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49 results for “global south”
Urban heat hazard in the Global South
<p>This dataset includes various estimates of fine-grained outdoor heat hazard and vegetation metrics for cities in the Global South, and also for U.S. cities.<br><br>Extent of urban clusters based on the Global Human Settlement Layer (GHSL; RWI_ALL_v2.geojson), European Space Agency Climate Change Initiative (ESACCI; RWI_ALL_GLOB_v2.geojson) land cover data, and U.S. urbanized areas (GLOB_distance_US.geojson) in geojson format.</p> <p>The geojsons and data tables have several environmental and socioeconomic variables by grid and census tract (for the U.S.).<br><br>The variables are:</p> <p>AT_max: 10-year (2010-2019) average maximum annual air temperature<br>AT_max_summ: 10-year (2010-2019) average maximum summer air temperature<br>AT_min: 10-year (2010-2019) average minimum annual air temperature<br>AT_min_summ: 10-year (2010-2019) average minimum summer air temperature<br>All_area: Area of the grid or census tract<br>EVI: Mean 10-year (2010-2019) Enhanced Vegetation Index<br>Grass_area: Area of grassland for ~2020 from the ESA WorldCover dataset<br>ID: ID of urban cluster (U.S. urbanized areas havr a 'NAME' for the urbanized area instead)<br>LST_day: 10-year (2010-2019) average daytime annual land surface temperature from MODIS Aqua<br>LST_day_summ: 10-year (2010-2019) average daytime summer land surface temperature from MODIS Aqua<br>LST_night: 10-year (2010-2019) average nighttime annual land surface temperature from MODIS Aqua<br>LST_night_summ: 10-year (2010-2019) average nighttime summer land surface temperature from MODIS Aqua<br>PM25: 10-year (2010-2019) average particulate matter below 2.5 micron (not used in paper)<br>Pop: Population of grid or census tract<br>REGION_WB: World Bank region<br>SUBREGION: World Bank subregion (not used in paper)<br>Tree: 10-year (2010-2019) average tree percentage from MODIS continuous vegetation fields<br>Tree_area: Area of trees for ~2020 from the ESA WorldCover dataset<br>Veg: 10-year (2010-2019) average non-tree vegetation percentage from MODIS continuous vegetation fields<br>error: Error in Relative Wealth Index<br>rwi: relative Wealth Index</p> <p> </p> <p>In addition to these, the 'distance' files include a column for distance of the grids or census tract from the centroid of the cluster in belongs to.</p>
Open Access in the Global South: Perspectives from the Open and Collaborative Science in Development Network
<p>As with science in general, the discussion around open access has generally been driven by the institutions and perspectives of western or global North countries. However, approaches to open access are far more diverse and there are alternative approaches that have not gained visibility, especially in historically marginalized communities. This presentation will present some key lessons of the OCSDNet http://ocsdnet.org. The OCSDNet is a research network that engaged in participatory research and consultation with scientists, development practitioners, community members and activists from 26 countries in Latin America, Africa, the Middle East and Asia to understand the values at the core of open science in development. What we learned is that there is not one right way to do open science, and “openness” requires constant negotiation and reflection, and the process will always differ by context due to historical and socio-political factors. The set of seven values and principles at the core of the OCSDNet manifesto will be discussed for a more inclusive open science in development. More important, we will discuss the implications of these values in relation to the aspirations and features of COAR’s Next Generation Repository.</p>
Figure 1 in Is the global decline reflects local declines? A case of the population trend of Far Eastern Curlew Numenius madagascariensis in Banyuasin Peninsula, South Sumatra, Indonesia
Figure 1. Map of Banyuasin Peninsula, South Sumatra, Indonesia.
Fig. 1 in The neglected diversity: Description and molecular characterisation of Trypanosoma haploblephari Yeld and Smit, 2006 from endemic catsharks (Scyliorhinidae) in South Africa, the first trypanosome sequence data from sharks globally
Fig. 1. Map of sampling sites on the south coast of South Africa.
Geospatial micro-estimates of slum populations in 129 Global South countries using machine learning and public data
<p><span>Reliable estimation of populations living in slums or slum-like conditions is crucial for urban planning, humanitarian resource allocation, and human well-being improvement. We generate the micro-estimate of slum population at a neighborhood level (~</span><span>3.63 arc-minutes</span><span>, preserving the privacy of vulnerable people) for 129 Global South countries in 2018. The estimates are built based on the Sustainable Development Goals 11.1 indicator framework and machine learning algorithms to heterogeneous data from household-based surveys and satellite images, as well as grided population data. Our integrated regional models show strong predictive capabilities for cluster-level slums proxy, explaining 82% to 96% of the variation in ground-truth surveys conducted in Global South countries, with root mean squared error ranging from 4.85% to 10.47%. The models perform match or surpass benchmarks established by previous studies.</span><span> </span><span>Cross-comparison with independent data sources at multi-scales suggest that our approach can yield reliable and consistent slum population estimates.</span></p>
Data from: Dissecting biodiversity in a global hotspot: uneven dynamics of immigration and diversification within the Cape Floristic Region of South Africa
Aim: Fragmented distributions should show immigration and diversification dynamics consistent with the predictions of island biogeography theory. We test whether this applies to the fragmented Cape fynbos vegetation. Location: Southern Africa, Cape Floristic Region (CFR) Taxon: Angiosperms, Restionaceae (restios) Methods: We used a large occurrence dataset and environmental layers to characterize an existing regionalization and the intervals between the regions ecologically and spatially. We extended the available phylogeny for restios and inferred their historical biogeography using models implemented in BioGeoBEARS. We then measured the relative contribution of immigration and in situ speciation to the species richness of each region within the CFR. We used standard statistical methods to test the predictions of the island biogeography theory. Results: The area and environmental heterogeneity of the seven regions of the CFR are positively correlated with in situ speciation rate. Furthermore, more isolated areas, and areas colonized more recently, have proportionally higher immigration rates, and more central and older areas proportionally higher in situ speciation rates. Main Conclusions: The variation in immigration and diversification dynamics among the regions within the CFR is extensive and consistent with the archipelago model of island biography theory. This dynamic may contribute significantly to the diversity of the Cape flora. Such a model could be generally useful for understanding the generation and maintenance of diversity in biodiversity hotspots, and may even scale up to explain continental biodiversity.
Supplementary material 2 from: Nkuna KV, Visser V, Wilson JRU, Kumschick S (2018) Global environmental and socio-economic impacts of selected alien grasses as a basis for ranking threats to South Africa. NeoBiota 41: 19-65. https://doi.org/10.3897/neobiota.41.26599
Figure S2 : Explanation note: The impact magnitude of the 48 studied alien grasses across different habitats. The impact magnitudes on the x-axis are the least-square means of the impact scores as derived from a cumulative link mixed effects model. On the y-axis are the habitat types impacted by alien grasses and in brackets is the number of species with records in that habitat. The points represent the impact magnitudes and the error bars represent 95 % confidence intervals. Letters on the right side of the confidence intervals are level groupings indicating no significant differences among the habits. Comparisons are Tukey adjusted.
Supplementary material 1 from: Nkuna KV, Visser V, Wilson JRU, Kumschick S (2018) Global environmental and socio-economic impacts of selected alien grasses as a basis for ranking threats to South Africa. NeoBiota 41: 19-65. https://doi.org/10.3897/neobiota.41.26599
Supplementary material 1 from: Nkuna KV, Visser V, Wilson JRU, Kumschick S (2018) Global environmental and socio-economic impacts of selected alien grasses as a basis for ranking threats to South Africa. NeoBiota 41: 19-65. https://doi.org/10.3897/neobiota.41.26599
Investigating the "Too Bright" Issue Pertaining to Non-PBL Clouds over the South Pacific Trade-Wind Region in CMIP6 Global Climate Models
<p><a href="../api/records/13314147/draft/files/f09.C6.B-hist.SON_ANN.tar.gz/content" target="_blank" rel="noopener noreferrer">f09.C6.B-hist.SON_ANN.tar.g</a>z</p> <p>CESM2-CAM6 with falling ice radiative effects (FIREs), fully coupled run folloing CMIP6 historical run, same as CESM2-CAM6 in CMIP6 data port.</p> <p> </p> <p>The data includes with netcdf self description.</p> <p>f09.C6.B-hist.h01_AWNC_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLDHGH_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLDLOW_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDMED_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLDTOT_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_CLOUD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CLOUDFRAC_CLUBB_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_CONCLD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_FREQL_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_ICWMR_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_NUMLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_OMEGA_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.h01_PRECC_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_PRECL_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_SST_ANN_climo-CDO.nc<br>f09.C6.B-hist.h01_tauy_ANN_climo-CDO.nc</p> <p><a href="../api/records/13314147/draft/files/f09.C6.B-hist.SON_ANN.tar.gz/content" target="_blank" rel="noopener noreferrer">f09.C6.B-hist.NOS_ANN.tar.g</a>z</p> <p>CESM2-CAM6 without falling ice radiative effects (FIREs), fully coupled run folloing CMIP6 historical run, same as CESM2-CAM6 in CMIP6 data port.</p> <p><br>f09.C6.B-hist.nos81_AWNC_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CDNUMC_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDHGH_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CLDLOW_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDMED_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLDTOT_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_CLOUD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CLOUDFRAC_CLUBB_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_CONCLD_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_FREQL_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_ICWMR_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_NUMLIQ_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_OMEGA_ANN_15L-CDO_1x1.nc<br>f09.C6.B-hist.nos81_PRECC_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_PRECL_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_SST_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_taux_ANN_climo-CDO.nc<br>f09.C6.B-hist.nos81_tauy_ANN_climo-CDO.nc</p>
Thaliaceans data in the global ocean and the South China Sea
<p>This dataset contains two sheets, one is the global presence-absence records of some typical thaliacean species with environmental data and the other one is the abundance of thaliaceans with environmental data in the South China Sea.</p>
Data from: Dissecting biodiversity in a global hotspot: uneven dynamics of immigration and diversification within the Cape Floristic Region of South Africa
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Data from: Woody encroachment over 70 years in South African savannas: overgrazing, global change or extinction aftershock?
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Data from: Coordinated species importation policies are needed to reduce serious invasions globally: the case of alien bumblebees in South America
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Supplementary material 2 from: Madzivanzira TC, Weyl OLF, South J (2022) Ecological and potential socioeconomic impacts of two globally-invasive crayfish. NeoBiota 72: 25-43. https://doi.org/10.3897/neobiota.72.71868
Field and laboratory photos showing crayfish damage
Supplementary material 1 from: Madzivanzira TC, Weyl OLF, South J (2022) Ecological and potential socioeconomic impacts of two globally-invasive crayfish. NeoBiota 72: 25-43. https://doi.org/10.3897/neobiota.72.71868
Macrophyte dry weight determination and morphometric averages (± SE) of used animals
SI_Robust Increase in South Asian Monsoon Rainfall Under Global Warming Driven by Southern Ocean Heat Uptake and Eurasia Cloud Changes
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South African global environmental change literature 2000-2018
<p>These data were used to assess how much research has been directed towards biological invasions in South Africa relative to other elements of global change and form part of a published book chapter. Using Web of Science, we systematically reviewed literature relevant to South African ecosystems published between 2000 and 2018 and relating to biological invasions, climate change, overharvesting, habitat change, pollution, and/or atmospheric CO<sub>2</sub> (search term details are provided below). We identified 1149 relevant papers, which appear in this dataset. These were scored in terms of their coverage of drivers and driver interactions that affect biodiversity or ecosystem services.</p> <p> </p> <p><em>Search terms used to identify relevant global change literature in South Africa</em></p> <p>We used an advanced search in the ISI Web of Science to identify potentially relevant research in South Africa. We constrained the search to use only the Science Citation index Expanded and Book Citation Index - Science, using a Timespan of 2000-2018. We performed six separate searches for each of the drivers and combined the results, removing any duplicate studies that were identified. The search terms used were:</p> <p> </p> <p>For Alien species</p> <p>TS = ((Invasiv* OR alien* OR exotic* OR non-native OR "non native" OR non-indigenous OR "non-indigenous" AND species) AND "South Africa*" AND (ecosystem* OR biodiversity) AND (impact* OR effect* OR trend*))</p> <p> </p> <p>For Climate change</p> <p>TS = (“climate change” AND “South Africa*” AND (ecosystem* OR biodiversity) AND (impact* OR effect* OR trend*))</p> <p><br> For CO<sub>2</sub></p> <p>TS = ((CO2 OR "carbon dioxide") AND "South Africa*" AND (ecosystem* OR biodiversity) AND (impact* OR effect* OR trend*))</p> <p><br> For Overharvesting</p> <p>TS = ((*harvesting OR *exploitation OR "resource use" OR "resource-use" OR hunting OR fishing OR extract*) AND "South Africa*" AND (ecosystem* OR biodiversity) AND (impact* OR effect* OR trend*))<br> </p> <p>For Pollution</p> <p>TS = (((pollut* OR *toxic* OR chemic* OR nitro* OR nitr* OR N2 OR phosph* OR sewage OR sewerage OR *plastic* OR acid*) AND "South Africa*" AND (ecosystem* OR biodiversity) AND (impact* OR effect* OR trend*)))</p> <p><br> For Habitat change</p> <p>TS = (("habitat change" OR "land cover" OR "land transformation" OR "land use" OR landuse OR land-use OR land-cover OR agriculture OR forestry OR mining OR ploughing OR erosion OR *fire OR disturbance* OR desertification OR aridification OR degradation OR "habitat alteration" OR fragmentation OR *trawling) AND "South Africa*" AND (ecosystem* OR biodiversity) AND (impact* OR effect* OR trend*))</p> <p> </p> <p>For each unique publication identified, we read the title and abstract and removed any studies that took place outside of South Africa (including those conducted in neighbouring countries such as Namibia, Swaziland and Lesotho) as well as those deemed to be beyond the study scope. The latter category included experimental studies with no clear link to a future time period (e.g. impacts of very high carbon dioxide concentrations), studies that valued ecosystem services as well as those that described restoration efforts, purely ecological studies, with no direct consideration of change drivers, studies that detailed management options for biodiversity and ecosystem services (including studies on biological control of invasive species) and descriptions of new alien species or their establishment. The final dataset that was scored consisted of 1149 papers.</p> <p> </p> <p>For each paper, we read the title and abstract and recorded (binary 0 or 1) as many direct driver effects on biodiversity and ecosystem services (out of the possible 6) or interactions of drivers. For example, a paper that demonstrated the impacts of drought on pollutant concentrations, with subsequent eutrophication and algal blooms would be counted as a direct effect of pollution on biodiversity and ecosystem services as well as an interaction of “Climate on pollution” and “Pollution on habitat”. We also recorded the environment (terrestrial, freshwater or marine and estuarine) in which the study took place.</p>
Data from: extended use and end of life in the Global South: a transportation justice perspective of US-Mexico second-hand vehicle trade
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Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 South America product 30 m V001
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South America for nominal year 2015 at 30 meter resolution (GFSAD30SACE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SACE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SACE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area.Known Issues* Known issues, including constraints and limitations, are provided on page 18 of the ATBD.
Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 South Asia, Afghanistan, and Iran product 30 m V001
The NASA Making Earth System Data Records for Use in Research Environments ([MEaSUREs](https://earthdata.nasa.gov/about/competitive-programs/measures)) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over South Asia, Afghanistan, and Iran for nominal year 2015 at 30 meter resolution (GFSAD30SAAFGIRCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30SAAFGIRCE data product uses the pixel-based supervised classifier, Random Forest (RF), to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30SAAFGIRCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area.Known Issues* Known issues, including constraints and limitations, are provided on page 18 of the ATBD.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.