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The unpatterned orange morph of Philippine Boiga cynodon photographed in 2016 in the University of the Philipines at Los Baños Quezon Land Grant Forest Reserve, Municipality of Siniloan, Quezon Province, southeastern Luzon Island, Photo: Rafe M. Brown. in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature
The unpatterned orange morph of Philippine Boiga cynodon photographed in 2016 in the University of the Philipines at Los Baños Quezon Land Grant Forest Reserve, Municipality of Siniloan, Quezon Province, southeastern Luzon Island, Photo: Rafe M. Brown.
Figure 7 in The relationship between Sardinella aurita landings and the environmental factors in Moroccan waters (21°-26°N)
Figure 7. – GAM smoothing curves fit- ted to effects of SST, Chl-a, and UI on S. aurita landings at latitude 22°N. The solid lines are the estimated smoother and the dashed lines represent 95% confidence intervals around the main effects. The black lines at the bottom of each plot indicate where the data values lie.
Variable vertical land motion for sea level rise projections
<h1><strong>Data for Govorcin et al., 2024: Variable vertical land motion for sea level rise projections [submitted for publication].</strong></h1> <p><strong>Disclaimer:</strong> Data is subject to change due to the review process.</p> <p><strong>Repository Contains:</strong></p> <ul> <li> <p><strong>Vertical Land Motion over California</strong></p> <ul> <li><strong>Reference:</strong> International Terrestrial Reference System, solution 2014 (ITRF2014)</li> <li><strong>Period:</strong> 2015-2023</li> <li><strong>Data Type:</strong> GeoTIFF</li> <li><strong>Unit:</strong> mm/yr</li> </ul> </li> <li> <p><strong>Vertical Land Motion (Propagated) Formal Uncertainties (Rates Std.) over California</strong></p> <ul> <li><strong>Reference:</strong> International Terrestrial Reference System, solution 2014 (ITRF2014)</li> <li><strong>Period:</strong> 2015-2023</li> <li><strong>Data Type:</strong> GeoTIFF</li> <li><strong>Unit:</strong> mm/yr</li> </ul> </li> <li> <p><strong>Vertical Land Motion Temporal Variability over California</strong></p> <ul> <li><strong>Period:</strong> 2015-2023</li> <li><strong>Data Type:</strong> GeoTIFF</li> <li><strong>Unit:</strong> mm/yr</li> </ul> </li> <li> <p><strong>Archive: Output HDF5 (Mintpy format) and GNSS Files</strong></p> <ul> <li>Includes <code>velocity.h5</code>, <code>geometry.h5</code>, <code>gnss_model</code>, <code>calibrated_velocity.h5</code>, <code>CA_3D_rates.h5</code>, and <code>temporal variability</code> per track and merged, projected to vertical. See <strong>README</strong> for more information.</li> </ul> </li> </ul> <h2>Citation:</h2> <p>If you use this data in your work, research or publication, please cite the following article:</p> <p>Govorcin, M. Bekaert, D., Hamlington, B., Sangha, S., Sweet, W. (2024). Variable Vertical Land Motion for Sea Level Rise Projections, 01 August 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-4676043/v1]</p> <h2>Acknowledgment</h2> <p>The research was conducted at the Jet Propulsion Laboratory, California Institute of Technology. This research was supported by the Observational Products for End-Users from Remote Sensing Analysis (OPERA) project (<a href="https://www.jpl.nasa.gov/go/opera" target="_blank" rel="noopener">https://www.jpl.nasa.gov/go/opera</a>), managed by the Jet Propulsion Laboratory and funded by the Satellite Needs Working Group, that is creating remote sensing products to address Earth observation needs across U.S. civilian federal agencies.</p>
FIGURE 6 in Late Holocene land vertebrate fauna from Cueva de los Nesofontes, Western Cuba: Stratigraphy, chronology, diversity, and paleoecology
FIGURE 6. Nesophontes skulls on dorsal view. 1-3, Nesophontes cf. longirostris; 3 is the holotype (AMNH 17626). 4-5, Nesophontes major. Small lines indicate discrete characters discussed in the text. Note the more elongated rostrum and wider gap between upper premolars in N. longirostris and the crowding in N. major.
FIGURE 4 in Late Holocene land vertebrate fauna from Cueva de los Nesofontes, Western Cuba: Stratigraphy, chronology, diversity, and paleoecology
FIGURE 4. Lateral profile of test pit D with source radiocarbon dates, oxygen stable isotopes, and NTAXA diversity by intervals. The fauna and multi-proxy analyses described in the text are from this excavation. The red lines indicate the major disconformities/erosional surfaces.
FIGURE 2 in Late Holocene land vertebrate fauna from Cueva de los Nesofontes, Western Cuba: Stratigraphy, chronology, diversity, and paleoecology
FIGURE 2. Cueva de los Nesofontes indicating geological, stratigraphic, and deposition features. 1, a gallery with main doline or sinkhole indicating areas of fauna collection. Smaller A-D letters pertain to the upper deposits/profiles described here. E-F are two test pits conducted on the lower level. G is the source location for the 14C-dated domestic dog mentioned in the text. 2, the upper area where the main test pits described are located: A is the test pit from 1985, B-C was dug between April 1995 and December 2003. The cross-section a-a' is the approximate source for the stratigraphic profiles illustrated in the following figures. Angles of inclination and heights above the main level are indicated. 3, cross-section (indicated b-b' on 1). Arrows indicate areas of sediment and raptor-derived pellet deposits and roost.
FIGURE 11 in Late Holocene land vertebrate fauna from Cueva de los Nesofontes, Western Cuba: Stratigraphy, chronology, diversity, and paleoecology
FIGURE 11. Plant, invertebrate, and reptile remains. 1-3, Anolis cf. chamaleolis (no. 606), scale bar 10 mm (Level I). 4, unidentified insect extremity ~1 mm (Level IV). 5, photid fly pupa, scale 10 mm (Level IV). 6, fungus spore (?), scale ~50μm. 7, plant seeds, scale 5 mm (All from level II and III). 8-9, seeds, scale 5 mm. 10, leaf fragment, scale ~50 μm (Level II). 11, plant spores, likely a conifer, Pinus? (Level IV), scale ~50μm.
FIGURE 9. Nesophontes left hemimandibles 1-2 in Late Holocene land vertebrate fauna from Cueva de los Nesofontes, Western Cuba: Stratigraphy, chronology, diversity, and paleoecology
FIGURE 9. Nesophontes left hemimandibles 1-2, Nesophontes cf. longirostris. 3, Nesophontes major (image inverted).
FIGURE 13 in Late Holocene land vertebrate fauna from Cueva de los Nesofontes, Western Cuba: Stratigraphy, chronology, diversity, and paleoecology
FIGURE 13. Rarefaction curve of Cueva de los Nesofontes doline test pit D (Levels I–IV) in relation to other Cuban deposits. A, Cueva GEDA, Pinar del Río; B, Cuevas Blancas, Mayabeque; D, The Desmodus deposit described in Orihuela (2010). G, from Gato Jíbaro archaeological deposit described in Orihuela and Tejedor (2012). See text for discussion.
FIGURE 1 in Late Holocene land vertebrate fauna from Cueva de los Nesofontes, Western Cuba: Stratigraphy, chronology, diversity, and paleoecology
FIGURE 1. Location of Loma del Palenque and Cueva de los Nesofontes in northwestern Cuba. 1, Landsat image of the Mayabeque-Matanzas region, indicating the area of the Loma del Palenque (Palenque Hill). 2, Satellite image of Palenque Hill. The asterisks (*) indicate a flat scarp at ~260 m where red-clay soils have formed, are the main source of the allochthonous sediment inside the cave. 3, Map of the Cuban archipelago, indicating the localities mentioned in the text: 1, Cueva El Abrón, GEDA, and Mono Fósil, Pinar del Río; 2, Cueva de Paredones, Artemisa; 3, Cueva del Túnel, Mayabeque; 4, Cuevas Blancas, Mayabeque; 5, Cueva del Gato Jíbaro, Matanzas; 6, Cueva Calero, Matanzas; 7, Breas de San Felipe, and Cuevas de Hato Nuevo, Matanzas; 8, Cueva de los Masones and Jagüey, Trinidad, Las Villas; and 9, Cueva del Indio, Daiquirí, Santiago de Cuba.
FIGURE 10. Bats. 1 in Late Holocene land vertebrate fauna from Cueva de los Nesofontes, Western Cuba: Stratigraphy, chronology, diversity, and paleoecology
FIGURE 10. Bats. 1, left hemimandible of Artibeus anthonyi (no, 1663, lower Level III). 2, ventral view of Phyllops vetus incomplete skull (no. 37, Level II). Both specimens were radiocarbon-dated (14C). 3, four hemimandibles of Antrozous koopmani (no. 20, 75, 1429, 1430 from Levels II and IV, also 14C dated). These specimens were also dated, helping corroborate the chronology discussed. The scale bar equals 10 mm.
FIGURE 7 in Late Holocene land vertebrate fauna from Cueva de los Nesofontes, Western Cuba: Stratigraphy, chronology, diversity, and paleoecology
FIGURE 7. Nesophontes skulls on ventral view. 1-3, Nesophontes cf. longirostris, 3 is the holotype (AMNH 17626). 4-5, Nesophontes major. Small lines indicate discrete characters discussed in the text.
FIGURE 14 in Late Holocene land vertebrate fauna from Cueva de los Nesofontes, Western Cuba: Stratigraphy, chronology, diversity, and paleoecology
FIGURE 14. Carbon stable isotopes signals from Nesophontes (filled square) and Artibeus spp. (open and closed circles), analyzed from bone collagen) at shown intervals.
Рис. 6. Титульный лист монографии Л.И. Шренка «Mollusken des Amur-Landes und des Nordjapanischen Meeres» (1867). in Leopold von Schrenck and his contribution to malacology: on the 190 birthday
Рис. 6. Титульный лист монографии Л.И. Шренка «Mollusken des Amur-Landes und des Nordjapanischen Meeres» (1867).
Fig. 1 in Land management and biodiversity maintenance: a case study in grasslands in the Coastal Plain of Rio Grande do Sul
Fig. 1. Principal Coordinates Analysis of four grassland with different historical use, described for 21 variables and 80 SU. ● = GSB, ○ = GRC, □ = GR1, ◊ = GR2, Aaff = Axonopus affinis, Ppum = Paspalum pumilum.
Figure 1 in The influence of land use-impacted tributaries on water quality and phytoplankton in the Mooi River, North West Province, South Africa
Figure 1: Catchment area of the Mooi River from its source to the confluence with the Vaal River, showing the position of sampling sites in the main stream (1–8) and in the tributaries (WFS, GM and WS). Different types of land use activities in the catchment are also indicated on the map.
Figure 3 in The influence of land use-impacted tributaries on water quality and phytoplankton in the Mooi River, North West Province, South Africa
Figure 3: Canonical Correspondence Analysis (CCA) of the physico-chemical environmental variables, natural log of the phytoplankton data, and the different sites located in the Mooi River for 2015.
Dataset for "Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions"
<p>This dataset is a part of the paper "Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions", accepted for publication in the Journal of Advances in Modeling Earth Systems (JAMES).</p> <h2>Contents</h2> <p>This dataset includes:</p> <ul> <li><strong>lhs-gen-190.csv</strong>: Training input LHS samples generated by <code>lhsgen.py</code>.</li> <li><strong>lhs-gen-50-test.csv</strong>: Test input LHS samples generated by <code>lhsgen.py</code>.</li> <li><strong>190-elm-samples.csv</strong>: Training input perturbed parameter samples for performing ELM simulations.</li> <li><strong>50-elm-test-samples.csv</strong>: Test input perturbed parameter samples for performing ELM simulations.</li> <li><strong>train_CH-CHA.csv</strong>: Contains the five ELM simulation output flux values for 240 samples (190 train + 50 test).</li> <li><strong>lhsgen.py</strong>: Script for generating Latin Hypercube Samples.</li> <li><strong>gpr-fit-new.py</strong>: Script for fitting Gaussian Process Regression (GPR) models.</li> <li><strong>sobol-new.py</strong>: Script for performing Sobol sensitivity analysis.</li> </ul> <h2>Usage</h2> <ol> <li><strong>lhsgen.py</strong>: <ul> <li>Use this script to generate the Latin Hypercube Samples for parameter sampling.</li> </ul> </li> <li><strong>gpr-fit-new.py</strong>: <ul> <li>This script fits GPR models using the training samples provided in <code>lhs-gen-190.csv</code>.</li> <li>It tests the models using the input testing samples in <code>lhs-gen-50-test.csv</code>.</li> <li>The fitted GPR models are stored as <code>.joblib</code> files in the <code>gpr_models</code> directory.</li> <li>Corresponding cross-validation and R-squared values are stored in <code>.xlsx</code> files.</li> </ul> </li> <li><strong>sobol-new.py</strong>: <ul> <li>This script performs Sobol sensitivity analysis using the fitted GPR models by reading the .joblib files.</li> <li>The Sobol indices are written to <code>.xlsx</code> files in the <code>results</code> directory.</li> </ul> </li> </ol>
How much energy can giant reed and Miscanthus produce in marginal lands across Italy? A modelling solution under current and future scenarios
<p>This data were presented in the research paper “How much energy can giant reed and Miscanthus produce in marginal lands across Italy? A modelling solution under current and future scenarios”, currently accepted in the journal Global Change Biology Bioenergy (https://onlinelibrary.wiley.com/journal/17571707).<br>The study delivers a model-based evaluation of how much energy, in the form of biomethane and bioethanol, can be produced by giant reed and Miscanthus across Italy in 2000, 2055 and 2085. Marginal lands were defined as low profitable non-irrigated lands, without mechanization and/or nature conservation limitations. Our findings offer an estimation of achievable energy yields and related stability under current/future climate, identifying critical spots and opportunities at province and regional level across Italy.<br>This work was conducted by the Council for Agricultural Research and Economics and supported by the Italian Ministry of Agricultural, Food and Forestry Policies (MiPAAF) under i) the AGROENER project (D.D. n. 26329, April 1, 2016, http://agroener.crea.gov.it/) and ii) the AgriDigit-Agromodelli project (DM n. 36502 of 20/12/2018, https://www.progettoagridigit.it/il-progetto).</p> <p><br>The database used was split in two main datasets, one for the national case study and one for the provincial case study (Bologna province).<br>The national dataset consists of:<br>1) a gridded shape file (Marginal_Suitable_Areas_National.shp; 500 x 500 m resolution) including marginal lands suitable for Miscanthus and giant reed cultivation across Italy (code_nod field), together with related geographic coordinates;<br>2) a csv file (Results_National.csv) reporting the values of key output variables for each of the marginal lands considered. Output variables are:<br>a. USDA soil texture classification: 1= Loamy, 2=Sandy−loam, 3=Silty−loam, 4= Clay−loam, 5= Sandy−clay−loam, 6=Silty−clay−loam, 7=Loamy−sand, 8=Sandy−clay, 9=Silty−clay, 10=Silty, 11=Clay, 12=Heavy−clay, 13=Sandy.<br>b. soil organic carbon (SOC) classification: SOC≤1.5%=low, 1.5%<SOC≤3%,=medium, otherwise=high;<br>c. maximum soil depth (depth) classification: depth≤50 cm=shallow, otherwise=deep;<br>d. absolute values of aboveground biomass (AGB, Mg ha-1) and energy yields (Giga J ha-1) obtainable from bioethanol (ETA) and biomethane (MET) energy carriers simulated for giant reed (GR) and Miscanthus (MI) in the current scenario;<br>e. minimum (Mn) and maximum (Mx) AGB percentage (%) variations (compared to the baseline) estimated in 2055 (55) and 2085 (85) for RCP 4.5 (4.5) and RCP 8.5 (8.5) scenarios;<br>f. potentially assignable marginal lands to Miscanthus (2) and giant reed (1) crop species in Italy based on attainable energy yields under current (C_Base) and future (2085) time slices, considering the more pessimistic (C_8.5_85_MIN) and optimistic (C_4.5_85_MAX) AGB projection for both crops.</p> <p><br>The provincial dataset (case study in the Bologna province) consists of:<br>1) a gridded shape file (Marginal_Suitable_Areas_Provincial.shp, 500 x 500 m resolution) including marginal lands suitable for Miscanthus and giant reed cultivation across the Bologna province (code_nod field), together with related geographic coordinates;<br>2) a csv file (Results_Provincial.csv) reporting the values of key output variables for each of the marginal lands considered. Output variables are:<br>a. absolute values of simulated energy (EN, Giga J ha-1) from bioethanol (ETA) and biomethane (MET) for giant reed (GR) and Miscanthus (MI) in 1995,<br>b. energy percentage variations (compared to the baseline) estimated in 2085 for more optimistic (EN_Mx, i.e., RCP 4.5_max) and pessimistic (EN_Mn, i.e., RCP 8.5_min) projections for giant reed (GR) and Miscanthus (MI) and<br>c. coefficients of variations (CV, %) computed for the whole 30-year period centred on 1995 (B) and 2085 for RCP 4.5_max (CV_Mx) and RCP 8.5_min (CV_Mn) for giant reed (GR) and Miscanthus (MI) in the Bologna province.</p>
Fig; 16 - MSNM i28026. Straight asymmetric wide trackway with an interior very light possible shell drag trace, diagnostic of fossil and extant land hermit crab walking traces (?Coenobichnus) (× 0.3). in Anomuran and brachyuran trackways and resting trace from the Pliocene of Valduggia (Piedmont, NW Italy): environmental, behavioural, and taphonomic implications
Fig; 16 - MSNM i28026. Straight asymmetric wide trackway with an interior very light possible shell drag trace, diagnostic of fossil and extant land hermit crab walking traces (?Coenobichnus) (× 0.3).
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.