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Fig. 2 in Fig. 11. Left G1s in Tuerkayana latens, a New Species of Land Crab from French Polynesia, with a Discussion on the Phylogeny of the Genus (Crustacea: Decapoda: Brachyura: Gecarcinidae)
Fig. 2. Phylogenetic tree constructed with Maximum Likelihood (ML) based on the COI gene for selected sequences of A. amphitrite, focusing on Iranian populations (green circles). Amphibalanus reticulatus (JQ035518.1), A. variegatus (JQ035522.1) and Balanus glandula (KU204282.1) were used as outgroups. Numbers show the bootstrap values after 1000 pseudo-replicates. Clades I, II and III are corresponding to those in Chen et al. (2014).
Fig. 3 in Fig. 11. Left G1s in Tuerkayana latens, a New Species of Land Crab from French Polynesia, with a Discussion on the Phylogeny of the Genus (Crustacea: Decapoda: Brachyura: Gecarcinidae)
Fig. 3. Maximum-parsimony mitochondrial DNA haplotype networks for material from the PG and the GO, and the retrieved sequences from the GenBank (only sequences of the clade I in Chen et al. (2014) phylogenetic tree are included).
Fig. 7. MAXENT reconstruction for clade I in Fig. 11. Left G1s in Tuerkayana latens, a New Species of Land Crab from French Polynesia, with a Discussion on the Phylogeny of the Genus (Crustacea: Decapoda: Brachyura: Gecarcinidae)
Fig. 7. MAXENT reconstruction for clade I (A) and clade III (B) of Amphibalanus amphitrite in the world representing current distribution models.
Fig. 6 in Fig. 11. Left G1s in Tuerkayana latens, a New Species of Land Crab from French Polynesia, with a Discussion on the Phylogeny of the Genus (Crustacea: Decapoda: Brachyura: Gecarcinidae)
Fig. 6. Bayesian skyline plots of effective population size through time in Amphibalanus amphitrite from two biogeographical areas (A: PG and B: GO), based on the 576 bp sequences of COI and a nucleotide substitution rate of 3.1%/MY. The bold black curve is the median of the parameter NeT, which is proportional to the effective population size; the blue lines delimit the 95% highest posterior density. For comparison, all x-axes have the same scale. The plots are truncated to the median estimate of each area's TMRCA.
Fig. 5 in Fig. 11. Left G1s in Tuerkayana latens, a New Species of Land Crab from French Polynesia, with a Discussion on the Phylogeny of the Genus (Crustacea: Decapoda: Brachyura: Gecarcinidae)
Fig. 5. Frequency distribution of the number of pairwise nucleotide differences (mismatch) between COI haplotypes in the two populations of Amphibalanus amphitrite. The solid line shows the theoretical distribution under the model of demographic expansion.
Fig. 4 in Fig. 11. Left G1s in Tuerkayana latens, a New Species of Land Crab from French Polynesia, with a Discussion on the Phylogeny of the Genus (Crustacea: Decapoda: Brachyura: Gecarcinidae)
Fig. 4. Maximum-parsimony of mitochondrial DNA haplotype networks for the PG and the GO populations constructed in PapArt. Hatch marks represent mutations; numbers in the figure legend refer to sampling sites in table 1 and figure 1.
ISIMIP 3b, Land Use Models outputs intercomparison
<p>These data sets contain the information used to compare the land use models' outputs submitted as human forcings for ISIMIP3b and utilized for different impact analyses and teams under various scenarios (SSP1-RCP2.6, SSP3-RCP7.0, SSP5-RCP8.5). </p>
Figure 2 in Generalist ground-nesting bees dominate diversity survey in intensively managed agricultural land
Figure 2. Species richness compared between sampling periods. Dark grey bars: species from the genus Andrena Fabricius (Andrenidae); light grey bars: species from the genera: Halictus Latreille, Lasioglossum Curtis (Halictidae), Osmia Panzer (Megachilidae), and Nomada Scopoli (Apidae); black bars: species from the genus Bombus Latreille (Apidae). Different letters above the dark grey bars indicate a significant statistical difference between sampling periods in total species richness of all sampled genera (F (3, 42) = 20.01, p<0.001).
Figure 1 in Generalist ground-nesting bees dominate diversity survey in intensively managed agricultural land
Figure 1. Total number of bees sampled in this study at the four different sampling periods. Dark grey bars: individuals from the genus Andrena Fabricius (Andrenidae); light grey bars: individuals from the genera: Halictus Latreille, Lasioglossum Curtis (Halictidae), Osmia Panzer (Megachilidae), and Nomada Scopoli (Apidae); black bars: individuals from the genus Bombus Latreille (Apidae). Different letters above the dark grey bars indicate a significant statistical difference between sampling periods in activity-density of individuals from all sampled genera (F (3, 42) = 18.89, p<0.001).
Dakar land use map at street block level
<p>This datatset contains a land use classification of Dakar (Senegal) at the street block level. It was created following the methodology presented in [1].</p> <p>Description of the files:</p> <ul> <li>"Dakar_landuse_shapefile.zip" : Shapefile of the street blocks extracted from OpenStreetMap using [2] with classification results in the attribute table.</li> <li>"Dakar_landuse_style.zip" : Files for style of the shapefile.</li> </ul> <p>Attribute table content:</p> <ul> <li>"CAT", "GID" : ID of the street block</li> <li>"PROB_ACS" : Probability to belong to class ACS</li> <li>"PROB_AGRI" : Probability to belong to class AGRI</li> <li>"PROB_BARE" : Probability to belong to class BARE</li> <li>"PROB_DEPR" : Probability to belong to class DEPR</li> <li>"PROB_PLAN" : Probability to belong to class PLAN</li> <li>"PROB_VEG" : Probability to belong to class VEG</li> <li>"FIRST_LABE" : Class with the highest classification probability</li> <li>"SEC_LABEL" : Class with the second highest classification probability</li> <li>"FIRST_PROB" : Value of the highest classification probability</li> <li>"SEC_PROB" : Value of the second highest classification probability</li> <li>"UNCERTAIN" : Difference between "FIRST_PROB" and "SEC_PROB"</li> <li>"BUILT_PERC" : Percentage of the street blocks covered by built-up (from land cover map)</li> <li>"MAP_LABEL" : Final classification label with uncertainty and different density classes</li> </ul> <p>Legend classes label:</p> <ul> <li>"AGRI" : Agricultural vegetation</li> <li>"VEG" : Natural vegetation</li> <li>"BARE" : Bare soils</li> <li>"ACS" : Non-residential built-up (administrative, commercial, services, etc.)</li> <li>"PLAN" : Planned residential built-up</li> <li>"PLAN_LD" : Planned residential low density built-up</li> <li>"DEPR" : Deprived residential built-up</li> <li>"UNCERT" : Uncertain classification</li> </ul> <p>References:</p> <p>[1] Grippa, Tais, 2018, "Mapping urban land use at street block level using OpenStreetMap, remote sensing data and spatial metrics", <em>ISPRS Int. J. Geo-Inf.</em> <strong>2018</strong>, <em>7</em>(7), 246. <a href="https://doi.org/10.3390/ijgi7070246">https://doi.org/10.3390/ijgi7070246</a> </p> <p>[2] Grippa, Tais. 2018. “Osm Street Blocks Extraction.” Zenodo. <a href="https://doi.org/10.5281/zenodo.1290637">https://doi.org/10.5281/zenodo.1290637</a>.</p> <p>Funding: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>
Land use and volcanic forcing files for past2k CESM simulation
<p>Data associated with the manuscript entitled "Asymmetric Cooling of the Atlantic and Pacific Arctic during the Past Two Millennia: A Dual Observation-Modeling Study".</p> <p> Transient simulations for the past 2,000 years (past2k) have been proposed for the Paleoclimate Model Intercomparison Phase 4 (PMIP4) contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Jungclaus et al., 2017). Forcing data for the past2k experiments was compiled by PMIP4. However, there was no land-cover change available for prior to 850 CE when this past2k CESM simulation was started. We therefore constructed a land cover forcing (Dataset1.tar) for this past2k simulation from 1 CE to 849 CE as a superposition of HYDE3.1 cropland changes (Goldewijk et al. 2010, 2011) onto the land cover forcing for the CESM Last Millennium Ensemble runs (LME, Otto-Blisner et al. 2016) for 850 CE. Land cover forcing for this past2k CESM simulation from 850 CE to 2005 CE is taken from LME, which implements grassland changes in addition to cropland changes as compiled by PMIP3.</p> <p>PMIP4 Easy Volcanic Aerosol data (EVA, Toohey et al. 2016) was used to generate volcanic aerosol forcing file for the past2k simulation with CESM. EVA provides space-time distribution of volcanic aerosol mass, effective radius, and optical depth. CESM reads in space-time distribution of volcanic aerosol mass, but assumes constant effective radius while computing optical depth. In order for CESM to match EVA optical depth, EVA aerosol mass needed to be scaled up by a factor of 1.67 based on a set of sensitivity experiments for the Tambora eruption in 1815. The forcing file (Dataset2.nc) is included for others to use.</p> <p>Data citation: Zhong, Y., Jahn, A., Miller, G. H., Geirsdottir, A. (2018). Asymmetric Cooling of the Atlantic and Pacific Arctic during the Past Two Millennia: A Dual Observation-Modeling Study. <em>Submitted to GRL</em>.</p>
Dataset from "Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes"
<p>These are the output land cover and land cover change raster maps from the paper, "<a href="https://doi.org/10.3390/rs10020306">Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes</a>," published in Remote Sensing journal.</p> <p>ABSTRACT. Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km<sup>2</sup>; 39% of total area), as well as total forest conversion into agricultural plantations (3,214 km<sup>2</sup>). Gross forest loss (5,133 km<sup>2</sup>) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5,471 km<sup>2</sup>) and rubber (4,025 km<sup>2</sup>) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes.</p>
NLCD_INEGI: Harmonized US-Mexico Land Cover Change Dataset, 1992/2001/2011
<p>We have taken the Uso del Suelo y Vegetacion land cover classification products for Mexico (courtesy of Mexico's Instituto Nacional de Estadistica y Geografia, or INEGI) for years 1985, 1993, 2002, 2007, and 2011 (INEGI, 2015); harmonized their classes with the classes of the Multi-Resolution Land Characteristics Consortium (MRLC) National Land Cover Database (NLCD) (Homer et al., 2015), and merged the two datasets to form a single land cover product covering the continental US and Mexico, for years 1992/3, 2001/2, and 2011. Details of processing, along with the processing scripts, are archived in GitHub in the <a href="https://github.com/tbohn/NLCD_INEGI/tree/v1.5">NLCD_INEGI</a> project (Bohn, 2019).</p> <p>Output files are ESRI ascii-format raster files, geographic projection, 0.000350884 degree cellsize. Each file contains a 1x1 degree box or "tile", named nlcd_inegi.<em>lat0</em>_<em>lat1</em>n.<em>lon0</em>_<em>lon1</em>w.asc, where <em>lat0</em> and <em>lat1</em> are the south and north boundaries of the box and <em>lon0</em> and <em>lon1</em> are the west and east boundaries (west = positive).</p> <p>This project contains the following g-zipped tar files:</p> <ul> <li>1992.tgz - land cover from 1992 (NLCD) and 1993 (INEGI)</li> <li>2001.tgz - land cover from 2001 (NLCD) and 2002 (INEGI)</li> <li>2011.tgz - land cover from 2011 (both NLCD and INEGI)</li> <li>stable_2001-2011.tgz - land cover from those pixels that had the same class in 2001 and 2011 ("stable" pixels)</li> </ul> <p>On LINUX, the contents of these files can be extracted via "tar":</p> <p>tar -xvzf 1992.tgz >& log.tar.txt</p> <p>On Windows, applications such as "7-zip" can extract the contents.</p> <p>Each of these .tgz files contain a folder with the same name but without the ".tgz". Within each of these folders is a sub-folder called "asc.clip.1deg". This folder contains the 1x1 tiles.</p>
Murgia Alta: land cover map (2013)
<p>A land cover map in "Murgia Alta" PA, for 2013, obtained by considering a Landsat intra-annual time series of NDVI spectral index computed for 27 images. A Maximum Likelihood classifier was used for a 13 classes problem. The images were preprocessed, atmospherically corrected, masked for clouds and shadow clouds cover and interpolated for the gaps filling.</p> <p>The map was produced at 30 meters spatial resolution and projected in WGS84/UTM33N.</p> <p>The Overall Accuracy (OA) of the map was: OA=83.62%±0.90%.</p> <p>The map has 14 values related as follows in LCCS-FAO taxonomy:</p> <p>Value 0 = Unclassified</p> <p>Value 1 = A12/A1.D1.E2</p> <p>Value 2 = A12/A1.D1.E1</p> <p>Value 3 = A12/A1.D2.E1</p> <p>Value 4 = A12/A2.A6</p> <p>Value 5 = A11/(A1ORA2).A7.A9</p> <p>Value 6 = A11/A1.A7.A10</p> <p>Value 7 = A11/A2.A7.A10</p> <p>Value 8 = A11/A3.A4.S3</p> <p>Value 9 = A11/A3.A4.S7</p> <p>Value 10 = A11/A3.A4.S5(LEGUMES)</p> <p>Value 11 = A11/A3.A4.S5(VEGETABLES)</p> <p>Value 12 = B15/A2.A6</p> <p>Value 13 = BURNED AREAS</p>
Land use and land cover data for Northern Coast of São Paulo State (Brazil) from 1985 to 2015
<p>Authors: Ana Beatriz Pierri Daunt and Thiago Sanna Freire Silva</p> <p>Product: Land use and land cover maps for 1985, 1990, 1995, 2000, 2005, 2010, 2015 in raster format.</p> <p>Study area: Northern Coast of São Paulo State (Brazil)</p> <p>Mapping methods: Land use and land cover were mapped using Landsat images and geographic object-based image analysis (GEOBIA), based on the Random Forests supervised algorithm processing using the “RSGISlib” library, accessible through the Python language. More information at <a href="https://www.rsgislib.org">https://www.rsgislib.org</a>. The automated classification was followed by manual correction of the land cover maps at the 1:25.000 scale. See Metadata.docx for more details and land use/cover description,</p> <p>Financial informations: This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, and by the National Council for Scientific and Technological Development (CNPq), fellowship #163870/2018-7, through the Geography Graduate Program, São Paulo State University. T.S.F. Silva acknowledges research productivity grant #310144/2015-9 from CNPq.</p> <p> </p>
FIG. 2 in Sagina hookeri Timaná, sp. nov. (Caryophyllaceae), a new endemic species for the flora of Île Amsterdam (French Southern and Antarctic Lands)
FIG. 2. — Close-up of type specimen of Sagina hookeri Timaná, sp. nov. (specimen P02434524; Rouhan et al. 1841).
FIG. 1. — A in Sagina hookeri Timaná, sp. nov. (Caryophyllaceae), a new endemic species for the flora of Île Amsterdam (French Southern and Antarctic Lands)
FIG. 1. — A, Location of Île Amsterdam in the Southern Indian Ocean; B, locality details showing the two sites known for Sagina hookeri Timaná, sp. nov. (dashed frames).
FIG. 4 in Sagina hookeri Timaná, sp. nov. (Caryophyllaceae), a new endemic species for the flora of Île Amsterdam (French Southern and Antarctic Lands)
FIG. 4. — Fruit close-up: A, fruit before dehiscence; B, fruit before dehiscence, with sepals removed, showing petals; C, mature fruit at dehiscence. Lourteig & Cour 57. Photo credits: MNHN – Germinal Rouhan, 2018. Scale bars: A, C, 500 µm; B, 1 mm.
FIG. 3 in Sagina hookeri Timaná, sp. nov. (Caryophyllaceae), a new endemic species for the flora of Île Amsterdam (French Southern and Antarctic Lands)
FIG. 3. — Living specimens and habitat of Sagina hookeri Timaná, sp. nov. in Île Amsterdam: A, habit; B, fruit close-up; C, flower close-up; D, fruiting individual; E, habitat of type specimen at Point Del Cano, in the middle of ' Terres Rouges'. Photos: MNHN – Germinal Rouhan, 2016.
Summary for policymakers of the assessment report on land degradation and restoration of the Intergovernmental SciencePolicy Platform on Biodiversity and Ecosystem Services: Figure SPM.1
<p>The purpose of Figure SPM.1 is to support the statement that land degradation occurs just about everywhere in the world (i.e. it is ‘pervasive’), takes many forms, and that examples of successful restoration are also widespread. The figure consists of a backdrop map of the world from a multiple land degradation perspective, showing the level of uncertainty between studies, overlaid with dots representing all the places specifically mentioned in the eight chapters of the main Assessment Report on Land Degradation and Restoration, including case studies of both degradation and restoration. Around the map are brief notes regarding the main forms of degradation encountered.</p>
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.