The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

We present all AMONs for GDB and Zinc data-bases using no more than 7 non-hydrogen atoms (AGZ7)---a calculated organic chemistry building-block dictionary based on the AMON approach [Huang and von Lilienfeld, Nature Chemistry (2020)]. AGZ7 records Cartesian coordinates of compositional and constitutional isomers, as well as properties for ∼140k small organic molecules obtained by systematically fragmenting all molecules of Zinc and the majority of GDB17 into smaller entities, saturating with hydrogens, and containing no more than 7 heavy atoms (excluding hydrogen atoms). AGZ7 cover the elements H, B, C, N, O, F, Si, P, S, Cl, Br, Sn and I and includes optimized geometries, total energy and its decomposition, Mulliken atomic charges, dipole moment vectors, quadrupole tensors, electronic spatial extent, eigenvalues of all occupied orbitals, LUMO, gap, isotropic polarizability, harmonic frequencies, reduced masses, force constants, IR intensity, normal coordinates, rotational constants, zero-point energy, internal energy, enthalpy, entropy, free energy, and heat capacity (all at ambient conditions) using B3LYP/cc-pVTZ (pseudopotentials were used for Sn and I) level of theory. We exemplify the usefulness of this data set with AMON based machine learning models of total potential energy predictions of seven of the most rigid GDB-17 molecules.

Use of absolute and derived values in assessing Population health and the activities of healthcare

Submitted by Riya Patil & Rutuja Sonar, to Moldoev Murzali Ilyazovich Osh state University

ABSTRACT

In contrast, derived values involve the use of statistical techniques to calculate indirect indicators from absolute values. These include metrics like disability-adjusted life years (DALYs), quality-adjusted life years (QALYs), and health-adjusted life expectancy (HALE). Derived values are instrumental in understanding the broader context of population health, as they often combine both mortality and morbidity data to reflect the overall burden of disease.

In healthcare institutions, these values are integral in guiding resource allocation, evaluating the effectiveness of interventions, and shaping policies aimed at improving health outcomes. While absolute values provide essential raw data, derived values offer nuanced insights into the quality and long-term impact of healthcare services. Together, they form a comprehensive approach to measuring and improving population health, helping healthcare institutions prioritize actions and allocate resources more effectively.

This paper explores the role of absolute and derived values in assessing population health and their relevance to healthcare institutions, examining how both types of values support decision-making and influence health policy.

Keywords: Population health, absolute values, derived values, healthcare institutions, mortality rates, morbidity, Disability-Adjusted Life Years (DALYs), Quality-Adjusted Life Years (QALYs), Health-Adjusted Life Expectancy (HALE), health policy, healthcare interventions.

INTRODUCTION

Use of Absolute and Derived Values in Assessing Population Health and the Activities of Healthcare Institutions**

Population health is a key focus of public health systems and healthcare institutions worldwide. Assessing the health of a population requires robust metrics to understand the current state of health, identify risks, and track trends over time. One of the essential tools in evaluating population health is the use of **absolute values** and **derived values**. These metrics offer complementary insights into both the health status of individuals within a population and the effectiveness of healthcare interventions.

**Absolute values** are straightforward measures that provide direct data points, such as the total number of people suffering from a specific disease, the number of hospital admissions, or the total expenditure on healthcare services. These values are critical for understanding the scale of health issues and resource needs within a community.

**Derived values**, on the other hand, are ratios or indices calculated from absolute values. They allow for more meaningful comparisons across populations, time periods, or geographical areas. Examples include rates such as morbidity or mortality rates, life expectancy, and disease prevalence, which are essential for assessing public health outcomes and guiding healthcare policy and decision-making.

By integrating both absolute and derived values, healthcare institutions can gain a comprehensive picture of population health, identify areas for improvement, allocate resources more efficiently, and track the effectiveness of healthcare initiatives. This approach helps ensure that healthcare systems are responsive to the needs of the population and can adapt to emerging health challenges.

METHODOLOGY

Method and analysis which is performed by the google worksheet and google forms

Absolute Values in Assessing Population Health:

Absolute values refer to raw, unadjusted data points that provide a direct measure of a population's health status. These values are fundamental for initial assessments, as they provide baseline data for various health indicators.

Definition and Examples

Absolute values refer to concrete figures that represent the total counts or occurrences of specific health events or conditions. For example:

Total Mortality Rate: The number of deaths in a population over a specific time period (e.g., deaths per 100,000 people).

Prevalence Rates: The proportion of individuals in a population diagnosed with a specific condition at a particular time (e.g., diabetes prevalence).

Incidence Rates: The number of new or newly diagnosed cases of a disease over a given period (e.g., cancer incidence).

Life Expectancy: The average number of years a person is expected to live based on current mortality rates.

Use in Population Health

Health Monitoring: Absolute values allow public health authorities to monitor trends in population health, such as increases in mortality or the spread of disease.

Resource Allocation: These values help in determining the burden of disease in different populations, aiding in the efficient distribution of healthcare resources.

Derived Values in Assessing Population Health

Derived values involve the use of mathematical formulas or statistical techniques to adjust or combine absolute values to create composite indices or ratios that provide deeper insights into health outcomes and healthcare activities.

Definition

Derived values are statistical measures that offer context to absolute

by relating them to population characteristics. Common examples include:

Age-Standardized Mortality Rate: Adjusts the mortality rate for differences in the age structure of different populations, allowing comparisons between populations with different age distributions.

Disability-Adjusted Life Years (DALY): A composite measure that combines years of life lost due to premature death and years lived with disability. DALY provides a more comprehensive understanding of the burden of disease.

Quality-Adjusted Life Years (QALY): A measure used to evaluate the effectiveness of healthcare interventions by combining quantity and quality of life.

Health Inequality Index: Derived by comparing health disparities between different subgroups within a population.

Use in Population Health

Risk Assessment: Derived values like DALYs or QALYs enable healthcare providers and policymakers to assess the relative impact of different diseases or health conditions on the population’s overall health.

Health Outcomes Comparison: Derived values facilitate comparisons across different populations or regions, adjusting for factors like age, gender, or socioeconomic status.

Policy and Program Evaluation: Derived values are used to evaluate the effectiveness of public health interventions or healthcare programs, such as whether a vaccination program reduces disease burden over time.

Significance

Contextualizing Health Trends: Absolute values alone may not offer a clear picture. For instance, while an increase in the number of cancer cases might be alarming, derived values like the cancer incidence rate allow us to understand if the increase is due to an actual rise in cases or simply a result of population growth.

Comparative Analysis: Derived values are essential when comparing different populations or regions. For example, comparing the infant mortality rate in different countries provides insights into healthcare system performance, whereas absolute numbers may mislead without considering population size differences.

Evaluating Healthcare Efficiency: Derived values such as cost-effectiveness or patient outcomes per healthcare dollar provide insights into the efficiency of healthcare institutions. This helps identify areas of improvement in resource allocation and delivery of services.

Policy and Planning: Derived values play a crucial role in informing public health policies and healthcare strategies. For example, the quality-adjusted life year (QALY), derived from health outcome measures, is commonly used in health economics to assess the effectiveness of medical treatments and interventions.

Conclusion

Both absolute and derived values are integral to assessing population health and healthcare institution activities. Absolute values provide raw data, while derived values allow for deeper analysis, trends, and comparisons, giving a more comprehensive picture of health outcomes and healthcare performance.

REFERENCE

1.Kindig D, Stoddart G (March 2003). "What is population health?". American Journal of Public Health. 93 (3): 380–3. doi:10.2105/ajph.93.3.380. PMC 1447747. PMID 12604476.

2. McGinnis JM, Williams-Russo P, Knickman JR (2002). "The case for more active policy attention to health promotion". Health Aff (Millwood). 21 (2): 78–93. doi:10.1377/hlthaff.21.2.78. PMID 11900188.. See also National Academies Press free publication: The Future of Public Health in the 21st Century.

3. World Health Organization. 2006. Constitution of the World Health Organization – Basic Documents, Forty-fifth edition, Supplement, October 2006.

4. Jeffery RW. 2001. Public health strategies for obesity treatment and prevention. American Journal of Health Behavior 25:252–259.

5. Buunk BP, Verhoeven K. 1991. Companionship and support at work: a microanalysis of the stress-reducing features of social interactions. Basic and Applied Social Psychology 12:243–258.

6. CDC. 2001. a. CDC FactBook 2000/2001: Profile of the Nation's Health. Atlanta, GA: CDC.

7. What is the WHO definition of health? from the Preamble to the Constitution of WHO as adopted by the

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

Overview

Purpose and Benefits A polygon feature service intended to serve as a repository to store daily wildfire perimeters for fires occurring within National Park Service parks. This service can be used to generate fire progressions.

Layer

Daily Wildfire Perimeter Attributes and their definitions can be found below.

Attributes:

        Fire Occurrence ID
        The Fire Occurence ID field is a unique identifier linking the daily wildfire perimeter to the wildland fire location feature class.


        Perimeter Date
        The Perimeter Date field is intended for users to capture the date the perimeter was collected.


        Feature Category
        The Feature Category field is intended for users to identify the type of event that occurred..


        GIS Acres
        The GIS Acres field is intended for users to capture the acres for the fire history or fuel treatment perimeter using GIS to calculate the acres.


        Public Display
        The Public Display field is intended for users to determine if the data can be used for public display – i.e any data representing sensitive information such as cultural resources should not be displayed on a public map.


        Data Access
        The Data Access field is intended for users to capture the accessibility of the data – i.e. most fire data is considered unrestricted, however, if cultural resources are included then the data would be restricted from sharing or use with others.


        Unit Code
        The UnitCode field is intended to allow users to identify the National Park that a particular resource may lie within. Some data collected and maintained by National Park Service may inventory resources outside NPS property or responsibility. To make data entry easier, the UnitCode field may select park unit names from a domain that contains all of the park unit 4-letter acronyms. All park units, associated monuments, memorials, seashores, etc., are represented in the domain values.



        Map Method 
        The Map Method field is intended for users to define how the geospatial feature was derived.


        Data Source 
        The Data Source field is intended for users to define the source of the data.


        Date of Source 
        The Source Date field is intended for users to define the date of the source data.


        XY Accuracy 
        The XY Accuracy field is intended to allow users to document the accuracy of the data.


        Notes 
        The Notes field is intended for users to add any additional information describing the feature.

This document provides the definition of terms used in the derived physics data files for the GEISHA project. For definitions of terms of the provided data for GEISHA, see the file "GEISHA Data Vocabulary".

This is a synthetic smart card data set that can be used to test pattern detection methods for the extraction of temporal and spatial data. The data set is tab seperated and based on a stylized travel pattern description for city of Utrecht in The Netherlands and is developed and used in Chapter 6 of the PhD Thesis of Paul Bouman.

This dataset contains the following files:

• journeys.tsv : the actual data set of synthetic smart card data
• utrecht.xml : the activity pattern definition that was used to randomly generate the synthethic smart card data
• validate.ref : a file derived from the activity pattern definition that can be used for validation purposes. It specifies which activity types occur at each location in the smart card data set.

Abstract

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

The Preliminary Assessment Extent (PAE) is a spatial layer that defines the land surface area contained within a bioregion over which coal resource development may have potential impact on water-dependent assets and receptors associated with those assets (Barrett et al 2013).

Purpose

The role of the PAE is to optimise research agency effort by focussing on those locations where a material causal link may occur between coal resource development and impacts on water dependent assets. The lists of assets collated by the Program are filtered for "proximity" such that only those assets that intersect with the PAE are considered further in the assessment process. Changes to the PAE such as through the identification of a different development pathway or an improved hydrological understanding may require the proximity of assets to be considered again. Should the assessment process identify a material connection between a water dependent asset outside the PAE and coal resource development impacts, the PAE would need to be amended.

Dataset History

The PAE is derived from the intersection of surface hydrology features; groundwater management units; mining development leases and/or CSG tenements; and, directional flows of surface and groundwater.

The following 5 inputs were used by the Specialists to define the Preliminary Assessment Extent:

1. Bioregion boundary

2. Geology and the coal resource

3. Surface water hydrology

4. Groundwater hydrology

5. Flow paths (Known available information on gradients of pressure, water table height, stream direction, surface-ground water interactions and any other available data)

Dataset Citation

Bioregional Assessment Programme (2014) CLM Preliminary Assessment Extent Definition & Report( CLM PAE). Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/2cdd0e81-026e-4a41-87b0-ec003eddc5c1.

Dataset Ancestors

• Derived From Bioregional Assessment areas v02

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From QLD Petroleum Leases, 28/11/2013

• Derived From Bioregional Assessment areas v01

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

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

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

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 dataset contains 10,000 replicates of AWRA model pre-processing outputs (streamflow Qtot and baseflow Qb), used for calculating additional coal resources development impacts on hydrological response variables in 30 simulation nodes (Zhang et al., 2016).

References

Zhang Y Q, Viney N R, Peeters L J M, Wang B, Yang A, Li L T, McVicar T R, Marvanek S P, Rachakonda P K, Shi X G, Pagendam D E and Singh R M (2016) Surface water numerical modelling for the Gloucester subregion. Product 2.6.1 for the Gloucester subregion from the Northern Sydney Basin Bioregional Assessment. Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia., Department of the Environment, Bureau of Meteorology, CSIRO and Geoscience Australia, Australia., http://data.bioregionalassessments.gov.au/product/NSB/GLO/2.6.1.

Purpose

This pre-processing data is used for estimating AWRA post-processing streamflow outputs under CRDP and baseline conditions, respectively.

Dataset History

The dataset has all files and scripts necessary to execute the 10,000 runs on the linux platform of the CSIRO High Performance Cluster computers.

The AWRA-L model version 4.5 has been used for all BA surface water simulations. The application is developed with the C\# language. All execution and class (dll) files can be found at \\OSM-07-CDC.it.csiro.au\OSM_CBR_LW_BA_working\Disciplines\SurfaceWater\Modelling\AWRA-LG\Bin. The executable file "BACalibrationAndSimulationApp.exe" generates global definition files which define the input and output data and input time series locations. The executable file "SimulateModel.exe" runs simulations based on the global definition files and outputs required variables (Qtot, Qb, Dd) in NetCDF format. All simulation runs have implemented on local Windows 7 work stations.

The AWRA preprocessing data are the inputs for estimating AWRA post-processing model outputs (GUID: http://data.bioregionalassessments.gov.au/dataset/15ca8f9d-84b4-4395-87db-ab4ff15b9f07).

The dataset was uploaded to

\\lw-osm-01-cdc.it.csiro.au\OSM_CBR_LW_BAModelRuns_app\GLO\AWRA_ScalingChange_rerun on 03 September 2016

This dataset were further used to compute daily streamflow post-processing outputs under CRDP and baseline conditions, respectively.

Dataset Citation

Bioregional Assessment Programme (XXXX) GLO AWRA Model Pre-Processing Data v01. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/51079bcc-96a8-409d-a951-3671fbbad6a2.

Dataset Ancestors

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

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

• Derived From GLO SW Receptors 20150828 withRivers&CatchmentAreas

• Derived From Groundwater Economic Assets GLO 20150326

• Derived From Gloucester digitised coal mine boundaries

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

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

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

• Derived From GLO SW receptor total catchment areas V01

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

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

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

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

• Derived From GLO Receptors 20150518

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

• Derived From Natural Resource Management (NRM) Regions 2010

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

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

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

• Derived From EIS Gloucester Coal 2010

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

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

• Derived From GEODATA TOPO 250K Series 3

• Derived From Asset database for the Gloucester subregion on 28 May 2015

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Geofabric Surface Cartography - V2.1

• Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv3 12032014

• Derived From EIS for Rocky Hill Coal Project 2013

• Derived From Bioregional Assessment areas v03

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

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

• Derived From Asset database for the Gloucester subregion on 8 April 2015

• Derived From Gloucester - Additional assets from local councils

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

• Derived From Asset database for the Gloucester subregion on 29 August 2014

• Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted

• Derived From Groundwater Modelling Report for Stratford Coal Mine

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

• Derived From NSW Office of Water Groundwater Licence Extract Gloucester - Oct 2013

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

• Derived From Freshwater Fish Biodiversity Hotspots

• Derived From NSW Office of Water Groundwater licence extract linked to spatial locations GLOv2 19022014

• Derived From GLO climate data stats summary

• Derived From Australia - Species of National Environmental Significance Database

• **Derived

There are a growing number of land cover data available for the conterminous United States, supporting various applications ranging from biofuel regulatory decisions to habitat conservation assessments. These datasets vary in their source information, frequency of data collection and reporting, land class definitions, categorical detail, and spatial scale and time intervals of representation. These differences limit direct comparison, contribute to disagreements among studies, confuse stakeholders, and hamper our ability to confidently report key land cover trends in the U.S. Here we assess changes in cropland derived from the Land Change Monitoring, Assessment, and Projection (LCMAP) dataset from the U.S. Geological Survey and compare them with analyses of three established land cover datasets across the coterminous U.S. from 2008-2017: (1) the National Resources Inventory (NRI), (2) a dataset Lark et al. 2020 derived from the Cropland Data Layer (CDL), and (3) a dataset from Potapov et al. 2022. LCMAP reports more stable cropland and less stable noncropland in all comparisons, likely due to its more expansive definition of cropland which includes managed grasslands (pasture and hay). Despite these differences, net cropland expansion from all four datasets was comparable (5.18-6.33 million acres), although the geographic extent and type of conversion differed. LCMAP projected the largest cropland expansion in the southern Great Plains, whereas other datasets projected the largest expansion in the northwestern and central Midwest. Most of the pixel-level disagreements (86%) between LCMAP and Lark et al. 2020 were due to definitional differences among datasets, whereas the remainder (14%) were from a variety of causes. Cropland expansion in the LCMAP likely reflects conversions of more natural areas, whereas cropland expansion in other data sources also captures conversion of managed pasture to cropland. The particular research question considered (e.g., habitat versus soil carbon) should influence which data source is more appropriate.

How to citePlease cite the original dictionary and the dataset and code in this repository as follows:Kähler, Hans. (1987). Enggano-Deutsches Wörterbuch (Veröffentlichungen Des Seminars Für Indonesische Und Südseesprachen Der Universität Hamburg 14). Berlin; Hamburg: Dietrich Reimer Verlag. https://search.worldcat.org/title/18191699.Rajeg, Gede Primahadi Wijaya; Pramartha, Cokorda Rai Adi; Sarasvananda, Ida Bagus Gede; Widiatmika, Putu Wahyu; Segara, Ida Bagus Made Ari; Pita, Yul Fulgensia Rusman; et al. (2024). Retro-digitised Enggano-German dictionary derived from Kähler’s (1987) “Enggano-Deutsches Wörterbuch”. University of Oxford. Dataset. https://doi.org/10.25446/oxford.28057742OverviewThis is a hand-digitised Enggano-German Dictionary derived from Hans Kähler's (1987) “Enggano-Deutsches Wörterbuch”. We crowdsourced the digitisation process by transcribing the dictionary's content into an online database system; the system was set up by Cokorda Pramartha and I B. G. Sarasvananda in collaboration with the first author. The database is exported into a .csv file to be further processed computationally and manually, such as fixing typos and incorrect mapping of the entry element, providing the English and Indonesian translations, and standardising the orthography.A pre-release can be accessed here. The minor update in the current version includes adding a description of the column names for the tabular data of the digitised dictionary. The dictionary is stored as a table for three file types: .rds (for the R data format), .csv, and .tsv.Aspects to be worked out for the future development of the dataset can be accessed here.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple datasets. The source dataset is identified in the Lineage field in this metadata statement. The processes …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple datasets. The source dataset is 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. AWRA-L model calibration outputs reulting from modelling based on observation data from 16 streamflow gauges. The model outputs include high streamflow calibration results and low streamflow calibration results. The AWRA-L model version 4.5 has been used for all BA surface water simulations. The application is developed with the C# language. The executable file "BACalibrationAndSimulationApp.exe" generates global definition files which define the input and output data and input time series locations. The executable file "SimulateModel.exe" runs simulations based on the global definition files and outputs required variables (Qtot, Qb, Dd) in NetCDF format. All simulation runs have implemented on local Windows 7 work stations. Purpose This data used as a reference to evaluate uncertainty analysis results. Dataset History This is the model calibration results obtained from AWRA-L model.The model outputs include high streamflow calibration results and low streamflow calibration results.The model calibration was carried out against historical streamflow data obtained from 16 catchments, which was obtained in in 2 February 2014. The model calibration outputs were generated in 20 November 2014. The executable file "BACalibrationAndSimulationApp.exe" generates global definition files which define the input and output data and input time series locations. The executable file "SimulateModel.exe" runs simulations based on the global definition files and outputs required variables (Qtot, Qb, Dd) in NetCDF format. All simulation runs have implemented on local Windows 7 work stations. Dataset Citation Bioregional Assessment Programme (2015) GLO AWRA-L Model calibration v01. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/fb6fb884-93df-4fac-bd8f-d1f394e52308. Dataset Ancestors Derived From Standard Instrument Local Environmental Plan (LEP) - Heritage (HER) (NSW) Derived From NSW Office of Water GW licence extract linked to spatial locations - GLO v5 UID elements 27032014 Derived From GLO SW Receptors 20150828 withRivers&CatchmentAreas Derived From Gloucester digitised coal mine boundaries Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014 Derived From Gloucester Surface Water Discharge & Quality extract v1 060314 Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv4 UID 14032014 Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From Geofabric Surface Catchments - V2.1 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Asset database for the Gloucester subregion on 12 September 2014 Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008 Derived From National Groundwater Information System (NGIS) v1.1 Derived From GLO Receptors 20150518 Derived From Groundwater Entitlement Data GLO NSW Office of Water 20150320 PersRemoved Derived From Asset database for the Gloucester subregion on 8 April 2015 Derived From Natural Resource Management (NRM) Regions 2010 Derived From Groundwater Entitlement Data Gloucester - NSW Office of Water 20150320 Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted Derived From Mean Annual Climate Data of Australia 1981 to 2012 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From EIS Gloucester Coal 2010 Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From Selected catchment boundaries and their SILO cell percentages for AWRA modelling for the Gloucester subregion Derived From GEODATA TOPO 250K Series 3 Derived From GLO AWRA Model Pre-Processing Data v01 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Geological Provinces - Full Extent Derived From Geofabric Surface Cartography - V2.1 Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv3 12032014 Derived From EIS for Rocky Hill Coal Project 2013 Derived From Bioregional Assessment areas v03 Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012 Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From Asset database for the Gloucester subregion on 28 May 2015 Derived From Gloucester - Additional assets from local councils Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From BA SYD 1 sec SRTM (h) DEM and hydrological derivatives Derived From Asset database for the Gloucester subregion on 29 August 2014 Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From Groundwater Modelling Report for Stratford Coal Mine Derived From Groundwater Economic Assets GLO 20150326 Derived From GLO SW Model Calibration Gauges v01 Derived From NSW Office of Water Groundwater Licence Extract Gloucester - Oct 2013 Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From Freshwater Fish Biodiversity Hotspots Derived From NSW Office of Water Groundwater licence extract linked to spatial locations GLOv2 19022014 Derived From GLO SW receptor total catchment areas V01 Derived From GLO climate data stats summary Derived From Australia - Species of National Environmental Significance Database Derived From Bioregional Assessment areas v01 Derived From Bioregional Assessment areas v02 Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From NSW Office of Water Groundwater Entitlements Spatial Locations Derived From GLO Receptors 20150828 Derived From Report for Director Generals Requirement Rocky Hill Project 2012 Derived From Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM) Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release) Derived From Climate model 0.05x0.05 cells and cell centroids

The English-Latvian Financial Dictionary is an automatically created dictionary that has been derived from the training of unsupervised machine translation system. The dictionary has been developed in the framework of the CEF project MT4ALL (http://ixa2.si.ehu.eus/mt4all/project). We license the actual packaging of this data under a CC0 1.0 Universal License.

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

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This metadata contains data for the AWRA-L model. In the Namoi subregion both the AWRA-L and AWRA-R modelling were done. AWRA-L flow outputs were used as model inputs for AWRA-R. The metadata within the dataset contains the workflow, processes, inputs and outputs data. The workflow pptx file under the top folder provides the top level summary of the modelling framework, including two slides. The first slide explains how to generate global definition file; the second slide outlines the calibration and simulation for AWRA-L model run. The exectable model framework is under the Application subfolder Other subfolders, including model calibration, model simulation, post processing, contain the associated files used for model calibration, simulation and post processing, respectively. Documentation about the implementation of AWRA-L in the Namoi subregion is provided in BA NAM 2.6.1.3 and 2.6.1.4 products. Dataset History AWRAL_Metadata Overview of modelling sequence for AWRA-L and AWRA-R is given in '..\AWRAL_Metadata\Namoi_Model_Sequence.pptx'. Here AWRA-L is described. The directorie contains the input and output data of the Namoi AWRA-L model for model calibration, simulation and post-processing. The model calibration folder contains the input and output subfolders used for two model calibration schemes: lowflow and normal. The lowflow model calibration puts more weight on median and low streamflow; the normal model calibration puts more weight on high streamflow. The model simulation input folder contains three folders as described below. The model simulation output folder contains only one replicate of model input and output as an example The pre-processing folder contains three subfolders: inputs, outputs and scripts used for generating streamflow under the baseline and coal mine resources development conditions. Input and output files are the daily data covering the period of 1953 to 2102, with the first 30 years (1953-1982) for model spin-up. Documentation about the implementation of AWRA-L in the Namoi bioregion is provided in BA NAM 2.6.1.3 and 2.6.1.4 products. Data details are below Model calibrations Climate forcings are under '... AWRAL_Metadata\model calibration\inputs\Climate' Lowflow calibration data including catchment location, global definition mapping, objective definition and optimiser definition under '... AWRAL_Metadata\model calibration\inputs\lowflow' Normal flow calibration data including catchment location, global definition mapping, objective definition and optimiser definition under '... AWRAL_Metadata\model calibration\inputs ormal' Observed streamflow data used for model calibrations are under '... AWRAL_Metadata\model calibration\inputs\Streamflow' Model simulations Climate forcings are under '... AWRAL_Metadata\model simulation\inputs\Climate' Global definition file used in netCDF output mode data is under '... AWRAL_Metadata\model simulation\inputs\GlobalDefFile' Output files used in netCDF output mode data contain dgw and Qtot outputs, which is used for AWRA-L postprocessing and is under '... AWRAL_Metadata\model simulation\outputs\Netcdf_Model_1' Application Gives the application files Post-processing Post processing was done as pre-processing for AWRA-R model and is given in '..\AWRAR_Metadata_NGW\pre processing' Dataset Citation Bioregional Assessment Programme (2017) Namoi AWRA-L model. Bioregional Assessment Derived Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/0d58109a-f3b3-4f3a-9bc0-f8d8398d4da8. Dataset Ancestors Derived From Historical Mining Footprints DTIRIS NAM 20150914 Derived From Namoi Environmental Impact Statements - Mine footprints Derived From Namoi Surface Water Mine Footprints - digitised Derived From Namoi Hydstra surface water time series v1 extracted 140814 Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008 Derived From Namoi Existing Mine Development Surface Water Footprints

E-OBS is a daily gridded land-only observational dataset over Europe. The blended time series from the station network of the European Climate Assessment & Dataset (ECA&D) project form the basis for the E-OBS gridded dataset. All station data are sourced directly from the European National Meteorological and Hydrological Services (NMHSs) or other data holding institutions. For a considerable number of countries the number of stations used is the complete national network and therefore much more dense than the station network that is routinely shared among NMHSs (which is the basis of other gridded datasets). The density of stations gradually increases through collaborations with NMHSs within European research contracts. Initially, in 2008, this gridded dataset was developed to provide validation for the suite of Europe-wide climate model simulations produced as part of the European Union ENSEMBLES project. While E-OBS remains an important dataset for model validation, it is also used more generally for monitoring the climate across Europe, particularly with regard to the assessment of the magnitude and frequency of daily extremes. The position of E-OBS is unique in Europe because of the relatively high spatial horizontal grid spacing, the daily resolution of the dataset, the provision of multiple variables and the length of the dataset. Finally, the station data on which E-OBS is based are available through the ECA&D webpages (where the owner of the data has given permission to do so). In these respects it contrasts with other datasets. The dataset is daily, meaning the observations cover 24 hours per time step. The exact 24-hour period can be different per region. The reason for this is that some data providers measure between midnight to midnight while others might measure from morning to morning. Since E-OBS is an observational dataset, no attempts have been made to adjust time series for this 24-hour offset. It is made sure, where known, that the largest part of the measured 24-hour period corresponds to the day attached to the time step in E-OBS (and ECA&D).

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.

A single instance of the surface water model for the Clarence Moreton region, as documented in Gilfedder et al. 2016. This dataset contains a Global definition file for AWRA-L model, i.e. GlobDefinition.xml, which is the only input file for an AWRA-L model run, and the output baseflow (Qb) and streamflow (Qtot) netcdf files for each run, i.e. Qg.nc and Qtot.nc.

A technical description of the AWRA-L 4.5 model can be found at https://publications.csiro.au/rpr/download?pid=csiro:EP162100&dsid=DS1

Dataset History

The AWRA-L model version 4.5 has been used for all Bioregional Assessments surface water simulations. The application is developed with the C# language. All execution and class (dll) files can be found at \OSM-07-CDC.it.csiro.au\OSM_CBR_LW_BA_working\Disciplines\SurfaceWater\Modelling\AWRA-LG\Bin. The executable file "BACalibrationAndSimulationApp.exe" generates global definition files (GlobDefinition.xml) which define the input and output data and input time series locations. The executable file "SimulateModel.exe" runs simulations based on the global definition files and outputs required variables (Qtot, Qb, Dd) in NetCDF format. All simulation runs have implemented on local Windows 7 workstations.

Dataset Citation

Bioregional Assessment Programme (2016) CLM AWRA model. Bioregional Assessment Derived Dataset. Viewed 09 October 2017, http://data.bioregionalassessments.gov.au/dataset/abfefbbf-4cc3-4b05-a4ea-1a79e916e72b.

Dataset Ancestors

• Derived From CLM - AWRA Calibration Gauges SubCatchments

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From CLM16swg Surface water gauging station data within the Clarence Moreton Basin

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

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

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

Overview of key attributes for land cover change data used to estimate cropland change in this study.

The 1 second SRTM derived DEM-H Version 1.0 is a 1 arc second (~30 m) gridded digital elevation model (DEM) that has been hydrologically conditioned and drainage enforced. The DEM-H captures flow paths based on SRTM elevations and mapped stream lines, and supports delineation of catchments and related hydrological attributes. The dataset was derived from the 1 second smoothed Digital Elevation Model (DEM-S; ANZCW0703014016) by enforcing hydrological connectivity with the ANUDEM software, using selected AusHydro V1.6 (February 2010) 1:250,000 scale watercourse lines (ANZCW0503900101) and lines derived from DEM-S to define the watercourses. The drainage enforcement has produced a consistent representation of hydrological connectivity with some elevation artefacts resulting from the drainage enforcement. A full description of the methods is in preparation (Dowling et al., in prep).

This product is the last of the Version 1.0 series derived from the 1 second SRTM (DSM, DEM, DEM-S and DEM-H) and provides a DEM suitable for use in hydrological analysis such as catchment definition and flow routing.

Abstract

This dataset was derived from groundwater data provided by the NSW Office of Water. You can find a link to the source dataset in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset represents the best available Groundwater entitlement data available for the Gloucester PAE at the time of Writing. 26/03/2015

This data has been created where possible to assign Groundwater volume entitlement information from the NSW office of Water licencing systems where possible to a location in the Gloucester PAE.

Please note licencing information in NSW can be difficult to assign to a location. If a licence is in the process of being traded it may not be tied to a location at the time of data extraction hence it is also difficult to reproduce exact figures to match previous or published sources.

The data included here is primarily from a prepackaged extract from the NSW Office of Water of their definition of the Glouster region. An overlay of their bore data shows that this area would represent most of the allocation activity in the BA Glouster PAE. This data has also been cross checked with NSW Office of Water fact sheets.

Purpose

Processing Groundwater Economic Assets for Gloucester.

Dataset History

This data was primarily created from a prepackaged extract from the NSW Office of Water for their definition of the Glouster region. (date)

The primary file "Gloucester_Basin_Licensed_Bores.csv" included a works number which could be joined to an extract of the National Groundwater Information system (NSW update, Nov 2014 - include GUID).

Tables with volume were then joined by licence number and volume allocated per works (bore in this case).

As significant volumes did not join to a bore other information was sourced to join these to a location.

The following two data sources provided information to join volumes to a location in the area.

1) Geoscience Mining locations - The centroid of the min property was used to assign volume to

2) Publication listing the other Industry agriculture bores (Water Management Plan for the Tiedman

Irrigation Program - Gloucester, May 2012) - A property number for the licences without locations were found here and these were identified from the NSW Cadastre website:http://maps.six.nsw.gov.au/

3) Water Sharing Plans, GMU Feb, 2015

Dataset Citation

Bioregional Assessment Programme (2015) Groundwater Economic Assets GLO 20150326. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/2e314212-0677-40b8-86ff-c5166c6906bd.

Dataset Ancestors

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

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