The Biodiversity Footprint Database contains global consumption-based, monetary, biodiversity impact factors for 44 countries and five rest of the world regions. The dataset has been compiled by combining information from EXIOBASE and LC-IMPACT databases. In addition, the EXIOBASE database has been analyzed with the pymrio analysis tool to determine the geographical location of the consumption-based biodiversity impacts. The mid-point impact factors from EXIOBASE are based on 2019 data, but the regional analysis with pymrio is based on 2011 data. EXIOBASE version 3.8.2 was used and LC-IMPACT version 1.3. The data is currently non peer-reviewed and under submission. The database will be open access after publication. The preprint of the manuscript can be found from: https://doi.org/10.48550/arXiv.2309.14186

About the units

The unit used in the database is the biodiversity equivalent (BDe). The biodversity equivalent, as we call it, is more commonly known as the global potentially disappeared fraction of species (global PDF, Verones et al., 2020). Thus, the monetary biodiversity impact factors are presented in the form BDe/€.

Prices are in basic prices and the conversion factors to transform purchaser prices (e.g. financial accounting prices) to basic prices are provided for Finland (and later for all regions), based on EXIOBASE supply and use tables (SUT).

Content of files

BiodiversityFootprintDatabase.xlsx

The biodiversity impact factors, regional abbreviations and basic price conversion factors for Finland.

BiodiversityFootprintDatabase_DetailedData.zip

The detailed data used to combine EXIOBASE and LC-IMPACT data after the EXIOBASE data was analyzed with the pymrio tool. Contains folders for each driver of biodiversity loss according to the LC-IMPACT classification.

20220406_Exio3stressorcode _2011.py & 20220406_Exio3StressorAggregationCode_2011.py

The pymrio codes that were used to analyze EXIOBASE and the geographical location of the drivers of biodiversity loss (mid-point indicators).

Abstract Four documents describe the specifications, methods and scripts of the Impact and Risk Analysis Databases developed for the Bioregional Assessments Programme. They are: Bioregional Assessment Impact and Risk Databases Installation Advice (IMIA Database Installation Advice v1.docx). Naming Convention of the Bioregional Assessment Impact and Risk Databases (IMIA Project Naming Convention v39.docx). Data treatments for the Bioregional Assessment Impact and Risk Databases (IMIA Project …Show full descriptionAbstract Four documents describe the specifications, methods and scripts of the Impact and Risk Analysis Databases developed for the Bioregional Assessments Programme. They are: Bioregional Assessment Impact and Risk Databases Installation Advice (IMIA Database Installation Advice v1.docx). Naming Convention of the Bioregional Assessment Impact and Risk Databases (IMIA Project Naming Convention v39.docx). Data treatments for the Bioregional Assessment Impact and Risk Databases (IMIA Project Data Treatments v02.docx). Quality Assurance of the Bioregional Assessment Impact and Risk Databases (IMIA Project Quality Assurance Protocol v17.docx). This dataset also includes the Materialised View Information Manager (MatInfoManager.zip). This Microsoft Access database is used to manage the overlay definitions of materialized views of the Impact and Risk Analysis Databases. For more information about this tool, refer to the Data Treatments document. The documentation supports all five Impact and Risk Analysis Databases developed for the assessment areas: Maranoa-Balonne-Condamine: http://data.bioregionalassessments.gov.au/dataset/69075f3e-67ba-405b-8640-96e6cb2a189a Gloucester: http://data.bioregionalassessments.gov.au/dataset/d78c474c-5177-42c2-873c-64c7fe2b178c Hunter: http://data.bioregionalassessments.gov.au/dataset/7c170d60-ff09-4982-bd89-dd3998a88a47 Namoi: http://data.bioregionalassessments.gov.au/dataset/1549c88d-927b-4cb5-b531-1d584d59be58 Galilee: http://data.bioregionalassessments.gov.au/dataset/3dbb5380-2956-4f40-a535-cbdcda129045​ Purpose These documents describe end-to-end treatments of scientific data for the Impact and Risk Analysis Databases, developed and published by the Bioregional Assessment Programme. The applied approach to data quality assurance is also described. These documents are intended for people with an advanced knowledge in geospatial analysis and database administration, who seek to understand, restore or utilise the Analysis Databases and their underlying methods of analysis. Dataset History The Impact and Risk Analysis Database Documentation was created for and by the Information Modelling and Impact Assessment Project (IMIA Project). Dataset Citation Bioregional Assessment Programme (2018) Impact and Risk Analysis Database Documentation. Bioregional Assessment Source Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c.

The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Observed Climate Change Impacts Database contains observed responses to climate change across a wide range of systems as well as regions. These data were taken from the Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008). It consists of responses in the the physical, terrestrial biological systems and marine-ecosystems. The observations that were selected include data that demonstrate a statistically significant trend in change in either direction in systems related to temperature or other climate change variable, and the is for at least 20 years between 1970 and 2004, although study periods may extend earlier or later. For each observation, the data series is described in terms of system, region, longitude and latitude, dates and duration, statistical significance, type of impact, and whether or not land use was identified as a driving factor. System changes are taken from ~80 studies (of which ~75 are new since the IPCC Third Assessment Report) containing more than 29,500 data series. Observations in the database are characterized as a "change consistent with warming" or a "change not consistent with warming", based on information from the underlying studies.

Abstract The Namoi Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component 3-4) products of the …Show full descriptionAbstract The Namoi Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component 3-4) products of the Bioregional Assessment Technical Programme (BATP). The Analysis Database brings together many of the data sets of the scientific disciplines of the Programme and includes modelling results from hydrogeology and hydrology, landscape classes and economic, sociocultural and ecological assets. These data sets are listed in the Data Register for each subregion and can be found on the Bioregional Assessments web site (http://www.bioregionalassessments.gov.au/). An Analysis Database of common design and schema was implemented for each individual subregion where a full Impact and Risk Analysis was completed. To populate each database, input datasets were transformed, normalised and inserted into their respective Analysis Database in accord with the common design and schema. The approach enabled the universal treatment of data analysis across all bioregions despite data being of a different specification and origin. The Analysis Database provided for this subregion is an exact replica of the original used for the assessment of the subregion with the exception that a few spatial data for individual Assets subject to restrictions have been removed before publication. The restrictions are typically for threatened species spatial data but occasionally, restrictive licencing conditions imposed by some custodians prevented publication of some data. The database is constructed using the Open Source platform PostgreSQL coupled with PostGIS. This technology was considered to better enable the provenance and transparency requirements of the Programme. The files provided here have been prepared using the PostgreSQL version 9.5 SQL Dump function - pg_dump. A detailed description of the Analysis Database, its design, structure and application is provided in the supporting documentation: http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c Purpose The Namoi Impact and Risk Analysis Database (Analysis Database) is the geospatial database for completing the Impact and Risk Analysis component of a Bioregional Assessment. This includes the creating of results, tables and maps that appear in the relevant Products of each assessment. The database also manages the data used by the BA Explorer. An individual instance of the Analysis Database was developed for each subregion where a component 3-4 Impact and Risks Assessment was conducted. With the exception of the subregion-specific data contained within it and the removal of restricted data records, each analysis database is of identical design and structure. Dataset History This Analysis Database is an instance of PostgreSQL version 9.5 hosted on Linux Red Hat Enterprise Linux version 4.8.5-4. PostgreSQL geospatial capabilities are provided by POSTGIS version 2.2. Data pre-processing and upload into each PostgreSQL database was completed using FME Desktop (Oracle Edition) version 2016.1.2.1. Analysis data and results are provided to users and systems via the geospatial services of Geoserver version 2.9.1. Scientific analysis and mapping was undertaken by connecting a range of data using a combination of Microsoft Excel, QGIS and ArcMap systems. During the Programme and for its working life, the Analysis Database was hosted and managed on instances of Amazon Web Services managed by Geoscience Australia and the Bureau of Meteorology. Dataset Citation Bioregional Assessment Programme (2018) NAM Impact and Risk Analysis Database v01. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/1549c88d-927b-4cb5-b531-1d584d59be58. Dataset Ancestors Derived From River Styles Spatial Layer for New South Wales Derived From Geofabric Surface Network - V2.1 Derived From Surface Geology of Australia, 1:1 000 000 scale, 2012 edition Derived From HUN SW footprint shapefiles v01 Derived From HUN Groundwater footprint polygons v01 Derived From Namoi Environmental Impact Statements - Mine footprints Derived From Namoi CMA Groundwater Dependent Ecosystems Derived From Landscape classification of the Namoi preliminary assessment extent Derived From Environmental Asset Database - Commonwealth Environmental Water Office Derived From Soil and Landscape Grid National Soil Attribute Maps - Clay 3 resolution - Release 1 Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014 Derived From Interim Biogeographic Regionalisation for Australia (IBRA), Version 7 (Regions) Derived From Key Environmental Assets - KEA - of the Murray Darling Basin Derived From Bioregional Assessment areas v03 Derived From GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013 Derived From BA ALL Assessment Units 1000m 'super set' 20160516_v01 Derived From Mean Annual Climate Data of Australia 1981 to 2012 Derived From Asset list for Namoi - CURRENT Derived From Bioregional Assessment areas v01 Derived From Bioregional Assessment areas v02 Derived From Namoi bore locations, depth to water for June 2012 Derived From Victoria - Seamless Geology 2014 Derived From Murray-Darling Basin Aquatic Ecosystem Classification Derived From HUN SW GW Mine Footprints for IMIA 20170303 v03 Derived From Climate model 0.05x0.05 cells and cell centroids Derived From Namoi hydraulic conductivity measurements Derived From Namoi groundwater uncertainty analysis Derived From Historical Mining footprints DTIRIS HUN 20150707 Derived From Namoi NGIS Bore analysis for 2012 Derived From Australian 0.05º gridded chloride deposition v2 Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From Bioregional Assessment areas v06 Derived From NAM Analysis Boundaries 20160908 v01 Derived From Namoi groundwater drawdown grids Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From BOM, Australian Average Rainfall Data from 1961 to 1990 Derived From Namoi Existing Mine Development Surface Water Footprints Derived From Surface water Preliminary Assessment Extent (PAE) for the Namoi (NAM) subregion - v03 Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012 Derived From National Surface Water sites Hydstra Derived From HUN Mine footprints for timeseries Derived From BA ALL Assessment Units 1000m Reference 20160516_v01 Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted Derived From Natural Resource Management (NRM) Regions 2010 Derived From Australia - Species of National Environmental Significance Database Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release) Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014) Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013 Derived From HUN Groundwater footprint kmz files v01 Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From Asset database for the Namoi subregion on 15 January 2015 Derived From NAM Riverstyles Stream Reaches for Impact and Risk Analysis 20170601 Derived From Gippsland Project boundary Derived From Missing SW Licensing Data in the Namoi PAE 20140711 Derived From Namoi AWRA-L model Derived From Namoi Surface Water ecological HRV quantiles for Impact and Risk Analysis 20170830 Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From Geological Provinces - Full Extent Derived From Namoi Surface Water model nodes for impact and risk analysis 20170515 Derived From NAM ZOPHC Master for impact and risk analysis 20170629 Derived From Namoi GW exceedance probability and drawdown quantile aspatial summary tables Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From Namoi Hydstra surface water time series v1 extracted 140814 Derived From Namoi AWRA-R (restricted input data implementation) Derived From Namoi groundwater model alluvium extent Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From Namoi ZoPHC and component layers 20170629 Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From HUN Historical Landsat Images Mine Foot Prints v01 Derived From NSW Office of Water Groundwater Licence Extract NIC- Oct 2013 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From Groundwater Zone of Impact for the Namoi subregion Derived From Impact and Risk Analysis Database Documentation Derived From Groundwater Preliminary Assessment Extent for the Namoi subregion Derived From Asset database for the Namoi subregion on 18 February 2016 Derived From Border Rivers Gwydir / Namoi Regional Native Vegetation Map Version 2.0. VIS_ID 4204 Derived From NSW Office of Water Surface Water Licences in NIC linked to locations v1 (22 April 2014) Derived From

Academic journals indicators developed from the information contained in the Scopus database (Elsevier B.V.). These indicators can be used to assess and analyze scientific domains.

Researchers seeking to share their data with coordinating centers such as the National Database for Autism Research (NDAR), face numerous barriers to establishing new connections and maintaining existing ones. We sought to dramatically reduce the time and money required to establish and maintain the interoperability of data between research centers, by establishing a process where manual recoding of data is replaced by data sharing instructions in the form of extraction and transformation scripts. Over the course of seven typical (20-60 subjects, 400-1000 fields each) data submissions to NDAR, the need for duplication, retranscription, or restructuring of the source data was fully eliminated. Separating the extraction and transformation scripts from data files also eradicated the impact of additional data collection on the time required to repeat successful transmissions. Revision controlled management of these scripts also provided a new benefit: traceability of the transformation process itself. Now, point-in-time retrieval of extraction scripts and explanations for modifications to the data sharing interface are possible. This method has proven to be successful and efficient for interfacing research data with NDAR. It presents little-to-no impact to transmitting investigators’ data, ensures high data integrity, trivializes the complexities of repeatedly modifying a growing dataset over time, and introduces traceability to the collaborative process of integrating two collections of data with one another.

Abstract The Maranoa-Balonne-Condamine Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component …Show full descriptionAbstract The Maranoa-Balonne-Condamine Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component 3-4) products of the Bioregional Assessment Technical Programme (BATP). The version provided here for public download has been slightly modified to remove restricted material such as the co-ordinates of protected or threatened species. This version was used to populate BA Explorer. The Analysis Database brings together many of the data sets used in Components 1 and 2 of the assessments and includes hydrology and hydrogeology modelling results, landscape classes and economic, sociocultural and ecological assets. These data sets are listed in the Component 1 and 2 products under the Assessments tab in http://www.bioregionalassessments.gov.au/. An Analysis Database of common design and schema was implemented for each subregion where a full Impact and Risk Analysis was completed. To populate each database, input datasets were transformed, normalised and inserted into their respective Analysis Databases in accord with the common design and schema. The approach enabled the universal treatment of data analysis across all bioregions despite data being of different specifications and origins. The Analysis Database includes all the data used for the assessment of the subregion with the exception of those datasets that were not provided to the program with an open access licence. The database is constructed using the Open Source platform PostgreSQL coupled with PostGIS. This technology was considered to better enable the provenance and transparency requirements of the Programme. The files provided here have been prepared using the PostgreSQL version 9.5 SQL Dump function - pg_dump. A detailed description of the Analysis Database, its design, structure and application is provided in the supporting documentation: http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c Purpose The Maranoa-Balonne-Condamine Impact and Risk Analysis Database (Analysis Database) is the geospatial database for completing the Impact and Risk Analysis component of the Maranoa-Balonne-Condamine Bioregional Assessment. This includes the creating of results, tables and maps that appear in the relevant Products of each assessment. The database also manages the data used by the BA Explorer. An individual instance of the Analysis Database was developed for each subregion where a component 3-4 Impact and Risks Assessment was conducted. With the exception of the subregion-specific data contained within it and the removal of restricted data records, each analysis database is of identical design and structure. Dataset History This Analysis Database is an instance of PostgreSQL version 9.5 hosted on Linux Red Hat Enterprise Linux version 4.8.5-4. PostgreSQL geospatial capabilities are provided by POSTGIS version 2.2. Data pre-processing and upload into each PostgreSQL database was completed using FME Desktop (Oracle Edition) version 2016.1.2.1. Analysis data and results are provided to users and systems via the geospatial services of Geoserver version 2.9.1. Scientific analysis and mapping was undertaken by connecting a range of data using a combination of Microsoft Excel, QGIS and ArcMap systems. During the Programme and for its working life, the Analysis Database was hosted and managed on instances of Amazon Web Services managed by Geoscience Australia and the Bureau of Meteorology. Dataset Citation Bioregional Assessment Programme (2017) MBC Impact and Risk Analysis Database v01. Bioregional Assessment Derived Dataset. Viewed 25 October 2017, http://data.bioregionalassessments.gov.au/dataset/69075f3e-67ba-405b-8640-96e6cb2a189a. Dataset Ancestors Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204 Derived From Surface Geology of Australia, 1:1 000 000 scale, 2012 edition Derived From Asset database for the Maranoa-Balonne-Condamine subregion on 16 June 2015 Derived From South East Queensland GDE (draft) Derived From Geofabric Surface Cartography - V2.1 Derived From Environmental Asset Database - Commonwealth Environmental Water Office Derived From QLD Dept of Natural Resources and Mines, Surface Water Entitlements 131204 Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From Catchment Scale Land Use of Australia - 2014 Derived From Surface water preliminary assessment extent for the Maranoa-Balonne-Condamine subregion - v02 Derived From MBC Groundwater model domain boundary Derived From Key Environmental Assets - KEA - of the Murray Darling Basin Derived From Bioregional Assessment areas v03 Derived From MBC Groundwater model ACRD 5th to 95th percentile drawdown Derived From Permanent and Semi-Permanent Waterbodies of the Lake Eyre Basin (Queensland and South Australia) (DRAFT) Derived From Receptors for the Maranoa-Balonne-Condamine subregion Derived From Bioregional Assessment areas v01 Derived From Bioregional Assessment areas v02 Derived From MBC Assessment Units 20160714 v01 Derived From Victoria - Seamless Geology 2014 Derived From Matters of State environmental significance (version 4.1), Queensland Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From Bioregional Assessment areas v06 Derived From Asset database for the Maranoa-Balonne-Condamine subregion on 9 June 2015 Derived From Queensland wetland data version 3 - wetland areas. Derived From Groundwater Preliminary Assessment Extent (PAE) for the Maranoa Balonne Condamine (MBC) subregion - v02 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From Asset database for the Maranoa-Balonne-Condamine subregion on 05 February 2016 Derived From MBC Groundwater model layer boundaries Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Baseline drawdown Layer 1 - Condamine Alluvium Derived From MBC Assessment unit codified by regional watertable Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores and NGIS v4 28072014 Derived From MBC Assessment Units 20160714 v02 Derived From MBC Groundwater model water balance areas Derived From Asset database for the Maranoa-Balonne-Condamine subregion on 25 February 2015 Derived From Australia - Species of National Environmental Significance Database Derived From MBC Groundwater model uncertainty analysis Derived From Spring vents assessed for the Surat Underground Water Impact Report 2012 Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release) Derived From Queensland QLD - Regional - NRM - Water Asset Information Tool - WAIT - databases Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014) Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013 Derived From MBC Assessment Units 20161211 v01 Derived From Asset database for the Maranoa-Balonne-Condamine subregion on 26 June 2015 Derived From Geofabric Surface Catchments - V2.1 Derived From National Groundwater Information System (NGIS) v1.1 Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From MBC ZoPHC and component layers 20170117 Derived From Queensland groundwater dependent ecosystems Derived From Gippsland Project boundary Derived From Queensland Water Commission, Underground Water Impact Report for the Surat Cumulative Management Area, 2012 - Report and Data Derived From Natural Resource Management (NRM) Regions 2010 Derived From Great Artesian Basin - Hydrogeology and Extent Boundary Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From Geological Provinces - Full Extent Derived From MBC Analysis Boundaries 20160718 v01 Derived From MBC Groundwater model mine footprints Derived From MBC Groundwater model uncertainty plots Derived From Groundwater Preliminary Assessment Extent (PAE) for the Maranoa Balonne Condamine (MBC) subregion - v01 Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From QLD DNRM Licence Locations Linked to Cadastre Plan - v1 - 20140307 Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From Australia World Heritage Areas Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From Landscape classification of the Maranoa-Balonne-Condamine preliminary assessment extent v02 Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From NSW Office of Water Groundwater Licence Extract NIC- Oct 2013 Derived From Landscape classification of the Maranoa-Balonne-Condamine preliminary assessment extent v03 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From MBC Coal mine extents Derived From MBC Groundwater model baseline 5th to 95th percentile drawdown Derived From Impact and Risk Analysis Database Documentation Derived From Preliminary Assessment Extent (PAE) for the Maranoa-Balonne-Condamine subregion - v03 Derived From NSW Office of Water Surface Water Licences in NIC linked to locations v1 (22 April 2014) Derived From Bioregional Assessment areas v04 Derived From Queensland

There is great concern on the effects of non-native species impacts on biodiversity and ecosystem services. We constructed a comprehensive database of the scientific papers on field studies reporting environmental impacts of invasive plant species in Europe. We searched for relevant articles on the Web of Science database until the end of 2022 with no restriction on publication year. Our final dataset included 266 publications on 897 impacts of 104 invasive plant species on native species, communities and ecosystem properties in 29 European countries. This database contributes to the IPBES Global Invasive Alien Species Assessment.

This dataset contains responses by government officials from India, Tanzania and Peru who were interviewed during June-August 2017. They filled out a short survey and were introduced to the Impact Evidence website.

Abstract

The Galilee Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component 3-4) products of the Bioregional Assessment Technical Programme (BATP). The Analysis Database brings together many of the data sets of the scientific disciplines of the Programme and includes modelling results from hydrogeology and hydrology, landscape classes and economic, sociocultural and ecological assets. These data sets are listed in the Data Register for each subregion and can be found on the Bioregional Assessments web site (http://www.bioregionalassessments.gov.au/).

An Analysis Database of common design and schema was implemented for each individual subregion where a full Impact and Risk Analysis was completed. To populate each database, input datasets were transformed, normalised and inserted into their respective Analysis Database in accord with the common design and schema. The approach enabled the universal treatment of data analysis across all bioregions despite data being of a different specification and origin.

The Analysis Database provided for this subregion is an exact replica of the original used for the assessment of the subregion with the exception that a few spatial data for individual Assets subject to restrictions have been removed before publication. The restrictions are typically for threatened species spatial data but occasionally, restrictive licencing conditions imposed by some custodians prevented publication of some data. The database is constructed using the Open Source platform PostgreSQL coupled with PostGIS. This technology was considered to better enable the provenance and transparency requirements of the Programme. The files provided here have been prepared using the PostgreSQL version 9.5 SQL Dump function - pg_dump.

A detailed description of the Analysis Database, its design, structure and application is provided in the supporting documentation: http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c

Purpose

The Galilee Impact and Risk Analysis Database (Analysis Database) is the geospatial database for completing the Impact and Risk Analysis component of a Bioregional Assessment. This includes the creating of results, tables and maps that appear in the relevant Products of each assessment. The database also manages the data used by the BA Explorer.

An individual instance of the Analysis Database was developed for each subregion where a component 3-4 Impact and Risks Assessment was conducted. With the exception of the subregion-specific data contained within it and the removal of restricted data records, each analysis database is of identical design and structure.

Dataset History

This Analysis Database is an instance of PostgreSQL version 9.5 hosted on Linux Red Hat Enterprise Linux version 4.8.5-4. PostgreSQL geospatial capabilities are provided by POSTGIS version 2.2.

Data pre-processing and upload into each PostgreSQL database was completed using FME Desktop (Oracle Edition) version 2016.1.2.1. Analysis data and results are provided to users and systems via the geospatial services of Geoserver version 2.9.1. Scientific analysis and mapping was undertaken by connecting a range of data using a combination of Microsoft Excel, QGIS and ArcMap systems.

During the Programme and for its working life, the Analysis Database was hosted and managed on instances of Amazon Web Services managed by Geoscience Australia and the Bureau of Meteorology.

Dataset Citation

Bioregional Assessment Programme (2018) GAL Impact and Risk Analysis Database v01. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/3dbb5380-2956-4f40-a535-cbdcda129045.

Dataset Ancestors

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

• Derived From Galilee Drawdown Rasters

• Derived From Galilee Groundwater Model, Hydrogeological Formation Extents v01

• Derived From GAL SW Quantiles Interpolation for IMIA Database

• Derived From SA Petroleum Production License Applications

• Derived From Galilee tributary catchments

• Derived From Springs of the Galilee subregion - Points Geometry

• Derived From GAL Aquifer Formation Extents v01

• Derived From Geofabric Surface Cartography - V2.1

• Derived From SA Mineral and/or Opal Exploration Licenses

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale

• Derived From GAL Assessment Units 1000m 20160522 v01

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From Phanerozoic OZ SEEBASE v2 GIS

• Derived From Asset database for the Galilee subregion on 2 December 2014

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

• Derived From Bioregional Assessment areas v03

• Derived From SA Petroleum Exploration Licences/Permits

• Derived From South Australia Mineral Leases Production, 6 March 2013

• Derived From BA ALL Assessment Units 1000m 'super set' 20160516_v01

• Derived From Kevin's Corner Project Environmental Impact Statement

• Derived From Galilee Hydrological Response Variable (HRV) model

• Derived From Asset list for Galilee - 20140605

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From QLD Current Exploration Permits for Minerals (EPM) in Queensland 6/3/2013

• Derived From Victoria - Seamless Geology 2014

• Derived From Galilee groundwater numerical modelling AEM models

• Derived From GAL Surface Water Reaches for Risk and Impact Analysis 20180803

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

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

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From GAL Aquifer Formation Extents v02

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

• Derived From Galilee surface water modelling nodes

• Derived From GAL Eco HRV SW Quantiles Interpolation for IMIA Database

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

• Derived From China Stone Coal Project initial advice statement

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From South Australia Mineral Production Claims, 6 March 2013

• Derived From Onsite and offsite mine infrastructure for the Carmichael Coal Mine and Rail Project, Adani Mining Pty Ltd 2012

*

Microarray study attributes and data sharing status397 rows, one row for each study that created gene expression microarray data as identified by Ochsner et al. (doi:10.1038/nmeth1208-991). Attributes of each study are included in 23 columns. Dependent variable is called is_data_shared.Piwowar_Metrics2009_rawdata.csvStatistical analysis R scriptStatistical R script for analysis and graphics as presented in the paper.Piwowar_Metrics2009_statistics.R

Abstract

The Hunter Impact and Risk Analysis Database (Analysis Database) is a fit-for-purpose geospatial information system developed for the Impact and Risk Analysis (Component 3-4) products of the Bioregional Assessment Technical Programme (BATP). The Analysis Database brings together many of the data sets of the scientific disciplines of the Programme and includes modelling results from hydrogeology and hydrology, landscape classes and economic, sociocultural and ecological assets. These data sets are listed in the Data Register for each subregion and can be found on the Bioregional Assessments web site (http://www.bioregionalassessments.gov.au/).

An Analysis Database of common design and schema was implemented for each individual subregion where a full Impact and Risk Analysis was completed. To populate each database, input datasets were transformed, normalised and inserted into their respective Analysis Database in accord with the common design and schema. The approach enabled the universal treatment of data analysis across all bioregions despite data being of a different specification and origin.

The Analysis Database provided for this subregion is an exact replica of the original used for the assessment of the subregion with the exception that a few spatial data for individual Assets subject to restrictions have been removed before publication. The restrictions are typically for threatened species spatial data but occasionally, restrictive licencing conditions imposed by some custodians prevented publication of some data. The database is constructed using the Open Source platform PostgreSQL coupled with PostGIS. This technology was considered to better enable the provenance and transparency requirements of the Programme. The files provided here have been prepared using the PostgreSQL version 9.5 SQL Dump function - pg_dump.

A detailed description of the Analysis Database, its design, structure and application is provided in the supporting documentation: http://data.bioregionalassessments.gov.au/dataset/05e851cf-57a5-4127-948a-1b41732d538c

Purpose

The Hunter Impact and Risk Analysis Database (Analysis Database) is the geospatial database for completing the Impact and Risk Analysis component of a Bioregional Assessment. This includes the creating of results, tables and maps that appear in the relevant Products of each assessment. The database also manages the data used by the BA Explorer.

An individual instance of the Analysis Database was developed for each subregion where a component 3-4 Impact and Risks Assessment was conducted. With the exception of the subregion-specific data contained within it and the removal of restricted data records, each analysis database is of identical design and structure.

Dataset History

This Analysis Database is an instance of PostgreSQL version 9.5 hosted on Linux Red Hat Enterprise Linux version 4.8.5-4. PostgreSQL geospatial capabilities are provided by POSTGIS version 2.2.

Data pre-processing and upload into each PostgreSQL database was completed using FME Desktop (Oracle Edition) version 2016.1.2.1. Analysis data and results are provided to users and systems via the geospatial services of Geoserver version 2.9.1. Scientific analysis and mapping was undertaken by connecting a range of data using a combination of Microsoft Excel, QGIS and ArcMap systems.

During the Programme and for its working life, the Analysis Database was hosted and managed on instances of Amazon Web Services managed by Geoscience Australia and the Bureau of Meteorology.

Dataset Citation

Bioregional Assessment Programme (2018) HUN Impact and Risk Analysis Database v01. Bioregional Assessment Derived Dataset. Viewed 28 August 2018, http://data.bioregionalassessments.gov.au/dataset/298e1f89-515c-4389-9e5d-444a5053cc19.

Dataset Ancestors

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

• Derived From HUN ZoPHC and component layers 20171115

• Derived From NSW Office of Water - National Groundwater Information System 20140701

• Derived From Greater Hunter Native Vegetation Mapping with Classification for Mapping

• Derived From NSW Wetlands

• Derived From Geofabric Surface Network - V2.1

• Derived From HUN AWRA-R simulation nodes v01

• Derived From Hunter AWRA Hydrological Response Variables (HRV)

• Derived From Surface Geology of Australia, 1:1 000 000 scale, 2012 edition

• Derived From HUN SW footprint shapefiles v01

• Derived From HUN Groundwater footprint polygons v01

• Derived From Asset database for the Hunter subregion on 24 February 2016

• Derived From BA All Regions BILO cells in subregions shapefile

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

• Derived From HUN AWRA-R River Reaches Simulation v01

• Derived From HUN AWRA-L simulation nodes v02

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014

• Derived From Hunter subregion boundary

• Derived From Interim Biogeographic Regionalisation for Australia (IBRA), Version 7 (Regions)

• Derived From Atlas of Living Australia NSW ALA Portal 20140613

• Derived From Bioregional Assessment areas v03

• Derived From HUN AWRA-R calibration catchments v01

• Derived From HUN AWRA-R Observed storage volumes Glenbawn Dam and Glennies Creek Dam

• Derived From Selected streamflow gauges within and near the Hunter subregion

• Derived From Groundwater Entitlement Hunter NSW Office of Water 20150324

• Derived From Asset database for the Hunter subregion on 20 July 2015

• Derived From BA ALL Assessment Units 1000m 'super set' 20160516_v01

• Derived From HUN Alluvium (1:1m Geology)

• Derived From HUN River Perenniality v01

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From Hunter bioregion (IBRA Version 7)

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

• Derived From HUN Riverine Landscape Classes subject to hydrological change

• Derived From Asset database for the Hunter subregion on 22 September 2015

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From HUN bores v01

• Derived From NSW Office of Water Groundwater Licence Extract, North and South Sydney - Oct 2013

• Derived From HUN SW GW Mine Footprints for IMIA 20170303 v03

• Derived From Climate model 0.05x0.05 cells and cell centroids

• Derived From HUN AWRA-LR Model v01

• Derived From HUN Landscape Classification v02

• Derived From [Historical

Access database containing biological and environmental data collected by the Australian Antarctic Division, Human Impacts Benthic Biodiversity group.

Frequencies and percentages of included systematic reviews per journal type and journal impact factors.

Utilize 700+ impact metrics to assess the value chain impact of business activities, analyze impact using ESG data from corporate sustainability disclosures, and conduct impact assessments on over 200k+ global controversies.

Our metrics provide sustainability data on important corporate ESG issues using:

• Modelled data for an assessment of business's value chain impact on emissions, land and water use, resource use, pollution, consumers health and safety, and broader community impacts.

• Scores that reflect the comprehensiveness of corporate sustainability policies, targets and practices. This assessment is conducted using corporate disclosures and analyzes ESG impact across key sustainability themes such as biodiversity, corporate governance, supply chain labour practices, human rights and more.

Each score has underlying features utilized for its assessment which help differentiate impact. For example, our scoring of a company's GHG emissions reduction target is enhanced by the scope of the target (targets which aim to reduce Scope 1, 2, and 3 emissions that are SBTi verified will be rated better than targets that aim to reduce only one of the scopes). All underlying features and data snippets from corporate disclosures are available as detailed reports.

• Impact analysis of ESG controversies from over 200k global sources which helps screen through the noise of published news and highlights material ESG controversies. The scoring of each controversy from low to high impact is done by ESG experts using features such as scale of impact, duration of impact, stakeholders affected, etc.

All our sustainability data is available alongside detailed reports which provides full transparency into the corporate disclosures, news data and analysis used for each score, along with source snippets. This helps simplify the ESG research that feeds into your own ESG models.

All our sustainability data leads to ESG impact scores from A+ to D- with a transparent methodology and sector-specific weights to simplify investment analysis. Integrate our vast ESG database and research into your own sustainable investment models and utilise our scores for an indicative assessment of sustainability performance.

This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper:

Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. https://doi.org/10.5194/esd-10-741-2019

Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de).

Climate change impact data

File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv

Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries.

File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv

Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).

File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv

Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).

In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, https://tntcat.iiasa.ac.at/SspDb/, last accessed 15Nov 2019).

Climate change mitigation cost data

The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2].

File 4: REMIND_scenario_results_economic_data.csv

File 5: REMIND_scenarios_climate_data.csv

Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature.

In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios.

The first dimension specifies the climate policy regime (delayed action, baseline scenarios):

1xx: climate action from 2010
5xx: climate action from 2015
2xx climate action from 2020 (used in this study)
3xx climate action from 2030
4x1 weak policy baseline (before Paris agreement)

The second dimension specifies the technology portfolio and assumptions:

x1x Full technology portfolio (used in this study)
x2x noCCS: unavailability of CCS
x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed
x4x NucPO: phase out of investments into nuclear energy
x5x Limited SW: penetration of solar and wind power limited
x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases)
x6x noBECCS: unavailability of CCS in combination with bioenergy

The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.).

xx1 0$/tCO2 (baseline)
xx2 10$/tCO2
xx3 30$/tCO2
xx4 50$/tCO2
xx5 100$/tCO2
xx6 200$/tCO2
xx7 500$/tCO2
xx8 40$/tCO2
xx9 20$/tCO2
xx0 5$/tCO2

For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price).

[1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a.

[2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.

Informatics resource citation counts (2000)The number of 'awareness', 'usage' and total citations for informatics resource publications up to the year 2000.citation_counts_2000.csvInformatics resource citation counts (2001)The number of 'awareness', 'usage' and total citations for informatics resource publications up to the year 2001.citation_counts_2001.csvInformatics resource citation counts (2002)The number of 'awareness', 'usage' and total citations for informatics resource publications up to the year 2002.citation_counts_2002.csvInformatics resource citation counts (2003)The number of 'awareness', 'usage' and total citations for informatics resource publications up to the year 2003.citation_counts_2003.csvInformatics resource citation counts (2004)The number of 'awareness', 'usage' and total citations for informatics resource publications up to the year 2004.citation_counts_2004.csvInformatics resource citation counts (2005)The number of 'awareness', 'usage' and total citations for...

A survey conducted in April and May 2023 revealed that around 35 percent of organizations in the United States and 40 percent of organizations in the United Kingdom pay higher costs for international data transfers due to data privacy regulations, but they also find it manageable. Furthermore, approximately 35 percent of respondents from both countries think the regulations encourage businesses by guaranteeing that the data will be safeguarded in other countries.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Aggregate Effects of Budget Stimulus: Evidence from the Large Fiscal Expansions Database. PIIE Working Paper 19-12.

If you use the data, please cite as: Cohen-Setton, Jeremie, Egor Gornostay, and Colombe Ladreit de Lacharrière (2019). Aggregate Effects of Budget Stimulus: Evidence from the Large Fiscal Expansions Database. PIIE Working Paper 19-12. Peterson Institute for International Economics.

Contains bioassay records and data for chemicals analyzed and evaluated for repellency, toxicity, reproductive inhibition, and immobilization.