Publicly accessible databases often impose query limits or require registration. Even when I maintain public and limit-free APIs, I never wanted to host a public database because I tend to think that the connection strings are a problem for the user.

See https://databases.pacha.dev

As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of 1244.08; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

OR-Trans is a GIS road centerline dataset compiled from numerous sources of data throughout the state. Each dataset is from the road authority responsible for (or assigned data maintenance for) the road data each dataset contains. Data from each dataset is compiled into a statewide dataset that has the best available data from each road authority for their jurisdiction (or assigned data maintenance responsibility). Data is stored in a SQL database and exported in numerous formats.

Fork this notebook to get started on accessing data in the BigQuery dataset by writing SQL queries using the BQhelper module.

Context

Google Patents Public Data, provided by IFI CLAIMS Patent Services, is a worldwide bibliographic and US full-text dataset of patent publications. Patent information accessibility is critical for examining new patents, informing public policy decisions, managing corporate investment in intellectual property, and promoting future scientific innovation. The growing number of available patent data sources means researchers often spend more time downloading, parsing, loading, syncing and managing local databases than conducting analysis. With these new datasets, researchers and companies can access the data they need from multiple sources in one place, thus spending more time on analysis than data preparation.

Content

The Google Patents Public Data dataset contains a collection of publicly accessible, connected database tables for empirical analysis of the international patent system.

Acknowledgements

Data Origin: https://bigquery.cloud.google.com/dataset/patents-public-data:patents

For more info, see the documentation at https://developers.google.com/web/tools/chrome-user-experience-report/

“Google Patents Public Data” by IFI CLAIMS Patent Services and Google is licensed under a Creative Commons Attribution 4.0 International License.

Banner photo by Helloquence on Unsplash

Calculation of sensitivity and specificity for probabilistic matching without manual review, not including address variables and using an ETS dataset that only including non-UK born individuals.

The open data portal catalogue is a downloadable dataset containing some key metadata for the general datasets available on the Government of Canada's Open Data portal. Resource 1 is generated using the ckanapi tool (external link) Resources 2 - 8 are generated using the Flatterer (external link) utility. ###Description of resources: 1. Dataset is a JSON Lines (external link) file where the metadata of each Dataset/Open Information Record is one line of JSON. The file is compressed with GZip. The file is heavily nested and recommended for users familiar with working with nested JSON. 2. Catalogue is a XLSX workbook where the nested metadata of each Dataset/Open Information Record is flattened into worksheets for each type of metadata. 3. datasets metadata contains metadata at the dataset level. This is also referred to as the package in some CKAN documentation. This is the main table/worksheet in the SQLite database and XLSX output. 4. Resources Metadata contains the metadata for the resources contained within each dataset. 5. resource views metadata contains the metadata for the views applied to each resource, if a resource has a view configured. 6. datastore fields metadata contains the DataStore information for CSV datasets that have been loaded into the DataStore. This information is displayed in the Data Dictionary for DataStore enabled CSVs. 7. Data Package Fields contains a description of the fields available in each of the tables within the Catalogue, as well as the count of the number of records each table contains. 8. data package entity relation diagram Displays the title and format for column, in each table in the Data Package in the form of a ERD Diagram. The Data Package resource offers a text based version. 9. SQLite Database is a .db database, similar in structure to Catalogue. This can be queried with database or analytical software tools for doing analysis.

As of June 2024, the most popular relational database management system (RDBMS) worldwide was Oracle, with a ranking score of 1244.08. Oracle was also the most popular DBMS overall. MySQL and Microsoft SQL server rounded out the top three.

Global In-memory database market is expected to revenue of around USD 36.21 billion by 2032, growing at a CAGR of 19.2% between 2024 and 2032.

Combining human expertise with information from book-consumer digital data may generate what it takes to face the following changes in such a critical market. Along with the publishing industry, researchers rely on book-related data to develop tools and applications, drawing constructive conclusions to make better informed and faster decisions. Such solutions range from best-selling prediction models to natural language processing to classify raw text. Besides require complex Artificial Intelligence (AI) methods, all of them are essentially data-dependent, mainly book-related data-dependent.

Data, and more specifically data growth, is essential for developing and performing such AI-powered applications. None of these efforts can be achieved without a preliminary collection of data on literary works, readers, and their reading habits. Therefore, it is critically important to build and make available datasets that fully comprise the essential elements of the book industry ecosystem. Although some efforts have been made for English language books, little has been done regarding other lesser-spoken languages, such as Portuguese. The evaluation of specific data is of fundamental importance for literature analysis, as Portuguese has its own literary peculiarities. Hence, we present PPORTAL, a Public domain PORTuguese-lAnguage Literature dataset. PPORTAL's contributions are summarized as follows:

Data integration of numerous public domain works from three digital libraries;

Enriched metadata for works, authors and online reviews extracted from Goodreads;

Feature engineering on the metadata to create meaningful additional features; and

Unrestricted access in two formats (SQL database and compressed .csv files

Context

The USPTO grants US patents to inventors and assignees all over the world. For researchers in particular, PatentsView is intended to encourage the study and understanding of the intellectual property (IP) and innovation system; to serve as a fundamental function of the government in creating “public good” platforms in these data; and to eliminate redundant cleaning, converting and matching of these data by individual researchers, thus freeing up researcher time to do what they do best—study IP, innovation, and technological change.

Content

PatentsView Data is a database that longitudinally links inventors, their organizations, locations, and overall patenting activity. The dataset uses data derived from USPTO bulk data files.

Fork this notebook to get started on accessing data in the BigQuery dataset using the BQhelper package to write SQL queries.

Acknowledgements

“PatentsView” by the USPTO, US Department of Agriculture (USDA), the Center for the Science of Science and Innovation Policy, New York University, the University of California at Berkeley, Twin Arch Technologies, and Periscopic, used under CC BY 4.0.

Data Origin: https://bigquery.cloud.google.com/dataset/patents-public-data:patentsview

Banner photo by rawpixel on Unsplash

The State Contract and Procurement Registration System (SCPRS) was established in 2003, as a centralized database of information on State contracts and purchases over $5000. eSCPRS represents the data captured in the State's eProcurement (eP) system, Bidsync, as of March 16, 2009. The data provided is an extract from that system for fiscal years 2012-2013, 2013-2014, and 2014-2015

Data Limitations:
Some purchase orders have multiple UNSPSC numbers, however only first was used to identify the purchase order. Multiple UNSPSC numbers were included to provide additional data for a DGS special event however this affects the formatting of the file. The source system Bidsync is being deprecated and these issues will be resolved in the future as state systems transition to Fi$cal.

Data Collection Methodology:

The data collection process starts with a data file from eSCPRS that is scrubbed and standardized prior to being uploaded into a SQL Server database. There are four primary tables. The Supplier, Department and United Nations Standard Products and Services Code (UNSPSC) tables are reference tables. The Supplier and Department tables are updated and mapped to the appropriate numbering schema and naming conventions. The UNSPSC table is used to categorize line item information and requires no further manipulation. The Purchase Order table contains raw data that requires conversion to the correct data format and mapping to the corresponding data fields. A stacking method is applied to the table to eliminate blanks where needed. Extraneous characters are removed from fields. The four tables are joined together and queries are executed to update the final Purchase Order Dataset table. Once the scrubbing and standardization process is complete the data is then uploaded into the SQL Server database.

Secondary/Related Resources:

• State Contract Manual (SCM) vol. 2 http://www.dgs.ca.gov/pd/Resources/publications/SCM2.aspx
• State Contract Manual (SCM) vol. 3 http://www.dgs.ca.gov/pd/Resources/publications/SCM3.aspx
• Buying Green http://www.dgs.ca.gov/buyinggreen/Home.aspx
• United Nations Standard Products and Services Code, http://www.unspsc.org/

The size of the Relational Database Market was valued at USD 19942.01 million in 2023 and is projected to reach USD 45481.69 million by 2032, with an expected CAGR of 12.50% during the forecast period. This growth trajectory is primarily driven by the advent of hybrid seeds, which offer superior yield and improved disease resistance. Government initiatives aimed at promoting food security and the adoption of advanced technologies further fuel market expansion. Key applications for hybrid seeds encompass field crops, horticulture, and fodder crops. Leading players in the market include Monsanto, DuPont Pioneer, Syngenta, and Bayer CropScience. Recent developments include: October 2022: Oracle released latest advancements in database technology with the announcement of Oracle Database 23c Beta. It accommodates diverse data types, workloads, and development styles. The release incorporates numerous innovations across Oracle's database services and product portfolio., October 2023: Microsoft has launched a public preview of a new Azure SQL Database free offering, marking a significant addition to its cloud services. Users can access a 32 GB general purpose, serverless Azure SQL database with 100,000 vCore seconds of compute free monthly..

Abstract

The public version of this Asset database can be accessed via the following dataset:

Asset database for the Cooper subregion on 27 August 2015 Public (526707e0-9d32-47de-a198-9c8f35761a7e)

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.

The asset database for Cooper subregion (v3) supersedes previous version (v2) of the Cooper Asset database (Asset database for the Cooper subregion on 14 August 2015, 5c3697e6-8077-4de7-b674-e0dfc33b570c). The M2_Reason in the Assetlist table and DecisionBrief in the AssetDecisions table have been updated with short descriptions (<255 characters) provided by project team 21/8, and the draft "water-dependent asset register and asset list" (BA-LEB-COO-130-WaterDependentAssetRegister-AssetList-V20150827) also updated accordingly. This change was made to avoid truncation in the brief reasons fields of the database and asset register. There have been no changes to assets or asset numbers.

This dataset contains a combination of spatial and non-spatial (attribute) components of the Cooper subregion Asset List - an mdb file (readable as an MS Access database or as an ESRI personal geodatabase) holds the non-spatial tabular attribute data, and an ESRI file geodatabase contains the spatial data layers, which are attributed only with unique identifiers ("AID" for assets, and "ElementID" for elements). The dataset also contains an update of the draft "Water-dependent asset register and asset list" spreadsheet (BA-NIC-COO-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx).

The tabular attribute data can be joined in a GIS to the "Assetlist" table in the mdb database using the "AID" field to view asset attributes (BA attribution). To view the more detailed attribution at the element-level, the intermediate table "Element_to_asset" can be joined to the assets spatial datasets using AID, and then joining the individual attribute tables from the Access database using the common "ElementID" fields. Alternatively, the spatial feature layers representing elements can be linked directly to the individual attribute tables in the Access database using "ElementID", but this arrangement will not provide the asset-level groupings.

Further information is provided in the accompanying document, "COO_asset_database_doc20150827.doc" located within this dataset.

Dataset History

Version ID Date Notes

1.0 27/03/2015 Initial database

2.0 14/08/2015 "(1) Updated the database for M2 test results provided from COO assessment team and created the draft BA-LEB-COO-130-WaterDependentAssetRegister-AssetList-V20150814.xlsx

(2) updated the group, subgroup, class and depth for (up to) 2 NRM WAIT assets to cooperate the feedback to OWS from relevant SA NRM regional office (whose staff missed the asset workshop). The AIDs and names of those assets are listed in table LUT_changed_asset_class_20150814 in COO_asset_database_20150814.mdb

(3) As a result of (2), added one new asset separated from one existing asset. This asset and its parent are listed in table LUT_ADD_1_asste_20150814 in COO_asset_database_20150814.mdb. The M2 test result for this asset is inherited from its parent in this version

(5) Added Appendix C in COO_asset_database_doc_201500814.doc is about total elements/assets in current Group and subgroup

(6)Added Four SQL queries (Find_All_Used_Assets, Find_All_WD_Assets, Find_Amount_Asset_in_Class and Find_Amount_Elements_in_Class) in COO_asset_database_20150814.mdb.mdb for total assets and total numbers

(7)The databases, especially spatial database (COO_asset_database_20150814Only.gdb), were changed such as duplicated attribute fields in spatial data were removed and only ID field is kept. The user needs to join the Table Assetlist or Elementlist to the relevant spatial data"

3.0 27/08/2015 M2_Reason in the Assetlist table and DecisionBrief in the AssetDecisions table have been updated with short descriptions (<255 characters) provided by project team 21/8, and the draft "water-dependent asset register and asset list" (BA-LEB-COO-130-WaterDependentAssetRegister-AssetList-V20150827) also updated accordingly. No changes to asset numbers.

Dataset Citation

Bioregional Assessment Programme (2014) Asset database for the Cooper subregion on 27 August 2015. Bioregional Assessment Derived Dataset. Viewed 27 November 2017, http://data.bioregionalassessments.gov.au/dataset/0b122b2b-e5fe-4166-93d1-3b94fc440c82.

Dataset Ancestors

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

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

• Derived From Matters of State environmental significance (version 4.1), Queensland

• Derived From Geofabric Surface Network - V2.1

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

• Derived From South Australia SA - Regional - NRM Board - Water Asset Information Tool - WAIT - databases

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

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

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

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

• Derived From Queensland wetland data version 3 - wetland areas.

• Derived From SA Department of Environment, Water and Natural Resources (DEWNR) Water Management Areas 141007

• Derived From South Australian Wetlands - Groundwater Dependent Ecosystems (GDE) Classification

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

• Derived From Asset database for the Cooper subregion on 14 August 2015

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

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

• Derived From Ramsar Wetlands of Australia

• Derived From Permanent and Semi-Permanent Waterbodies of the Lake Eyre Basin (Queensland and South Australia) (DRAFT)

• Derived From SA EconomicElements v1 20141201

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores and NGIS v4 28072014

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

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

• Derived From SA Department of Environment, Water and Natural Resources (DEWNR) Groundwater Licences 141007

• Derived From Lake Eyre Basin (LEB) Aquatic Ecosystems Mapping and Classification

• Derived From Australia - Species of National Environmental Significance Database

• Derived From Asset database for the Cooper subregion on 27 March 2015

• Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal

• Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public)

• Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release)

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

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

This is a collection of layers created by Tian Xie(Intern in DDP) in August, 2018. This collection includes Detroit Parcel Data(Parcel_collector), InfoUSA business data(BIZ_INFOUSA), and building data(Building). The building and business data have been edited by Tian during field research and have attached images.

Direct monitoring of chemical concentrations in different environmental and biological media is critical to understanding the mechanisms by which human and ecological receptors are exposed to exogenous chemicals. Monitoring data provides evidence of chemical occurrence in different media and can be used to inform exposure assessments. The monitoring data provide required information for parameterization and evaluation of predictive models based on chemical uses, fate and transport, and release or emission processes. Finally, these data are useful in supporting regulatory chemical assessment and decision-making. There are a wide variety of public monitoring data available from existing government programs, historical efforts, public data repositories, and peer-reviewed literature databases. However, these data are difficult to access and analyze in a coordinated manner. Here, data from 20 individual public monitoring data sources were extracted, curated to unique chemical identifiers (EPA’s DSSTox Substance Identifiers, DTXSID) and medium identifiers (e.g., blood, drinking water, indoor air, ambient air, etc.) and harmonized into a sustainable machine-readable data format for support of exposure assessments. The database is provided here as a compressed MySQL dump, containing SQL statements that would recreate MMDB in its entirety.Note that the full uncompressed size of this file is over 300 GB. Users should be aware of space requirements before uncompressing.Curation updates (minor error fixes) for MMDB releases are available at https://doi.org/10.23645/epacomptox.16674298.v5.This database accompanies the publication “A Harmonized Chemical Monitoring Database for Support of Exposure Assessments”, by Isaacs K.K., Wall J.T., Williams A.R., Hobbie K.A., Sobus J.R., Ulrich E., Lyons D., Dionisio K.L., Williams A.J., Grulke C., Foster C.A., McCoy J., and Bevington C.

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.

The asset database for Pedirka subregion (v4) supersedes previous versions of the Arckaringa Asset database (Asset database for the Pedirka subregion on 27 August 2015, GUID: 62dc178f-65ae-4e6a-b5d4-12895b37d04c). Total number of registered water assets in this V4 database was increased by 2 because two assets changed M2 test from "No" to "Yes" from M2 test review done by Ecologist group.

This dataset contains v4 of the Asset database (PED_asset_database_20160308.mdb), a Geodatabase version for GIS mapping purposes (PED_asset_database_20160308_GISOnly.gdb), an updated draft Water Dependent Asset Register spreadsheet (BA-LEB-PED-130-WaterDependentAssetRegister-AssetList-v20160308.xlsx), a data dictionary (PED_asset_database_doc_20160308.doc), and a folder (NRM_DOC) containing documentation associated with the Water Asset Information Tool (WAIT) process.

The tabular attribute data can be joined in a GIS to the "Assetlist" table in the mdb database using the "AID" field to view asset attributes (BA attribution). To view the more detailed attribution at the element-level, the intermediate table "Element_to_asset" can be joined to the assets spatial datasets using AID, and then joining the individual attribute tables from the Access database using the common "ElementID" fields. Alternatively, the spatial feature layers representing elements can be linked directly to the individual attribute tables in the Access database using "ElementID", but this arrangement will not provide the asset-level groupings.

Further information is provided in the accompanying document, "PED_asset_database_doc_20160308.doc" located within this dataset.

The public version of this asset database can be accessed via the following dataset: Asset database for the Pedirka subregion on 08 March 2016 Public (https://data.gov.au/data/dataset/c104deb9-5969-4427-977a-12f284564c93).

Dataset History

VersionID\tDate\tNotes

1.0\t13/03/2015\tInitial database

1.1\t19/03/2015\tAdd SA point Eco data (2 assets and 28 Elements) and fixed Note field value in table AssetDecisions

2\t7/08/2015\t"(1) Updated the database for M2 test results provided from PED assessment team and created the draft BA-LEB-PED-130-WaterDependentAssetRegister-AssetList-V20150807.xlsx

(2) Updated the group, subgroup, class and depth for (up to) 67 NRM WAIT assets to cooperate the feedback to OWS from relevant SA NRM office (whose staff missed the asset workshop). The AIDs and names of those assets are listed in table LUT_changed_asset_class_20150807 in PED_asset_database_20150807.mdb

(3) Appendix C in PED_asset_database_doc_201500807.doc is about total elements/assets in current Group and subgroup

(4) Four SQL queries (Find_All_Used_Assets, Find_All_WD_Assets, Find_Amount_Asset_in_Class and Find_Amount_Elements_in_Class) in PED_asset_database_20150807.mdb can be used for total assets and total numbers

(5)There are 1 asset (in PED subregion), which is same as 1 asset in MBC subregion. Its AID, Asset Name, Group, SubGroup, Depth, Source and ListDate is using values from MBC asset. This asset is listed in table LUT_DUP_PED_MBC in PED_asset_database_20150807.mdb

(6)The databases, especially spatial database (PED_asset_database_20150807Only.gdb), were changed such as duplicated attribute fields in spatial data were removed and only ID field is kept. The user needs to join the Table Assetlist or Elementlist to the relevant spatial data."

3 27/08/2015 M2_Reason in the Assetlist table and DecisionBrief in the AssetDecisions table have been updated with short descriptions (<255 characters) provided by project team 21/8, and the draft "water-dependent asset register and asset list" (BA-LEB-PED-130-WaterDependentAssetRegister-AssetList-V20150827) also updated accordingly. No changes to asset numbers.

4\t8/03/2016\t"(1) Total number of registered water assets was increased by 2 due to: Two assets changed M2 test from "No" to "Yes" from M2 test review done by Ecologist group.

              (2) The draft new Water Dependent Asset Register file (BA-LEB-PED-130-WaterDependentAssetRegister-AssetList-V20160308.xlsx) was created"

Dataset Citation

Bioregional Assessment Programme (2014) Asset database for the Pedirka subregion on 08 March 2016. Bioregional Assessment Derived Dataset. Viewed 07 February 2017, http://data.bioregionalassessments.gov.au/dataset/336879f0-470f-4f9d-826c-e6c8653657eb.

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 Northern Territory Groundwater Elements v120141202

• Derived From Matters of State environmental significance (version 4.1), Queensland

• Derived From Geofabric Surface Network - V2.1

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

• Derived From South Australia SA - Regional - NRM Board - Water Asset Information Tool - WAIT - databases

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From PED AssetList V1 20150313

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

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

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

• Derived From Queensland wetland data version 3 - wetland areas.

• Derived From Asset database for the Pedirka subregion on 07 August 2015

• Derived From SA Department of Environment, Water and Natural Resources (DEWNR) Water Management Areas 141007

• Derived From South Australian Wetlands - Groundwater Dependent Ecosystems (GDE) Classification

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

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

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

• Derived From Asset database for the Pedirka subregion on 27 August 2015

• Derived From Permanent and Semi-Permanent Waterbodies of the Lake Eyre Basin (Queensland and South Australia) (DRAFT)

• Derived From Queensland wetland data version 3 - wetland lines.

• Derived From SA EconomicElements v1 20141201

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores and NGIS v4 28072014

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

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

• Derived From Northern Territory Groundwater Management Units 20140630

• Derived From Northern Territory - Lake Eyre Basin - Wetlands Mapping - METADATA ONLY

• Derived From SA Department of Environment, Water and Natural Resources (DEWNR) Groundwater Licences 141007

• Derived From Northern Territory Groundwater Licence Extract 20140130

• Derived From Lake Eyre Basin (LEB) Aquatic Ecosystems Mapping and Classification

• Derived From [Australia - Species of National Environmental Significance

This is a collection of layers created by Tian Xie(Intern in DDP) in August, 2018. This collection includes Detroit Parcel Data(Parcel_collector), InfoUSA business data(BIZ_INFOUSA), and building data(Building). The building and business data have been edited by Tian during field research and have attached images.