Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
23
datasets available to search
ShareScore release 0.9.0
Dataset results
23 results for “occurrence map”
Monthly maps of Warm Core Ring Occupancy and occurrences of Salinity Maximum Intrusions in the Slope Sea (1990-2019)
<p>This dataset presents two important variables across the shelfbreak in the Northwest Atlantic: (i) Warm Core Ring Occupancy in the Slope sea proximate to the shelfbreak; and (ii) locations of Salinity maximum intrusions in the shelf. Monthly fields of both of these fields together are presented for the period 1990-2019 with file name format <em>smax_ring_mm_yyyy.jpg. </em>The gray and red dots on the shelf represent locations of profiles taken from the Ecosystem Monitoring Program’s (EcoMon) hydrographic data (available from the National Centers for Environmental Information World Ocean Database accessible at <a href="http://www.ncei.noaa.gov/products/world-ocean-database">www.ncei.noaa.gov/products/world-ocean-database</a>). Red dots show locations of profiles which contained mid-depth salinity maximum intrusions, gray dots are profiles without any mid-depth salinity maximum intrusion. Profiles with intrusions were identified using the methodology of Gawarkiewicz et al., 2022. The ring occupancy was calculated from a Warm Core Ring Tracking dataset with ring tracks from 2000-2010 and 2011- 2020 available from Zenodo (<a href="https://doi.org/10.5281/zenodo.6436380">https://doi.org/10.5281/zenodo.6436380</a>, <a href="https://doi.org/10.5281/zenodo.7406675">https://doi.org/10.5281/zenodo.7406675</a>) and ring trajectories from 1978 through 1999 available from the Bedford Institute of Oceanography, Canada. To calculate the ring occupancy the region was sub-divided into 0.1 by 0.1 degree bins. Ring trajectories and approximate geographical range (calculated from the ring area, assuming the ring is a perfect circle) were overlain on this region and the days rings are present in each bin are counted in units of ring days. A ring day is the presence of one single ring in a bin during a given day. These ring day counts were converted to percentages, dividing by days in the given month and multiplying by 100. For more details see Silver et al., 2022 and Salois et al., 2023. From these figures one can see the spatial relationship between Warm Core Rings and Salinity Maximum Intrusions, with clusters of intrusions occurring in areas adjacent to high ring occupancy. </p> <p>An animation of two particular years is also presented in <em>movie_smax_ring_1993_2012.gif</em> to highlight this relationship: Low ring year (1993) leading to fewer Smax intrusion and high ring year (2012) leading to more intrusions.</p> <p> </p> <p>Gawarkiewicz, G., Fratantoni, P., Bahr, F., & Ellertson, A. (2022). Increasing Frequency of Mid‐Depth Salinity Maximum Intrusions in the Middle Atlantic Bight. <em>Journal of Geophysical Research: Oceans</em>, <em>127</em>(7), e2021JC018233. <a href="https://doi.org/10.1029/2021JC018233">https://doi.org/10.1029/2021JC018233</a></p> <p>Silver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea. <em>Journal of Geophysical Research: Oceans</em>, <em>127</em>(8), e2022JC018737. <a href="https://doi.org/10.1029/2022JC018737">https://doi.org/10.1029/2022JC018737</a> </p> <p>Salois, S. L., Hyde, K. J., Silver, A., Lowman, B. A., Gangopadhyay, A., Gawarkiewicz, G., ... & Lapp, M. (2023). Shelf break exchange processes influence the availability of the northern shortfin squid, Illex illecebrosus, in the Northwest Atlantic. <em>Fisheries Oceanography</em>. <a href="https://doi.org/10.1111/fog.12640">https://doi.org/10.1111/fog.12640</a> </p>
Data from: Area of habitat maps and validated occurrences for neotropical birds of conservation concern
<p>Understanding species distributions is essential for advancing bird conservation, especially in the rapidly changing landscapes of the Neotropics, where habitat loss and degradation are accelerating. Area of Habitat (AOH) maps offer valuable spatial tools for illustrating species distributions by highlighting potentially suitable habitats within their geographic range. In this study, we generated AOH maps for 713 neotropical bird species of conservation concern, which includes species listed as globally or nationally threatened, endemic, or with restricted ranges. Using primary biodiversity data and a structured geospatial workflow, we refined approximately 2.5 million occurrence records through a flagging process and validated 50,743 records manually.<strong> </strong>This unparalleled effort led to the creation of high-quality AOH maps, along with altitude-corrected Extent of Occurrence (EOO-DEM) and Inverse Distance Weighted (IDW) range maps. Our AOH maps significantly improved species distribution predictions for 82% of species, over EOO-DEM maps. The validated occurrences and AOH maps produced in this study have wide-ranging applications, providing a valuable basis for the development of new species distribution models and for evaluating species’ natural history, extinction risk, and habitat threats. They also support the identification of priority areas for strategic conservation investments. Importantly, these maps played a key role in systematic conservation planning analyses for the Conserva Aves initiative, which is facilitating the creation of more than 80 new protected areas across Latin America, safeguarding 2 million hectares and improving the management of an additional 2 million hectares (<a href="https://conserva-aves.org/">https://conserva-aves.org/</a>).</p>
Text-fig. 7. Geology of the Muaredzi-Muanza sector of the Cheringoma Plateau showing the location of fossil occurrences. White stars – fossiliferous localities mapped by Pickford (2012, 2013), Black stars – fossil sites mapped by Habermann et al. (2019) and d'Oliveira Coelho et al. (2021) (GPL 12 and GPL 12b correspond to the White Patch sites). TTI – Cheringoma Formation, TTs1 – Mazamba Formation, TTs1a – Palaeopan facies, TTs2 – Inhaminga Formation, Qc – Quaternary sediments. The base map is modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 7. Geology of the Muaredzi-Muanza sector of the Cheringoma Plateau showing the location of fossil occurrences. White stars – fossiliferous localities mapped by Pickford (2012, 2013), Black stars – fossil sites mapped by Habermann et al. (2019) and d'Oliveira Coelho et al. (2021) (GPL 12 and GPL 12b correspond to the White Patch sites). TTI – Cheringoma Formation, TTs1 – Mazamba Formation, TTs1a – Palaeopan facies, TTs2 – Inhaminga Formation, Qc – Quaternary sediments. The base map is modified from Google Earth.
Text-fig. 1. Locality map with the approximate extent of Clarkia Lake during Miocene times in what is today northern Idaho, USA. Black dots mark three of the localities yielding the Miocene Clarkia flora; the fossil leaf of Nymphaea sp. described here comes from locality P-33. Other symbols: Dashed lines for county boundaries; a thin dotted line for Idaho State Hwy 3; a triangle for the local peak of Bechtel Butte; and a star for the town of Clarkia. Inset: Location of the map in northern Idaho. Abbreviations: WA – Washington state, OR – Oregon, ID – Idaho, MT – Montana. Map redrawn from Ladderud et al. (2015). in First Water Lily, A Leaf Of Nymphaea Sp., From The Miocene Clarkia Flora, Northern Idaho, Usa: Occurrence, Taphonomic Observations, Floristic Implications
Text-fig. 1. Locality map with the approximate extent of Clarkia Lake during Miocene times in what is today northern Idaho, USA. Black dots mark three of the localities yielding the Miocene Clarkia flora; the fossil leaf of Nymphaea sp. described here comes from locality P-33. Other symbols: Dashed lines for county boundaries; a thin dotted line for Idaho State Hwy 3; a triangle for the local peak of Bechtel Butte; and a star for the town of Clarkia. Inset: Location of the map in northern Idaho. Abbreviations: WA – Washington state, OR – Oregon, ID – Idaho, MT – Montana. Map redrawn from Ladderud et al. (2015).
OMOP2OBO Condition Occurrence Mappings
<p><strong>OMOP2OBO Condition Occurrence Mappings V1.0</strong></p> <p>These mappings were created by the OMOP2OBO mapping algorithm (see links below). OMOP2OBO - the first health system-wide, disease-agnostic mappings between standardized clinical terminologies and eight Open Biomedical Ontology (OBO) Foundry ontologies spanning diseases, phenotypes, anatomical entities, cell types, organisms, chemicals, vaccines, and proteins. These mappings are also the first to be explicitly created using standard terminologies in the Observational Medical Outcomes (OMOP) common data model (CDM), ensuring both semantic and clinical interoperability across a space of N conditions (and N relationships curated in these ontologies).</p> <p>The mappings in this repository were created between OMOP standard condition occurrence concepts (i.e., SNOMED CT) to the Human Phenotype Ontology (HPO) and the (Mondo). The National Library of Medicine's Unified Medical Language System (UMLS) Semantic Types are first used to filter out all concepts that did not have a biological origin (accidents, injuries, external complications, and findings without clear interpretations). Then, the Semantic Type was used to prioritize the mapping of HPO concepts to findings and symptoms and Mondo to Semantic Types indicative of disease. For these OMOP domains, owl:intersectionOf (“and”), and owl:unionOf (“or”) constructors were used to construct semantically expressive mappings.</p> <p><br> <strong>Mapping Details</strong><br> Mappings included in this set were generated automatically using OMOP2OBO or through the use of a Bag-of-words embedding model using TF-IDF. Cosine similarity is used to compute similarity scores between all pairwise combinations of OMOP and OBO concepts and ancestor concepts. To improve the efficiency of this process, the algorithm searches only the top 𝑛 most similar results and keeps the top 75th percentile among all pairs with scores >= 0.25. Manually created mappings are also included.</p> <p><strong><em>Mapping Categories</em></strong></p> <ul> <li><strong>Automatic One-to-One Concept</strong>: Exact label or synonym, dbXRef, or expert validated mapping @ concept-level; 1:1</li> <li><strong>Automatic One-to-One Ancestor:</strong> Exact label or synonym, dbXRef, or expert validated mapping @ concept ancestor-level; 1:1</li> <li><strong>Automatic One-to-Many Concept: </strong>Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li><strong>Automatic One-to-Many Ancestor:</strong> Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li><strong>Manual One-to-One: </strong>Hand mapping created using expert suggested resources; 1:1</li> <li><strong>Manual One-to-Many:</strong> Hand mapping created using expert suggested resources; 1:Many</li> <li><strong>Cosine Similarity:</strong> score suggested mapping -- manually verified</li> <li><strong>UnMapped:</strong> No suitable mapping or not mapped type</li> </ul> <p><br> <em><strong>Mapping Statistics</strong></em><br> Additional statistics have been provided for the mappings and are shown in the table below. This table presents the counts of OMOP concepts by mapping category and ontology:</p> <table align="center"> <thead> <tr> <th scope="col">Mapping Category</th> <th scope="col">HPO</th> <th scope="col">Mondo</th> </tr> </thead> <tbody> <tr> <td>Automatic One-to-One Concept</td> <td>4767</td> <td>9097</td> </tr> <tr> <td>Automatic One-to-Many Concept</td> <td>150</td> <td>885</td> </tr> <tr> <td>Cosine Similarity</td> <td>1375</td> <td>667</td> </tr> <tr> <td>Automatic One-to-One Ancestor</td> <td>13595</td> <td>8911</td> </tr> <tr> <td>Automatic One-to-Many Ancestor </td> <td>38080</td> <td>40224</td> </tr> <tr> <td>Manual</td> <td>5131</td> <td>755</td> </tr> <tr> <td>Manual One-to-Many</td> <td>10326</td> <td>2835</td> </tr> <tr> <td>Unmapped</td> <td>36301</td> <td>46345</td> </tr> </tbody> </table> <p><br> <strong>Provenance and Versioning: </strong>The V1.0 deposited mappings were created by OMOP2OBO v1.0.0 on October 2022 using the OMOP Common Data Model V5.0 and OBO Foundry ontologies downloaded on September 14, 2020. </p> <p><strong>Caveats:</strong> The deposited files only contain the mappings that were generated automatically by the algorithm. The manually generated mappings will be deposited with the official preprint manuscript. Please note that these are the original mappings that were created for the preprint. They have not been updated to current versions of the ontologies. In our experience, this should result in very few errors, but we do suggest that you check the ontology concepts used against current versions of each ontology before using them.</p> <p> </p> <p><strong>Important Resources and Documentation</strong></p> <ul> <li>GitHub: <a href="https://github.com/callahantiff/OMOP2OBO">OMOP2OBO</a></li> <li>Project Wiki: <a href="https://github.com/callahantiff/OMOP2OBO/wiki">OMOP2OBO - wiki</a></li> <li>Zenodo Community: <a href="https://zenodo.org/communities/omop2obo">OMOP2OBO</a></li> <li>Preprint Manuscript: <a href="https://doi.org/10.5281/zenodo.5716421">10.5281/zenodo.5716421</a></li> </ul>
Text-fig. 1. Simplified map of the Bohemian Cretaceous Basin (BCB, the Czech Republic), showing the occurrence of Filogranula cincta (GOLDFUSS) in the Bohemian Cretaceous Basin: Předboj, Plaňany, Velim-Skalka, Kutná Hora-Karlov, Chrtníky, Novosedlice ("Weisskirchlitz"), Želenice nad Bílinou ("Schillinge") and Klokočské Loučky. in Filogranula Cincta (G , 1831), A Serpulid Worm (Polychaeta, Sedentaria, Serpulidae) From The Bohemian Cretaceous Basin
Text-fig. 1. Simplified map of the Bohemian Cretaceous Basin (BCB, the Czech Republic), showing the occurrence of Filogranula cincta (GOLDFUSS) in the Bohemian Cretaceous Basin: Předboj, Plaňany, Velim-Skalka, Kutná Hora-Karlov, Chrtníky, Novosedlice ("Weisskirchlitz"), Želenice nad Bílinou ("Schillinge") and Klokočské Loučky.
Text-fig. 2. The Prague Basin and localities of the documented scolecodont occurrences (map after Havlíček and Štorch 1990) 1. Kosov Quarry, 2. Pod lanovkou, 3. Koledník, 4. Liščí Quarry, 5. cut of the road from Loděnice to Bubovice, 6. Arethusina Gorge. in Revision Of Kettnerites Žebera, 1935 (Scolecodonta, Silurian Of The Barrandian Area, Czech Republic): Preliminary Results
Text-fig. 2. The Prague Basin and localities of the documented scolecodont occurrences (map after Havlíček and Štorch 1990) 1. Kosov Quarry, 2. Pod lanovkou, 3. Koledník, 4. Liščí Quarry, 5. cut of the road from Loděnice to Bubovice, 6. Arethusina Gorge.
Fig. 4 in Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
Fig. 4. Transmission electron micrographs showing the tegument and capsule walls of metacercariae of Cardiocephaloides longicollis. A and F illustrate the capsule wall of monocyst and multicyst metacercariae; B and I represent diagrams of monocyst and multicyst metacercariae showing the location of the following TEM micrographs. C – E Longitudinal section through the capsule wall and tegument of a monocyst. G, H, J-M Longitudinal section through the inner capsule wall and tegument of a multicyst metacercaria. D, E, J-M Detail of necrotic material accumulated on the capsule wall surrounding the metacercaria. K, Detail of inner capsule walls merging together within a multicyst. CW, capsule wall; F, fibrocyte; Gx, glycocalyx; GxF, glycocalyx filaments; ICW, inner capsule wall; M, metacercaria; M1- M3 number of metacercaria in a multicyst; MA, macrophage; Mt, metacercarial tegument; N, nucleus; NC, necrotic cells. Head arrows indicate glycocalyx filaments, asterisks (*) outside of the cyst, (**) inside of the cyst, (***) inside of the cyst when encysted with more than one capsule wall. Scale bars: D, E = 1 μm; C, G, H, J, L, M = 5 μm; K = 10 μm.
Fig. 3 in Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
Fig. 3. Occupation of the fish brain by Cardiocephaloides longicollis in fresh (A, B) and histological samples (C–F). Cardiocephaloides longicollis metacercariae within (A) the PGZ and (B) the medulla oblongata in experimentally-infected fish one month after infection. Asterisks indicate the position of metacercariae. Cardiocephaloides longicollis metacercariae are found at 6 dpi in the tectal ventricle (C), and as they grow (D, 21 dpi; E, 8 mpi; F, 15 mpi) they occupy larger part of the tectal ventricle, and also the PGZ. The representations of brains indicate the sections and positions (yellow square) where metacercariae have been found. Legend: TeO striped, cerebellum in dots and Mo squared. ICL, inferior cerebellar lobe; Mo, medulla oblongata; PGZ, periventricular gray zone of optic tectum; TeO, tectum opticum; TV, tectal ventricle. Scale bars: A = 300 μm; B = 450 μm; C–F = 200 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
Fig. 1. Distribution of metacercariae of Cardiocephaoides longicollis in the different fish brain regions, i.e., olfactory bulbs (Olf-B), olfactory lobes (Olf-L), optic lobe region (Op-L), inferior and superior cerebellar lobes (ICL, SCL), medulla oblongata (Mo), and spinal cord (SC). Metacercarial distribution in different fish species sampling locations are provided. N, number of infected brains used for metacercarial distribution; P, prevalence (based on total number of fish, see Table 1); MI, mean intensity. Note that the number of metacercariae in the brain of fish from the marine pond is based only of half brain (see Materials and methods).
Fig. 2 in Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
Fig. 2. Variation in the number of metacercariae of Cardiocephaloides longicollis encysted in different fish groups. Box plots represent the median number of metacercariae per brain region, upper and lower quartile (box) with maximum and minimum ranges (whiskers). Olfactory bulbs (Olf-B), olfactory lobes (Olf-L), optic lobe region (Op-L), inferior and superior cerebellar lobes (ICL, SCL), medulla oblongata (Mo), and spinal cord (SC). Y-axis is represented in logarithmic scale, and dots represent jittered raw data.
Text-fig. 1. Simplified map of the Bohemian Cretaceous Basin (grey) showing the occurrence of Stramentum (Stramentum) pulchellum (G. B. SOWERBY JR., 1843). in A Systematic Revision Of Stramentum (Stramentum) Pulchellum (G.B. Sowerby Jr., 1843) (Cirripedia, Thoracica, Stramentidae) From The Bohemian Cretaceous Basin, The Czech Republic
Text-fig. 1. Simplified map of the Bohemian Cretaceous Basin (grey) showing the occurrence of Stramentum (Stramentum) pulchellum (G. B. SOWERBY JR., 1843).
L4D - Probability map of giant trees occurrence (> 70 m) in the Brazilian Amazon
<p>The probability of giant trees occurrence (> 70m) based on environmental conditions. The observations higher than 70 m were filtered out and used to adjust an envelope model based on maximum entropy. In its optimization routine, the algorithm tracked how much the model gain was improved when small changes were made to each coefficient value associated with a particular variable. The resulting map of predicted occurrence of the tallest trees in the Amazon from the MaxEnt model shows that the probability of maximum tree height occurrence is highest in the northeastern Amazon (Fig. 6), more specifically in the Roraima and Guianan Lowlands. We considered 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s<sup>-1</sup>); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s<sup>-1</sup>); (5) the number of days not affected by cloud cover (clear days; in days yr<sup>-1</sup>); (6) the number of days with precipitation above 20 mm (days > 20mm; in days yr<sup>-1</sup> ); (7) the number of months with precipitation below 100 mm (months < 100mm; in months yr<sup>-1</sup> ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15) standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %). </p>
Figure 1. Map showing 3 in Occurrence and temporal variation in the size-frequency distribution of 2 bloom-forming jellyfishes, Catostylus perezi (L. Agassiz, 1862) and Rhizostoma pulmo (Cuvier, 1800), in the Indus Delta along the coast of Sindh, Pakistan
Figure 1. Map showing 3 sampling sites: Bhanbore, Mirpur Sakro, and Keti Bunder.
Potential map generated by the RF spatial model to define ideal zones for the occurrence of high density of giant trees in the Amazon
<p>The provided image is a theoretical map of giant tree density in the Amazon, generated from a spatial model based on the **Random Forest** algorithm. The map displays the spatial distribution of tree density, representing the number of trees taller than 60 meters per square kilometer (trees/km²). The model was developed using climatic, topographic, and soil variables to predict areas with higher concentrations of these giant trees.</p> <p>The areas are color-coded according to different density ranges, where:<br>- Lighter shades indicate lower tree density (≤ 5 trees/km²),<br>- Darker shades indicate higher density (up to 141 trees/km²).</p> <p>Biogeographic provinces within the Amazon biome, such as the **Guiana Shield**, **Xingu-Tapajós**, and **Roraima**, are highlighted, showing distinct density patterns across the Amazon region. This map is a valuable tool for understanding the spatial distribution of giant trees in the Amazon and plays a crucial role in conservation efforts and ecological monitoring in the region.</p>
Data from: Bedrock geological map predictions for Phanerozoic fossil occurrences
<p>This is the supplementary data repository of the <em>Paleobiology</em> paper titled <em>Bedrock Geological Map Predictions for Phanerozoic Fossil Occurrences</em>. Geographically-explicit, taxonomically resolved fossil occurrences are necessary for reconstructing macroevolutionary patterns and for testing a wide range of hypotheses in the Earth and life sciences. Heterogeneity in the spatial and temporal distribution of fossil occurrences in the Paleobiology Database (PBDB) is attributable to several different factors, including turnover among biological communities, socioeconomic disparities in the intensity of paleontological research, and geological controls on the distribution and fossil yield of sedimentary deposits. Here we use the intersection of global geologic map data from Macrostrat and fossil collections in the PBDB to assess the extent to which the potentially fossil-bearing, surface-expressed sedimentary record has yielded fossil occurrences. We find a significant and moderately strong positive correlation between geologic map area and the number of fossil occurrences. This correlation is consistent regardless of map unit age and binning protocol, except at period level; the Neogene and Quaternary have non-marine map units covering large areas and yielding fewer occurrences than expected. The sedimentary record of North America and Europe yields significantly more fossil occurrences per sedimentary area than similarly-aged deposits in most of the rest of the world. However, geographic differences in area and age of sedimentary deposits lead to regionally different expectations for fossil occurrences. Using the sampling of surface-expressed sedimentary units in North America and Europe as a predictor for what might be recoverable from the surface-expressed sedimentary deposits of other regions, we find that the rest of the globe is approximately 45% as well sampled in the PBDB. Using age and area of bedrock and sampling in North America and Europe as a basis for prediction, we estimate that over 639 thousand occurrences from outside of these regions would need to be added to the PBDB to achieve global geological parity in sampling. In general, new terrestrial fossil occurrences are expected to have the greatest impact on macroevolutionary patterns. </p>
Data set to: Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
<p>Data for the manuscript "Mapping a brain parasite: occurrence and spatial distribution in fish encephalon", doi: 10.1016/j.ijppaw.2023.03.004. Description of the distribution of metacercariae from the trematode species <em>Cardiocephaloides longicollis</em> in the brain of fish. </p>
Data from: Bedrock geological map predictions for Phanerozoic fossil occurrences
Open the record for dataset details and reuse information.
FIGURE. Map and phytophysiognomies of occurrence of species of Chamaecrista ser. Paniculatae. A. South America map highlighting the Phytogeographic Domain of Cerrado. B. Transition between Cerrado and Caatinga (Grão Mogol State Park, Minas Gerais). C. Cerrado típico (Chapada dos Veadeiros National Park, Goiás). D. Cerrado ralo (Jalapão State Park, Tocantins). E. Cerrado denso (Serra de Cladas Novas State Park, Goiás). F. Campo rupestre (Chapada dos Veadeiros National Park, Goiás). G. Cerrado rupestre (Chapada dos Veadeiros National Park, Goiás). in Taxonomic review of Chamaecrista sect. Absus subsect. Absus ser. Paniculatae (Benth.) H.S. Irwin & Barneby (Leguminosae, Caesalpinioideae)
FIGURE. Map and phytophysiognomies of occurrence of species of Chamaecrista ser. Paniculatae. A. South America map highlighting the Phytogeographic Domain of Cerrado. B. Transition between Cerrado and Caatinga (Grão Mogol State Park, Minas Gerais). C. Cerrado típico (Chapada dos Veadeiros National Park, Goiás). D. Cerrado ralo (Jalapão State Park, Tocantins). E. Cerrado denso (Serra de Cladas Novas State Park, Goiás). F. Campo rupestre (Chapada dos Veadeiros National Park, Goiás). G. Cerrado rupestre (Chapada dos Veadeiros National Park, Goiás).
Data from: Modeling and mapping the probability of occurrence of invasive wild pigs across the contiguous United States
Open the record for dataset details and reuse information.
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.