd93-cc8c-4c01-8df0-cef743c72112)

2) Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) (Internal 878f6780-be97-469b-8517-54bd12a407d0)

3) Communities of National Environmental Significance Database - RESTRICTED - Metadata only (c01c4693-0a51-4dbc-bbbd-7a07952aa5f6)

4) Northern Rivers CMA GDEs (DPI pre-release) - RESTRICTED - Metadata only ((ac1bd285-5f50-46e2-bc04-b21e8e182a62)

These important assets are included in the bioregional assessment, but are unable to be publicly distributed by the Bioregional Assessment Programme due to restrictions in their licensing conditions. Please note that many of these data sets are available directly from their custodian. For more precise details please see the associated explanatory Data Dictionary document enclosed with this dataset.

Purpose

The data are for any external party that wants to access the asset database used for the assessment. The BATP is required to release these wherever possible, to comply with the requirements of transparency and repeatability.

Dataset History

The public version of the asset database retains all of the unrestricted components of the Asset database for the Clarence-Moreton bioregion on 24 February 2016 - any material that is unable to be published or redistributed to a third party by the BA Programme has been removed from the database. The data presented corresponds to the assets published Clarence-Moreton bioregion product 1.3: Description of the water-dependent asset register and asset list for the Clarence-Moreton bioregion on 24 February 2016 , and the associated Water-dependent asset register and asset list for the Clarence-Moreton bioregion on 24 February 2016 .

Individual spatial features or elements are initially included in database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). In accordance to BA submethodology M02: Compiling water-dependent assets, individual spatial elements are then grouped into assets which are evaluated by project teams to determine whether they meet materiality test 2 (M2), which are assets that are considered to be water dependent.

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 assessment team and incorporated into the AssetList table in the Asset database.

Development of the Asset Register from the Asset database:

Decisions for M0 (fit for BA purpose), M1 (PAE) and M2 (water dependent) determine which assets are included in the "asset list" and "water-dependent asset register" which are published as Product 1.3.

The rule sets are applied as follows:

M0\tM1\tM2\tResult

No\tn/a\tn/a\tAsset is not included in the asset list or the water-dependent asset register

(≠ No)\tNo\tn/a\tAsset is not included in the asset list or the water-dependent asset register

(≠ No)\tYes\tNo\tAsset included in published asset list but not in water dependent asset register

(≠ No)\tYes\tYes\tAsset included in both asset list and water-dependent asset register

Assessment teams are then able to use the database to assign receptors and impact variables to water-dependent assets and the development of a receptor register as detailed in BA submethodology M03: Assigning receptors to water-dependent assets and the receptor register is then incorporated into the asset database.

At this stage of its development, the Asset database for the Clarence-Moreton bioregion on 24 February 2016, which this document describes, does contain receptor information, but the receptor information was removed from this public version.

Dataset Citation

Bioregional Assessment Programme (2016) Asset database for the Clarence-Moreton bioregion on 24 February 2016 Public 20170112 v03. Bioregional Assessment Derived Dataset. Viewed 20 March 2017, http://data.bioregionalassessments.gov.au/dataset/b3338edb-a6df-47ae-944f-0be927639bca.

Dataset Ancestors

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204

• Derived From Combined Surface Waterbodies for the Clarence-Moreton bioregion

• Derived From Queensland QLD - Regional - NRM - Water Asset Information Tool - WAIT - databases

• Derived From Version 02 Asset list for Clarence Morton 8/8/2014 - ERIN ORIGINAL DATA

• Derived From Asset database for the Clarence-Moreton bioregion on 11 December 2014, minor version v20150603

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

• 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 Geofabric Surface Catchments - V2.1

• Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores v3 03122014

• Derived From Australia World Heritage Areas

• Derived From National Groundwater Information System (NGIS) v1.1

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

• Derived From Australia - Present Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)

• Derived From Queensland QLD Regional CMA Water Asset Information WAIT tool databases RESTRICTED Includes ALL Reports

• Derived From Northern Rivers CMA GDEs (DRAFT DPI pre-release)

• Derived From Australia - Species of National Environmental Significance Database

• Derived From Queensland wetland data version 3 - wetland areas - WETCLASS: E, L, M, P, R

• Derived From South East Queensland GDE (draft)

• Derived From [Natural Resource Management (NRM)

Open data, commonly referred to by the English term "Open Data" even in the Italian context, are certain types of data that are freely accessible to everyone, without copyright restrictions, patents, or other forms of control that limit their reproduction. The opening of public databases promotes transparency, innovation, and efficiency in public administration and is an opportunity to create value-added services for high-performing and differentiated services and to help generate economic and business growth. With the "Open Data Project, the Useful Ones," the Municipality of Rimini aims to publish and share the Open Data held by the municipal administration to promote its dissemination, fostering policies of transparency, access, and participation. The project is part of the participatory path of the Digital Agenda of the Municipality of Rimini, the plan of which was approved with resolution G.C. n. 342 of 02/12/2014. https://sites.google.com/site/agendadigitalelocalerimini/piano The data opening process of the Municipality of Rimini, already started experimentally in previous years, has been defined with the approval of the opening strategy outlined in the "Guidelines for the reuse and dissemination of public data of the Municipality of Rimini" approved by the City Council with Resolution n. 270 of 11/08/2015, and with the establishment of the open data team through the formalization of a working group composed of contact persons identified within each Directorate, from which a path of involvement of the entire municipal administration was initiated, structured in the phases of awareness and reconnaissance of the entity's information assets to identify databases useful for publication. To this end, this online section "OPEN DATA Municipality of Rimini" has been implemented, created according to the standards set by the national Guidelines for the enhancement of public information assets, into which the datasets already published previously have been merged and where those of new identification or request will be published as they become available. The site is based on an open-source data cataloging software called CKAN, developed by the Open Knowledge Foundation: a non-profit organization that promotes free knowledge. Each entry contains a description of the data (metadata) and other useful information, such as available formats, the data holder, the license, and the topics that the data address. For geographic open data, the Geo open data web site http://data.sit-rimini.opendata.arcgis.com/, a section developed ad hoc by the Municipality of Rimini on the Esri's ArcGIS Online platform, which has made geographic open data more complete and usable, viewable in preview in graphic and tabular format, together with the metadata, can also be accessed from these pages. Send us suggestions, proposals and requests through the twitter, facebook, email channels. Translated from Italian Original Text: I dati aperti, comunemente chiamati con il termine inglese Open Data anche nel contesto italiano, sono alcune tipologie di dati liberamente accessibili a tutti, senza restrizioni di copyright, brevetti o altre forme di controllo che ne limitino la riproduzione. L'apertura delle banche dati pubbliche favorisce la trasparenza, l'innovazione e l'efficienza della PA ed è un'opportunità per creare servizi a valore aggiunto per prestazioni performanti e differenziate e per contribuire a generare crescita economica e d'impresa. Con il "Progetto Open Data, quelli utili" il Comune di Rimini si pone come obiettivo la pubblicazione e condivisione degli Open Data in possesso dell'Amministrazione comunale per promuoverne la diffusione favorendo politiche di trasparenza, accesso e partecipazione. Il progetto fa parte del percorso partecipativo dell' Agenda Digitale del Comune di Rimini il cui piano è stato approvato con deliberazione G.C. n. 342 del 02/12/2014. https://sites.google.com/site/agendadigitalelocalerimini/piano Il processo di apertura dei dati del Comune di Rimini, già avviato in fase sperimentale negli scorsi anni, ha avuto una sua definizione con l'approvazione della strategia di apertura delineata nelle "Linee guida per il riutilizzo e la diffusione dei dati pubblici del Comune di Rimini" approvate dalla Giunta Comunale con Deliberazione n.270 del 11/08/2015, e con l'istituzione del team open data avvenuta con la formalizzazione di un gruppo di lavoro composto da referenti individuati nell'ambito di ogni Direzione, a partire dalle quali è stato avviato un percorso di coinvolgimento dell'intera amministrazione comunale articolato nelle fasi di sensibilizzazione e ricognizione del patrimonio informativo dell'ente per poter individuare le banche dati utili alla pubblicazione. A tal fine è stata implementata questa sezione online "OPEN DATA Comune di Rimini", realizzata secondo gli standard fissati dalle Linee guida nazionali per la valorizzazione del patrimonio informativo pubblico, in cui sono confluiti i dataset già pubblicati in precedenza e dove verranno pubblicati man mano quelli di nuova individuazione o richiesta. Il sito è basato su un software opensource di catalogazione dei dati, chiamato CKAN, sviluppato dalla Open Knowledge Foundation: un'organizzazione noprofit che promuove il sapere libero. Ogni voce contiene una descrizione dei dati (metadati) e altre informazioni utili, come i formati disponibili, il detentore, la licenza e gli argomenti che i dati affrontano. Per gli open data geografici viene raggiunta da queste pagine anche la Geo open data web site http://data.sit-rimini.opendata.arcgis.com/ sezione sviluppata ad hoc dal Comune di Rimini sulla piattaforma Arcgis on line della Esri che ha reso gli open data geografici più completi e fruibili, visualizzabili in anteprima in formato grafico e tabellare, unitamente ai metadati. Inviateci suggerimenti, proposte e richieste attraverso i canali twitter, facebook,email.

[Note: Integrated as part of FoodData Central, April 2019.] The database consists of several sets of data: food descriptions, nutrients, weights and measures, footnotes, and sources of data. The Nutrient Data file contains mean nutrient values per 100 g of the edible portion of food, along with fields to further describe the mean value. Information is provided on household measures for food items. Weights are given for edible material without refuse. Footnotes are provided for a few items where information about food description, weights and measures, or nutrient values could not be accommodated in existing fields. Data have been compiled from published and unpublished sources. Published data sources include the scientific literature. Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research Service (ARS). Updated data have been published electronically on the USDA Nutrient Data Laboratory (NDL) web site since 1992. Standard Reference (SR) 28 includes composition data for all the food groups and nutrients published in the 21 volumes of "Agriculture Handbook 8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963). SR28 supersedes all previous releases, including the printed versions, in the event of any differences. Attribution for photos: Photo 1: k7246-9 Copyright free, public domain photo by Scott Bauer Photo 2: k8234-2 Copyright free, public domain photo by Scott Bauer Resources in this dataset:Resource Title: READ ME - Documentation and User Guide - Composition of Foods Raw, Processed, Prepared - USDA National Nutrient Database for Standard Reference, Release 28. File Name: sr28_doc.pdfResource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: ASCII (6.0Mb; ISO/IEC 8859-1). File Name: sr28asc.zipResource Description: Delimited file suitable for importing into many programs. The tables are organized in a relational format, and can be used with a relational database management system (RDBMS), which will allow you to form your own queries and generate custom reports.Resource Title: ACCESS (25.2Mb). File Name: sr28db.zipResource Description: This file contains the SR28 data imported into a Microsoft Access (2007 or later) database. It includes relationships between files and a few sample queries and reports.Resource Title: ASCII (Abbreviated; 1.1Mb; ISO/IEC 8859-1). File Name: sr28abbr.zipResource Description: Delimited file suitable for importing into many programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Title: Excel (Abbreviated; 2.9Mb). File Name: sr28abxl.zipResource Description: For use with Microsoft Excel (2007 or later), but can also be used by many other spreadsheet programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/ Resource Title: ASCII (Update Files; 1.1Mb; ISO/IEC 8859-1). File Name: sr28upd.zipResource Description: Update Files - Contains updates for those users who have loaded Release 27 into their own programs and wish to do their own updates. These files contain the updates between SR27 and SR28. Delimited file suitable for import into many programs.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)