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1,977 results for “2007”
MUSES Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) 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 FAPAR product at 500m spatial resolution and 8-day temporal resolution. The MUSES FAPAR product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of PAR through the entire canopy (Xiao <em>et al</em>., 2015). The MUSES FAPAR values are the instantaneous values at 10:30 am local time, close approximation of daily average PAPAR values, and they are physically consistent with the corresponding MUSES LAI values. The MUSES FAPAR product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FAPAR product in 2007. <em>Please <a href="https://zenodo.org/record/7812329#.ZDPM4XZBypo"><strong>click here</strong></a> to download the MUSES FAPAR product <strong>in 2006</strong></em>, and <em><a href="https://zenodo.org/record/7811204#.ZDKE2-ZBw2x"><strong>click here</strong></a> to download the MUSES FAPAR 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: 0.004</li> <li>Valid Range: 0 – 250</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>., Estimating the fraction of absorbed photosynthetically active radiation from the MODIS databased GLASS leaf area index product. <em>Remote Sensing of Environment</em>, 171,105-117, 2015.</li> <li>Xiao Zhiqiang, <em>et al</em>., Evaluation of Three Long Time Series for Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 56, 5509-5524, 2018.</li> <li>Zheng Y., Xiao Z., Li J., Yang H., Song J., Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products at 500 m Spatial Resolution. <em>Remote Sensing</em>, 14, 3304, 2022.</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 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 FVC product at 500m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2018 (continuously updated). It was generated from the MUSES LAI product at 500m 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. <em>Please <a href="https://zenodo.org/record/7854133#.ZEOZz3ZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2006</strong></em>, and <em><a href="https://zenodo.org/record/7851237#.ZEJ-DPxBw2x"><strong>click here</strong></a> to download the MUSES FVC 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: 0.004</li> <li>Valid Range: 0 – 250</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) Monthly Global 1km 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 1 km 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/7885495#.ZFH5BXZByUk"><strong>click here</strong></a> to download the MUSES LAI product <strong>in 2006</strong></em>, <em>and <a href="https://zenodo.org/record/7884975#.ZFCs1XZByUl"><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: 1 km</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>
MUSES Fractional Vegetation Coverage (FVC) 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 FVC product at 500 m spatial resolution and monthly 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 500 m 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. <em>Please <a href="https://zenodo.org/record/7895027#.ZFRUiXZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2006</strong></em>, <em>and <a href="https://zenodo.org/record/7889277#.ZFJi-tpBw2x"><strong>click here</strong></a> to download the MUSES FVC 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: 500 m</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</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 1km 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 FVC product at 1 km 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 1 km 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. <em>Please <a href="https://zenodo.org/record/7919484#.ZFuEFHZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2006</strong></em>, <em>and <a href="https://zenodo.org/record/7916840#.ZFssRRFBw2x"><strong>click here</strong></a> to download the MUSES FVC 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: 1 km</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</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) Monthly Global 1km 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 FVC product at 1 km spatial resolution and monthly 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 1 km 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. <em>Please <a href="https://zenodo.org/record/7932282#.ZF-DoHZBypo"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2006</strong></em>, <em>and <a href="https://zenodo.org/record/7931443#.ZF7ZmBFBw2x"><strong>click here</strong></a> to download the MUSES FVC 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: 1 km</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</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>
Encuesta Nacional a Hogares Rurales de México 2007 (ENHRUM II)
<p><strong>Encuesta Nacional a Hogares Rurales de México 2007 (ENHRUM II)</strong></p> <p>La Encuesta Nacional a Hogares Rurales de México se llevó a cabo por primera vez en el año 2003 (ENHRUM I). Como en esa ocasión, el PRECESAM (COLMEX), el REAP (Universidad de California en Davis) y universidades e instituciones estatales de México realizaron una segunda encuesta (ENHRUM II) durante los meses de enero a marzo de 2008, que recolectó información de la economía rural de 2007 (y en algunos casos de años previos). </p> <p>El objetivo principal de las dos ENHRUM es contar con información para el 2002 y 2007 de una muestra representativa de hogares rurales a partir de la obtención de datos en 80 localidades del país distribuidas en 14 estados agrupados en cinco regiones. La selección de la muestra fue diseñada por el Instituto Nacional de Estadística Geografía e Informática (INEGI).</p> <p>Los propósitos básicos de la ENHRUM II son los que siguen:</p> <ul> <li>Recabar datos sobre la economía rural y sus recursos naturales</li> <li>Extender a 2007 los datos para 2002 de la ENHRUM I</li> <li>Formar recursos humanos en el levantamiento de encuestas y en la investigación</li> <li>Elaborar estudios sobre la demografía, economía, sociedad, migración y uso de recursos naturales en el medio rural de México y los cambios experimentados durante los últimos 5 años</li> </ul> <p>Como en la ENHRUM I, hay dos encuestas: Comunitaria y a Hogares, y se aplican sus cuestionarios correspondientes a las mismas comunidades y hogares.</p> <p>Para hacer estudios sobre la economía y sociedad de los hogares rurales mexicanos con rigor estadístico, la ENHRUM se ha diseñado como una encuesta tipo panel de hogares. Esto significa que en la ENHRUM II se entrevistaron a los mismos hogares que se encuestaron en la ENHRUM I. </p>
Colombia. Justicia. Rama Judicial. Municipal. Tiempo Promedio procesal 2007 a 2015
<p>Colombia. Justicia. Rama Judicial. Municipal. Tiempo Promedio procesal 2007 a 2015</p>
One-point Integral and Turbulent wind Characteristics at the FINO1 Platform (2007-2008)
<p><strong>Dataset</strong>: One-point Integral and Turbulent wind Characteristics at the FINO1 Platform (2007-2008)</p> <p><strong>Authors:</strong> Cheynet, E.</p> <p><strong>Description:</strong></p> <p>This dataset offers a comprehensive collection of wind measurements derived from continuous sonic anemometer recordings at the FINO1 offshore platform during the years 2007 and 2008. The measurements were conducted to investigate the properties and characteristics of wind turbulence in the marine atmospheric boundary layer, especially in relation to atmospheric stability. The data provide insights that have potential implications for the design and safety of offshore structures, particularly wind turbines, as they relate to wind loads. The database includes aggregated mean flow data alongside one-point turbulent characteristics.</p> <p><strong>Tutorial:</strong></p> <p><a href="https://se.mathworks.com/matlabcentral/fileexchange/134446-offshore-wind-turbulence-characteristics-at-fino1">https://se.mathworks.com/matlabcentral/fileexchange/134446-offshore-wind-turbulence-characteristics-at-fino1</a></p> <p><strong>Content Overview:</strong></p> <ul> <li><strong>Format:</strong> Matlab (.mat) files.</li> <li><strong>Data Duration:</strong> The dataset spans two years, from 2007 to 2008.</li> <li><strong>Volume:</strong> 24 separate .mat files, approximately one file for each month of the two-year period.</li> <li><strong>Measurement Tool:</strong> Sonic anemometer positioned at the FINO1 offshore platform.</li> <li><strong>Primary Features:</strong> Aggregated mean flow and one-point turbulent characteristics.</li> </ul> <p> </p> <p><strong>Relevance and Applications:</strong></p> <ol> <li>Exploration of the validity of various turbulence models (e.g., Kaimal or Mann spectral models) in offshore environments.</li> <li>Study of the single-point auto-spectral and cross-spectral densities of wind turbulence.</li> <li>Assessment of the applicability of certain standards, such as IEC 61400-1 and IEC 61400-3, especially in the context of the North Sea wind turbines.</li> </ol> <p><strong>Literature Related to the Dataset:</strong></p> <ul> <li>Cheynet, E., Jakobsen, J. B., & Obhrai, C. (2017). Spectral characteristics of surface-layer turbulence in the North Sea. Energy Procedia, 137, 414-427. Elsevier.</li> <li>Cheynet, E., Jakobsen, J. B., & Reuder, J. (2018). Velocity spectra and coherence estimates in the marine atmospheric boundary layer. Boundary-layer meteorology, 169(3), 429-460. Springer Netherlands.</li> <li>Cheynet, E. (2019). Influence of the Measurement Height on the Vertical Coherence of Natural Wind. Lecture Notes in Civil Engineering, 27, 207-221. Springer.</li> </ul>
Dataset corresponding to the SIRs observed by the Wind spacecraft during 2007, 2008, 2018, and 2019
<p>Dataset corresponding to the Stream Interaction Regions (SIRs) observed by the Wind spacecraft during 2007, 2008, 2018, and 2019. The table consists of several columns detailing the solar wind (SW) properties and related parameters.</p> <p>In addition, we show the coronal holes (CHs) locations from which the high-speed streams originate using synoptic maps. CHs can be near the solar equator, at midlatitudes, or as low-latitude extensions of polar CH.</p>
GRACE High-Resolution Trend Mascons - Greenland Ice Sheet (2007-2015)
<p>High-resolution mascon trend solution, computed for the Greenland Ice Sheet over the time period from January 2007 and January 2015, where each mascon regression model (including the trend) has been directly estimated from the Gravity Recovery and Climate Experiment (GRACE) Level 1B data. The GAD product has not been restored, meaning the ocean mascons are consistent with the Level 2 GSM product information.</p><p>Description of columns in the dataset:</p><ol><li>Latitude center (deg)</li><li>Longitude center (deg)</li><li>Mass change trend (cm w.e. / yr)</li><li>Mass change trend uncertainty (cm w.e. / yr)</li><li>Latitude minimum (deg)</li><li>Latitude maximum (deg)</li><li>Longitude minimum (deg)</li><li>Longitude maximum (deg)</li><li>Area of mascon (sq. km)</li><li>Label of mascon</li></ol><p>When citing this dataset, please also include this citation:</p><p>Loomis, B. D., D. Felikson, T. J. Sabaka, and B. Medley (2021). High‐spatial‐resolution mass rates from GRACE and GRACE‐FO: Global and ice sheet analyses. <i> Journal of Geophysical Research: Solid Earth, </i><a href="https://doi.org/10.1029/2021JB023024">https://doi.org/10.1029/2021JB023024</a></p>
Ullevi_GasDilln_2007_1_skepp_textur.
Ullevi, Gåsinge-Dillnäs 207:1 hela delytan, Södermanland. Hällristning med skeppsfigurer och fotsulor samt skålgropar. Sörmlands museum och Opus-Heritas. 3D-SFM. Source: Objaverse 1.0 / Sketchfab
Ferney Chesters N01 (May 2007)
Rock on private farmland near Ferney Chesters to the NE of Capheaton, Northumberland. A record for cup marks in this area was first recorded on Northumberland County Council's Historic Environment Record. The site was subsequently surveyed during the NADRAP project, where this panel was referenced 'Ferney Chesters N01' and described: '...Almost pyramidal in shape with a much weathered pointed top. The sides slope in all directions at steep angles; the western sloping displays a single cup mark on a slightly raised area. It is well-rounded and very deep, measuring 0.02 m deep and 0.08 m in diameter. It has been carved into a surface now vertical but it is unknown if the motif was carved with the stone as it now is…' ERA info: https://archaeologydataservice.ac.uk/era/section/panel/overview.jsf?eraId=1619 Model created from a stereo pair captured by Joe Gibson (NADRAP Team 3) in May 2007. The imagery forms part of the full NADRAP archive deposited with Historic England & Northumberland County Council. Source: Objaverse 1.0 / Sketchfab
Ray Sunniside a (Feb 2007)
Boulder found within a cairn on Ray Fell to the W of Kirkwhelpington, Northumberland. This stone is referenced 'Ray-Sunniside a' on the Beckensall Archive (BA) and was added to ERA by NADRAP in 2008. The NADRAP team describe: "Rock photographed in Beckensall Archive was found on N side of cairn by comparison with the photo. It has a naturally grooved face but not the cup mark recorded. Another rock 1m S of cairn has two hollows (probably natural) but not considered to be rock art. The rock with single cup originally recorded by Beckensall may now be missing or hidden within the stone pile." ERA and BA info: https://archaeologydataservice.ac.uk/era/section/panel/overview.jsf?eraId=1402 Model created from 3 stereo pairs captured during NADRAP recording by Joe Gibson in February 2007. The imagery forms part of the full NADRAP archive deposited with Historic England & Northumberland County Council. Source: Objaverse 1.0 / Sketchfab
Comparison of Immune Responses to Influenza Vaccine In Adults of Different Ages (SLVP015 2007-2017)
ClinicalTrials.gov study NCT01827462. IPD Sharing: YES. Countries: 1. Publications: 15.
Temporal Trends of Thrombolysis Treatment in Chinese Acute Ischemic Stroke (AIS) Patients From 2007-2017: Analysis of China National Stroke Registry (CNSR) I, II, and III; CTP-Draft Review Performed;
ClinicalTrials.gov study NCT04290494. IPD Sharing: YES. Countries: 1. Publications: 0.
Carabids data of Pterostichus flavofemoratus and Carabus depressus in the Gran Paradiso National Park (2006, 2007, 2012, 2013)
Open the record for dataset details and reuse information.
Data from: Does tolerance allow bonobos to outperform chimpanzees on a cooperative task? A conceptual replication of Hare et al., 2007
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Redescription of the cranial skeleton of the Early Devonian (Emsian) sarcopterygian Durialepis edentatus Otto, 2007 (Dipnomorpha; Porolepiformes)
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Black-tailed deer distance sampling on Blakely Island (WA), 2007 - 2021
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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.