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1,977 results for “2007”
Steinbauer and Weir, 2007
Steinbauer, M.J. and Weir, T.A. (2007), Summer activity patterns of nocturnal Scarabaeoidea (Coleoptera) of the southern tablelands of New South Wales. Australian Journal of Entomology, 46: 7-16. <p></p>https://doi.org/10.1111/j.1440-6055.2007.00579.x<p></p>
Greven, 2007
Greven, H. 2007. Comments on the eyes of tardigrades. Arthropod Structure & Development, Volume 36, Issue 4, Pages 401-407. <p></p>https://doi.org/10.1016/j.asd.2007.06.003 <p></p>https://www.academia.edu/12971932/Comments_on_the_eyes_of_tardigrades<p></p>
Renous et al, 2007
Renous, S., de Lapparent de Broin, F., Depecker, M., Davenport, J., Bels, V. 2007. Evolution of Locomotion in Aquatic Turtles. In book: Biology of Turtles, J. Wyneken, M. H. Godfrey & V. Bels (Eds) CRC Press, Taylor &Francis Group, Boca Raton (FL) USA. <p></p>https://www.researchgate.net/publication/256843745_Evolution_of_Locomotion_in_Aquatic_Turtles<p></p>
Sundberg and Gibson 2007
Sundberg P., Gibson R. (2007) Global diversity of nemerteans (Nemertea) in freshwater. In: Balian E.V., Lévêque C., Segers H., Martens K. (eds) Freshwater Animal Diversity Assessment. Developments in Hydrobiology, vol 198. Springer, Dordrecht. <p></p>https://doi.org/10.1007/978-1-4020-8259-7_7<p></p>
Baumiller and Messing, 2007
Baumiller, T.K, Messing, C.G. 2007. Stalked Crinoid Locomotion, and its Ecological and Evolutionary Implications. Palaeontologia Electronica, 10(1):1-10. <p></p>https://nsuworks.nova.edu/occ_facarticles/89/<p></p>
McLay, 2007
McLay, C. 2007. New crabs from hydrothermal vents of the Kermadec Ridge submarine volcanoes, New Zealand: Gandalfus gen. nov. (Bythograeidae) and Xenograpsus (Varunidae) (Decapoda: Brachyura). Zootaxa 1524: 1–22. <p></p>http://dx.doi.org/10.11646/zootaxa.1524.1.1 <p></p>https://research.nhm.org/pdfs/27742/27742.pdf<p></p>
Hunt, 2007: Hunt 2007
GENE HUNT. 2007. "MORPHOLOGY, ONTOGENY, AND PHYLOGENETICS OF THE GENUS POSEIDONAMICUS (OSTRACODA: THAEROCYTHERINAE)," Journal of Paleontology, 81(4), 607-631, (1 July 2007). <p></p>https://doi.org/10.1666/pleo0022-3360(2007)081[0607:MOAPOT]2.0.CO;2<p></p>
Monthly averaged lightning and trace gases data extracted from EMAC simulations (2007, T42L90MA resolution)
<pre>About Dataset Monthly averaged lightning and trace gases data extracted from EMAC simulations (2007, T42L90MA resolution) Authors: Francisco J. Pérez-Invernon, Francisco J. Gordillo-Vázquez, Heidi Huntrieser, Patrick Jöckel and Eric J. Bucsela Description of the data CTR simulations: CTR_*.nc files LNOfs simulation: LNOfs_*.nc files *tr_*.nc: Monthly averaged trace gases *lnox*.nc: Monthly averaged lightning data<br>*ECHAM5*.nc: Monthly averaged dynamical variables<br>*grid_def*.nc: Monthly averaged grid variables<br>*tropop*.nc: Monthly averaged tropospheric variables </pre> <pre>File format: netcdf</pre> <p> </p>
Zeppelin_BC_FLEXPART_2007
<p>FLEXPART sensitivities at three vertical levels for twenty day simulations backward in time. <br> The Flexpart BC tracer model was used for simulating backwards the light-absorbing aerosol arriving at Zeppelin during 2007.<br> </p>
Figure 1 in Review of Carelli, A., and M.L. Monné, 2015. Taxonomic revision of Phygopoda Thomson, 1864 and Pseudophygopoda Tavakilian & Peñaherrera-Leiva, 2007 (Insecta: Coleoptera: Cerambycidae: Cerambycinae)
Figure 1. Neophygopoda tibialis male, dorsal habitus.
Figure 1 in Fecundity, embryo size and embryo loss in the estuarine shrimp Salmoneus carvachoi Anker, 2007 (Crustacea: Alpheidae) from a tidal mudflat in northeastern Brazil
Figure 1. Measurements of morphological characters of Salmoneus carvachoi obtained from the Paripe River estuary, Pernambuco, northeastern Brazil, in December 2016 and February 2017. CL: Carapace Length; PW: Pleura Width; PH: Pleura Height and SW: Sternite Width.
Figure 3 in Fecundity, embryo size and embryo loss in the estuarine shrimp Salmoneus carvachoi Anker, 2007 (Crustacea: Alpheidae) from a tidal mudflat in northeastern Brazil
Figure 3. Comparison of mean embryo volume at the initial (I), intermediate (II), and final stage of development in Salmoneus carvachoi obtained from the Paripe River estuary, Pernambuco, northeastern Brazil, in December 2016 and February 2017.
Figure 2 in Fecundity, embryo size and embryo loss in the estuarine shrimp Salmoneus carvachoi Anker, 2007 (Crustacea: Alpheidae) from a tidal mudflat in northeastern Brazil
Figure 2. Correlations between fecundity and carapace length (A), width of the pleura of the second abdominal somite (B), sternite width measured at the level of the second abdominal somite (C), and pleura height of the second abdominal somite (D). Specimens of Salmoneus carvachoi were obtained from the Paripe River estuary, Pernambuco, northeastern Brazil, in December 2016 and February 2017.
Fig 2 in Abstracts of the Immature Beetles Meeting 2007
Fig 2: Michael Ivie during his lecture on the larval stages of the Omethidae.
Fig. 1 in A new species of the genus Indochinamon Yeo & Ng, 2007 (Crustacea: Brachyura: Potamoidea: Potamidae) from northern Vietnam
Fig. 1. Collection site of Indochinamon chuahuong, new species.
Data from: Does tolerance allow bonobos to outperform chimpanzees on a cooperative task? A conceptual replication of Hare et al., 2007
<p><span>Across various taxa, social tolerance is thought to facilitate cooperation, and many species are treated as having species-specific patterns of social tolerance. Yet studies that assess wild and captive bonobos and chimpanzees result in contrasting findings. By replicating a cornerstone experimental study on tolerance and cooperation in bonobos and chimpanzees (Hare et al., 2007</span><span> <em>Cur. Biol</em>. </span><span>17</span><span>, 619–623</span><span>), we aim to understand whether current discrepant findings may result from noise, methodological differences, or behavioural variability. We tested bonobos and chimpanzees housed at the same facility in a co-feeding and cooperation task. Food was placed on dishes located on both ends or in the middle of a platform. In the co-feeding task, the tray was simply made available to the ape duos, whereas in the cooperation task the apes had to simultaneously pull at both ends of a rope attached to the platform to retrieve the food. In contrast to the published findings, b</span><span>onobos and chimpanzees co-fed to a similar degree, indicating a similar level of tolerance. However, bonobos cooperated more than chimpanzees when the food was monopolizable, which replicates the original study. Our findings call into question the interpretation that at the species level bonobos cooperate to a higher degree because they are inherently more tolerant.</span></p>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2007 (001–177)
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2007 (001–177). <em>Please <a href="https://zenodo.org/record/7546696#.Y8jVokFBw2w"><strong><em>click</em> here</strong></a> to download the MUSES FVC product <strong>in 2006 (185–361)</strong></em>, and <em><a href="http://zenodo.org/record/7540561#.Y8c995hBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2007 (185–361</strong></em><em><strong>)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2007 (001–177)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2007 (185–361)
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2007 (185–361). <em>Please <a href="https://zenodo.org/record/7545878#.Y8e1zHZByUk"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2007 (001–177)</strong> and <em><a href="https://zenodo.org/record/7539466#.Y8Uex3ZByUk"><strong>click here</strong></a> to download the MUSES FVC produ</em>ct <strong>in 2008 (001–177)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2007 (185–361)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Leaf Area Index (LAI) 8-Day Global 500m SIN Grid in 2007
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global LAI product at 500m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES LAI product in 2007. <em>Please <a href="https://zenodo.org/record/7605252#.Y98DG3ZByUk"><strong>click here</strong></a> to download the MUSES LAI product <strong>in 2006</strong></em><strong>, </strong>and <em><a href="https://zenodo.org/record/7607281#.Y-BpU3ZByUk"><strong>click here</strong></a> to download the MUSES LAI product <strong>in 2008</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2007</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Leaf Area Index (LAI) Monthly Global 500m SIN Grid in 2007
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global LAI product at 500m spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES LAI product in 2007. <em>Please <a href="https://zenodo.org/record/7749782#.ZBbVzXZByUk"><strong>click here</strong></a> to download the MUSES LAI product <strong>in 2006</strong></em><strong>, </strong>and <em><a href="https://zenodo.org/record/7748732#.ZBZRlsJBw2x"><strong>click here</strong></a> to download the MUSES LAI product <strong>in 2008</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2007</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</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.