The "Forest Proximate People" (FPP) dataset is one of the data layers contributing to the development of indicator #13, “number of forest-dependent people in extreme poverty,” of the Collaborative Partnership on Forests (CPF) Global Core Set of forest-related indicators (GCS). The FPP dataset provides an estimate of the number of people living in or within 5 kilometers of forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Madrid, M., & Pina, L. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Rome, FAO.

The "Tree Proximate People" (TPP) dataset is one of the data layers contributing to the development of indicator #13, “number of forest-dependent people in extreme poverty,” of the Collaborative Partnership on Forests (CPF) Global Core Set of forest-related indicators (GCS). The TPP dataset provides an estimate of the number of people living in or within 1 kilometers of trees outside forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level. Trees outside forests are defined as areas classified as agricultural lands with at least 10% tree cover. Code available to update annually using Google Earth Engine. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Madrid, M., & Pina, L. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Rome, FAO.

ABSTRACT Determination of soil properties helps in the correct management of soil fertility. The portable X-ray fluorescence spectrometer (pXRF) has been recently adopted to determine total chemical element contents in soils, allowing soil property inferences. However, these studies are still scarce in Brazil and other countries. The objectives of this work were to predict soil properties using pXRF data, comparing stepwise multiple linear regression (SMLR) and random forest (RF) methods, as well as mapping and validating soil properties. 120 soil samples were collected at three depths and submitted to laboratory analyses. pXRF was used in the samples and total element contents were determined. From pXRF data, SMLR and RF were used to predict soil laboratory results, reflecting soil properties, and the models were validated. The best method was used to spatialize soil properties. Using SMLR, models had high values of R² (≥0.8), however the highest accuracy was obtained in RF modeling. Exchangeable Ca, Al, Mg, potential and effective cation exchange capacity, soil organic matter, pH, and base saturation had adequate adjustment and accurate predictions with RF. Eight out of the 10 soil properties predicted by RF using pXRF data had CaO as the most important variable helping predictions, followed by P2O5, Zn and Cr. Maps generated using RF from pXRF data had high accuracy for six soil properties, reaching R2 up to 0.83. pXRF in association with RF can be used to predict soil properties with high accuracy at low cost and time, besides providing variables aiding digital soil mapping.

A collection of 15 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

Identification of cis-regulatory sequences controlling gene expression is an arduous challenge that is being actively explored to discover key genetic factors responsible for traits of agronomic interest. Here, we used a genome-wide de novo approach to investigate preferentially located motifs (PLMs) in the proximal cis-regulatory landscape of Arabidopsis thaliana and Zea mays. We report three groups of PLMs in both the 5’- and 3’-gene-proximal regions and emphasize conserved PLMs in both species, particularly in the 3’-gene-proximal region. Comparison with resources from transcription factor and microRNA binding sites shows that 79% of the identified PLMs are unassigned, although some are supported by MNase-defined cistrome occupancy analysis. Enrichment analyses further reveal that unassigned PLMs provide functional predictions that differ from those derived from transcription factor and microRNA binding sites. Our study provides a comprehensive map of PLMs and demonstrates their potential utility for future characterization of orphan genes in plants.

The purpose of this note is to briefly describe a detailed (30 m) spatial dataset that estimates the degree of human modification for the lands of Colorado reflecting ~2020 conditions. The degree of human modification is a well-established method to estimate the proximate human activities or processes that have caused, are causing, or may cause impacts on biodiversity and ecosystems. This includes stressors for: urban and built-up, crop and pasture lands, livestock grazing, oil and gas production, mining and quarrying, power generation (renewable and nonrenewable), roads, railways, power lines and towers, logging and wood harvesting, human intrusions, and air pollution.

Please see the attached PDF -- Technical note on a map of human modification in Colorado for 2020 -- that provides further details.

Use this global model layer when performing analysis across continents. This layer displays predictions globally of relative vulnerability to modification by humans by the year 2050. ESA CCI land cover maps from the years 2010 and 2018 were used to create this prediction.Variable mapped: Vulnerability of land cover to anthropogenic change by 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer can be used in analysis, to estimate and compare vulnerability to land cover change globally due to expansion of human activity, by 2050. This layer is useful in ecological planning, helping to prioritize areas for conservation. Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and global) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between proximate countries, use the country level. If mapping larger patterns or vastly separated countries, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasContinentCountryRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil Texture

Abstract This dataset maps the coastal landform types (geomorphology) of continental Australia and most adjacent islands (excluding the Great Barrier Reef). The dataset was compiled primarily using existing spatial data sets provided by a number of Commonwealth, State, Territory and other agencies from across Australia. The Smartline is a line map that represents the coastline (typically the High Water Mark), and is split into segments wherever any of the coastal landform attributes change. Each individual coastline segment has a series of attributes which describe the landform types of that segment of the coast. The coastal characteristics recorded refer not only to those at the precise location of the coastline itself, but to a coastal area nominally extending up 500m inland and offshore of the coastline itself. Where such information is available, these attributes describe the landforms in the subtidal, intertidal and backshore zones; the backshore profile and intertidal zone slope; the shoreline segment exposure; and the geological substrate. The dataset also contains attribute-level metadata: for each geomorphic attribute in each coastline segment, there are two additional attributes that identify the source dataset from which the attributes were derived, and defining the scale of the source dataset. Finally, there are a series of attributes which classify each coastline segment according to a shoreline landform stability classification scheme.

Currency Date Modified: 06 May 2019 Modification Frequency: As needed

Data Extent Coordinate reference: GDA94 / Australian Albers Spatial Extent North: -9°South: -44°East: 154°West: 112°

Source informationeCAT record: https://pid.geoscience.gov.au/service/ga/104560Geopackage download: https://pid.geoscience.gov.au/dataset/ga/104160Geodatabase download: https://pid.geoscience.gov.au/dataset/ga/104100Sharples, C., & Mount, R., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Manual and Data Dictionary; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/104100/104100_02_0.pdf Sharples, C., Mount, R., Pedersen, T., Lacey, M., Newton, J., Jaskierniak, D., & Wallace, L., 2009: The Australian Coastal Smartline Geomorphic and Stability Map Version 1: Project Report; School of Geography & Environmental Studies, University of Tasmania. https://d28rz98at9flks.cloudfront.net/83568/SmartlineProjectReport_2009_v1.pdfLineage statementThis dataset was derived from a total of 240 individual datasets. A complete list of these source datasets with additional metadata is available as a table in the Manual and Data Dictionary report and the Project report. Because of the wide variety of source datasets, a variety of different methods were used to extract and reclassify the source data. A generalised workflow is described below. Most of these steps were carried out using existing or newly-developed tools in ArcGIS 9.3:

For each coastal state, the best available coastline dataset was identified and obtained for use as a Smartline base map. Attributes from each source dataset were transferred to a copy of the Smartline base map for the relevant state. For aspatial and non-georeferenced source datasets, the transfer process was manual; for georeferenced datasets, the transfer process was automated as much as possible. The transferred attributes were reclassified and inserted into the appropriate Smartline field or fields using transfer tables and the Smartline Classification Scheme. The individual Smartline base maps containing attribute data from different source datasets were merged into a single state Smartline. Where conflicting or concordant attribute data were present for a single Smartline field, a resolution process was followed to choose a single attribute from those available. As a general rule, priority was given to the source datasets that contained the most relevant and detailed attribute information; if the available source datasets contained similar attribute information, preference was given to the source dataset with the best spatial resolution. A series of logical checks were carried out to ensure consistency between attributes in related Smartline attribute fields. Coastal features less than 10m long were merged with the most similar adjacent feature (based on attributes) where possible. Some features which were less than 10m long (generally very small islands) could not be merged because there were no adjacent features to merge with. A topology was developed and used to ensure that the Smartline geometry was identical to the original Smartline base map, and that the Smartline did not have any self-overlapping segments. All topological errors were repaired. Incorrectly spelled attribute data were detected and repaired using tables exported from an Access database containing the complete Smartline Classification Scheme. The Smartline Stability fields were populated using queries based on particular combinations of geomorphic attributes.For further details of the data compilation, processing, editing and verification processes, please refer to the Smartline Manual and Data Dictionary Report, and the Smartline Project Report. Data dictionary

Attribute name Description

OBJECTIDUnique IDBASELINEReference ID for source of base line mapBASEMAPSCALEScale of base mapBASEFEATURECoastal feature upon which base line map is basedAUSCOASTFIDUnique Australian coastal segment identifier number (v.1.0)UPDATEDDate of data currency or last updateABSAMP_IDBeach numberBACKPROX_NClass code of Backshore proximal landforms (The first notable landform type or assemblage present immediately to landwards of or above the intertidal zone (may include supratidal landforms))BACKPROX_VClass name of Backshore proximal landforms (The first notable landform type or assemblage present immediately to landwards of or above the intertidal zone (may include supratidal landforms))BACKPROX_RData source (reference) ID of Backshore proximal landforms (The first notable landform type or assemblage present immediately to landwards of or above the intertidal zone (may include supratidal landforms))BACKPROX_SSource data scale of Backshore proximal landforms (The first notable landform type or assemblage present immediately to landwards of or above the intertidal zone (may include supratidal landforms))COMMENTSGeneral notes and commentsContactOceans, Reefs, Coasts and the Antarctic (ORCA), Geoscience Australia. clientservices@ga.gov.au

Use this country model layer when performing analysis within a single country. This layer displays predictions within each country of relative vulnerability to modification by humans by the year 2050. ESA CCI land cover maps from the years 2010 and 2018 were used to create these predictions.Variable mapped: Vulnerability of land cover to anthropogenic change by 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer can be used in analysis, to estimate and compare vulnerability to land cover change globally due to expansion of human activity, by 2050. This layer is useful in ecological planning, helping to prioritize areas for conservation. Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and global) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between proximate countries, use the country level. If mapping larger patterns or vastly separated countries, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasContinentCountryRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil Texture

Underlying data of paper describing impact of alternative trajectories after farmland abandonment in Europe. Farmland abandonment is a major proximate driver of landscape change in European rural areas and is often followed by natural revegetation. In certain conditions, it might be preferable to prevent or reverse farmland abandonment or manage these areas towards active restoration (i.e., guided rewilding with wild or domesticated animals). These alternative responses to farmland abandonment lead to context-dependent impacts, which can potentially contribute to European Green Deal objectives for environment and rural areas. While previous studies analysed direct impacts of abandonment, there is little insight into how alternative ways of managing abandoned farmland can best contribute to environmental policy goals, and what type of management is preferred where. To assess opportunities in these areas, we compared three abandonment trajectories: natural revegetation, active restoration with rewilding, and extensive re-farming. We analysed the potential positive and negative environmental and cultural impacts of developing these management strategies in all farmland locations that could potentially be abandoned across Europe. Mapping and quantification of the benefits and risks associated with different management responses to abandonment indicates a large spatial variation across regions. While natural revegetation can support high benefits for carbon sequestration and erosion reduction, it is also linked to more frequent trade-offs than re-farming and rewilding. However, there is a very strong spatial variation in these trade-offs. It is worthwhile to focus on areas with the largest gains and fewest trade-offs when targeting investments for prevention of abandonment or rewilding. Our maps can help inform interventions in abandoned farmland to maximise the potential contributions of these lands to the European Green Deal environmental and rural policy targets.

The U.S. Geological Survey (USGS), in conjunction with collaborating geoscientists from agencies outside the United States, developed the World Coal Quality Inventory (WoCQI), a digital compilation focused on the major-, minor-, and trace-element chemistry of coals from around the world. The USGS studied international coals for many years previous to this project and this data file contains samples that pre-date the major effort of WoCQI, which was from 1995-2006. Analyses were performed by various laboratories: the USGS Energy Program Geochemistry Laboratory in Denver, CO did major-, minor-, and trace-elements and university or commercial laboratories did proximate and ultimate analysis. Data were published as U.S. Geological Survey Open-File Report 2010-1196.Chemical data are sorted alphabetically by country, and the information includes some or all of the following: proximate and ultimate analyses as well as forms of sulfur on an as-received basis, ash fusion temperatures (degrees Fahrenheit, reducing atmosphere), Hardgrove grindability index (HGI), free-swelling index (FSI), apparent specific gravity, major and minor oxide values (on ash), and major-, minor-, and trace-element concentrations on a whole-coal basis. Commercial laboratory analyses follow ASTM standard methods listed below. Data generated by the USGS laboratory also utilize ASTM methods, when possible. Available sample provenance information is also included.

The purpose of this note is to briefly describe a detailed (270 m) spatial dataset that estimates the degree of human modification for the lands of the continental US reflecting ~2021 conditions. The degree of human modification is a well-established method to estimate the proximate human activities or processes that have caused, are causing, or may cause impacts on biodiversity and ecosystems. This includes stressors for: urban and built-up, crop and pasture lands, livestock grazing, oil and gas production, mining and quarrying, power generation (renewable and nonrenewable), roads, railways, power lines and towers, logging and wood harvesting, human intrusions, and air pollution. More details are provided in the technical document PDF available here.

This work is copyrighted as CC 4.0 BY-NC-SA.

The purpose of this note is to briefly describe a detailed (270 m) spatial dataset that estimates the degree of human modification for the lands of the continental US reflecting ~2021 conditions. The degree of human modification is a well-established method to estimate the proximate human activities or processes that have caused, are causing, or may cause impacts on biodiversity and ecosystems. This includes stressors for: urban and built-up, crop and pasture lands, livestock grazing, oil and gas production, mining and quarrying, power generation (renewable and nonrenewable), roads, railways, power lines and towers, logging and wood harvesting, human intrusions, and air pollution. More details are provided in the technical document PDF available here.

This work is copyrighted as CC 4.0 BY-NC-SA.

Evidence map of risk factors and their association to maternal and neonatal mortality.

This map illustrates the refugee camp currently under construction in Al Azraq, Jordan using an image collected by the WorldView-2 satellite on 28 December 2013. As of 28 December 2013 a total of 3,174 structures were detected in the camp, 2,431 infrastructure and support buildings and 743 tent structures. Preparations are continuing so as to accommodate additional incoming refugees. The previous analysis done by UNOSAT using an image from 14 September 2013 detected a total of 2,689 infrastructure, support buildings and shelters. This is an increase of approximately 18%. Paved and unpaved roads have likewise increased significantly and define the transportation network in and around the camp. Water and sanitation services are also under development in multiple camp zones suitable for supporting thousands of proximate shelters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.

This map illustrates the refugee settlement in Al Azraq, Jordan as seen by the Pleiades satellite on 5 October 2015. Analysis by UNITAR-UNOSAT of the satellite image indicates a total of 14,227 structures are visible. This total includes 2,690 infrastructure and support buildings as well as 10,071 transitional shelters. Preparations are continuing so as to accommodate additional incoming refugees. The previous analysis done by UNOSAT using an image from 11 November 2014 detected a total of 12,761 infrastructure, support buildings and transitional shelters. This is an increase of approximately 0.5%. Water and sanitation services are also under development in multiple camp zones suitable for supporting thousands of proximate shelters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

Results of Mann–Whitney U test on map view duration on all wayfinding tasks between TBI and control group.

Use this regional model layer when performing analysis within a single continent. This layer displays predictions within each continent of relative vulnerability to modification by humans by the year 2050. ESA CCI land cover maps from the years 2010 and 2018 were used to create these predictions.Variable mapped: Vulnerability of land cover to anthropogenic change by 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer can be used in analysis, to estimate and compare vulnerability to land cover change globally due to expansion of human activity, by 2050. This layer is useful in ecological planning, helping to prioritize areas for conservation. Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and global) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between proximate countries, use the country level. If mapping larger patterns or vastly separated countries, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 - GlobalLand Cover Vulnerability to Change 2050 - RegionalLand Cover Vulnerability to Change 2050 - CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasContinentCountryRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil Texture

Values for 1-g SAR (SAR1g), 10-g local SAR (SAR10g) and global SAR (SARG), as well as SAR1g/SARG and SAR10g/SARG ratios calculated for the simulated E- maps, as achieved with the C (CH1, CH 2, CH 3, CH 4) and L (m1, m2, m3, m4) bases.