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.
2,516
datasets available to search
ShareScore release 0.9.0
Dataset results
2,516 results for “Seri”
Time-series of groundwater recharge, Tiber Riber Basin, Italy from 801 CE to the present day
<p>Groundwater, essential for water availability, sanitation, and achieving Sustainable Development Goals, is shaped by climate dynamics and complex hydrogeological structures. Here, we provide a time-series of groundwater recharge from 801 CE to the present day in the Tiber River Basin, Italy, using historical records and hydrological modelling. Groundwater drought occurred in 36% of the Medieval Climatic Anomaly (801-1249) years, 12% of the Little Ice Age (1250-1849) years, and 26% of the Modern Warming Period (1850-2020) years. Importantly, a predominant warm phase of the Atlantic Multidecadal Oscillation, aligned with solar maxima, coincided with prolonged dry spells during both the medieval and modern periods, inducing a reduction in recharge rates due to hydrological memory effects. This study enhances understanding of climate-water interactions, offering a comprehensive view of groundwater dynamics in central Mediterranean and highlighting the importance of the past for sustainable future strategies. Leveraging this understanding can address water scarcity and enhance basin resilience.</p>
Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021)
<p>The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in <a href="https://doi.org/10.1016/j.rse.2021.112831">Blickensdörfer et al. (2022)</a>.</p> <p>All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software <a href="https://force-eo.readthedocs.io/en/latest/">FORCE </a>- Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.</p> <p>The map extent covers all areas in Germany that are defined in the respective year as cropland, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). </p> <p>Version v201:<br>Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).</p> <p>Version v202:<br>Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the „Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refined. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps.</p> <p>The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.</p> <p>Class-specific accuracies for each year are provided in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. </p> <p> </p> <p><strong>References:<br></strong><br><em>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. </em><br><em>https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).</em></p> <p><em>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</em></p> <p><em>Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland <br>https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).</em></p> <p>___________________________________________________________________________<br>National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0. </p> <p>Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (<a href="https://www.agrarmonitoring-monvia.de/en/">MonViA</a>, Monitoring of biodiversity in agricultural landscapes).</p> <p>The study was financially supported by the European Environment Agency and the European Union’s Horizon Europe Research and Innovation programme under Grant Agreement No 101060423 (LAMASUS).</p>
Future electricity demand time series for European Countries from 2023 to 2100
<p>This dataset represents the future time series of electricity demand for European countries from 2023 to 2100, aligning with the findings presented in our paper 'Future Electricity Demand for Europe: Unraveling the Dynamics of the Temperature Response Function,' published in Applied Energy. To cite this dataset, please cite the published paper <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.apenergy.2024.123387" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.apenergy.2024.123387</a></p> <p>This dataset includes electricity demand data for 36 European countries, with each year being presented as a distinct .CSV file. Data for all years in each country are then compressed in a single .ZIP file. </p> <p>The column explanation is as below:</p> <ul> <li>'country_code': the country code in 2 digits</li> <li>'year': the projection year</li> <li>'month': month of the year</li> <li>'day': day of the month</li> <li>'S0_RCP26_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S0_RCP26_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S0_RCP45_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S0_RCP45_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S0_RCP85_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S0_RCP85_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S1_RCP26_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S1_RCP26_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S1_RCP45_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S1_RCP45_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S1_RCP85_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S1_RCP85_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S2_RCP26_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S2_RCP26_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S2_RCP45_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S2_RCP45_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S2_RCP85_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S2_RCP85_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S3_RCP26_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S3_RCP26_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S3_RCP45_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S3_RCP45_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S3_RCP85_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S3_RCP85_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S4_RCP26_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S4_RCP26_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S4_RCP45_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S4_RCP45_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S4_RCP85_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S4_RCP85_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> </ul>
AVHRR NDVI Time Series
<p>Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.</p>
CoUDlabs_TA_USFD_02_ANNULAR_Regueiro. Time series analysis of sewer temperatures in an annular flume to estimate sediment accumulation
<p>This dataset contains the results of the experimental campaign and how data were collected on the the <a href="https://co-udlabs.eu/">Co-UDlabs</a> Transnational Access project: <em>Temperature time series analysis for predicting sedimentation in sewer systems</em>, by Regueiro-Picallo et al. The Transnational Access project was funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101008626.</p> <p>The experiments were designed to further develop an innovative methodology for measuring sediment bed deposits in UDS based on temperature data analysis (<a href="https://doi.org/10.1039/D2EW00820C" target="_blank" rel="noopener">Regueiro-Picallo et al., 2023</a>; <a href="https://doi.org/10.1039/D3EW00825H" target="_blank" rel="noopener">Regueiro-Picallo et al., 2024</a>). Particularly, the aim of these experiments was to study the influence of the sewer hydrodynamics for estimating the sediment accumulation using temperature-based methods. Therefore, the main tasks of this project were the design of a temperature-control system for the ANNULAR flume to reproduce sewer temperature patterns, and the development of an experimental campaign including different flow, sediment depth and temperature pattern conditions. The purpose of these measurements is to prove the use of simplified systems to monitor sediment accumulation in sewer systems.</p> <p>The data are described so that others can use and reproduce.</p>
Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL: Part 2
<p>The dataset named SEBAL evapotranspiration in China (SEBAL ET) characterized the daily evapotranspiration (in millimeter) of vegetation in China from 2001 to 2018, the spatial resolution is 1 km × 1km and the temporal resolution is 1 day with the coordinate system of GCS_WGS_1984. The products were generated using Surface Energy Balance Algorithm of Land (SEBAL) and multi-sources remote sensing data, including MOD43A1 daily surface albedo, MOD11A1 daily surface temperature and MOD13 vegetation indices (obtained from NASA: https://ladsweb.modaps.eosdis.nasa.gov/search/), the meteorological data obtained from GMAO (https://gmao.gsfc.nasa.gov/research/highlights/2013-2015.php), the input variables were all aggregated of resampled to 1 km × 1km. The products were evaluated using the eight flux towers observation data for point validation and water balance method for regional validation and showed R value of 0.79 and 0.98, respectively, which indicated the products have a great performance. SEBAL ET can be used for several geoscience studies, especially for global change, water resources mangement and agricultural drought monitoring, etc.</p>
Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL: Part 1
<p>The dataset named SEBAL evapotranspiration in China (SEBAL ET) characterized the daily evapotranspiration (in millimeter) of vegetation in China from 2001 to 2018, the spatial resolution is 1 km × 1km and the temporal resolution is 1 day with the coordinate system of GCS_WGS_1984. The products were generated using Surface Energy Balance Algorithm of Land (SEBAL) and multi-sources remote sensing data, including MOD43A1 daily surface albedo, MOD11A1 daily surface temperature and MOD13 vegetation indices (obtained from NASA: https://ladsweb.modaps.eosdis.nasa.gov/search/), the meteorological data obtained from GMAO (https://gmao.gsfc.nasa.gov/research/highlights/2013-2015.php), the input variables were all aggregated of resampled to 1 km × 1km. The products were evaluated using the eight flux towers observation data for point validation and water balance method for regional validation and showed R value of 0.79 and 0.98, respectively, which indicated the products have a great performance. SEBAL ET can be used for several geoscience studies, especially for global change, water resources mangement and agricultural drought monitoring, etc.</p> <p> </p>
Figure 12. BIBE 45958 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod
Figure 12. BIBE 45958, Alamosaurus sanjuanensis. Incomplete left scapula and dorsal vertebral centrum in A, lateral, and B, medial views. Dorsal vertebral centrum is attached to medial surface of scapula. Abbreviations: acr, acromion process; acrf, acromion fossa; con, asymmetrical medial concavity; dv, dorsal vertebral centrum; gl, glenoid fossa; tu, tuberosity. Scale bar = 10 cm.
Figure 6. BIBE 45854 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod
Figure 6. BIBE 45854, Alamosaurus sanjuanensis, cervical vertebrae 6 to 14 in dorsal view. A, cervical vertebrae 14, 13 and posterior part of 12. B, anterior part of cervical vertebra 13, cervical vertebra 12 and posterior part of cervical vertebra 11. C, anterior part of cervical vertebra 12, cervical vertebra 11 and posterior part of cervical vertebra 10. D, anterior part of cervical vertebra 10, cervical vertebra 9 and posterior half of cervical vertebra 8. E, anterior part of cervical vertebra 9, cervical vertebra 8 and posterior part of cervical vertebra 7. F, anterior half of cervical vertebra 8 and cervical vertebra 7. G, anterior part of incomplete cervical vertebra 7 and cervical vertebra 6. Solid grey fill in G indicates broken bone surface. All scale bars = 50 cm.
Figure 1 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod
Figure 1. Location of Big Bend National Park in Texas, USA, shown by solid grey fill. White star within expanded outline of Big Bend National Park marks the approximate location of the BIBE 45854 excavation site within Tornillo Flat.
Figure 3. BIBE 45854 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod
Figure 3. BIBE 45854, articulated series of nine mid and posterior cervical vertebrae of a large, osteologically mature Alamosaurus sanjuanensis. Series is estimated to represent the sixth to fourteenth cervical vertebrae. A, composite photo-mosaic of the cervical series in right lateral view; identification of each vertebra indicated by C6 to C14, respectively. B, line drawing based on the photo-mosaic in A. C, line drawing in B with labels shown and vertebral fossae indicated by solid grey fill; cross-hatching represents broken bone surfaces and reconstructive material. Abbreviations: C, cervical vertebra; cdf, centrodiapophyseal fossa; clf, centrum lateral fossa; pocdf, postzygapophyseal centrodiapophyseal fossa; prcdf, prezygapophyseal centrodiapophyseal fossa; prcdf1, dorsal prezygapophyseal centrodiapophyseal fossa; prcdf2, ventral prezygapophyseal centrodiapophyseal fossa; sdf, spinodiapophyseal fossa; spof, spinopostzygapophyseal fossa; sprf, spinoprezygapophyseal fossa.
Figure 9. BIBE 45854, Alamosaurus sanjuanensis A, cervical vertebra 11 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod
Figure 9. BIBE 45854, Alamosaurus sanjuanensis A, cervical vertebra 11 in ventral view at an incomplete stage of preparation; segment of thin, right cervical rib 10 visible embedded in sediment ventral to centrum. B, interpretive line drawing of view in A; solid grey fill indicates broken bone surface; stippling indicates rock; diagonal cross-hatching indicates temporary support jacket constructed during preparation. Abbreviations: C10, posterior rom of centrum of cervical vertebra 10; C11, cervical vertebra 11; crib10, part of shaft of right cervical rib 10; crib 11, main body of left cervical rib 11; pp, parapophysis; prdl, prezygodiapophyseal lamina; tp, transverse process. Scale bar = 20 cm.
Figure 13. TMM 41541-1, Alamosaurus sanjuanensis. A in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod
Figure 13. TMM 41541-1, Alamosaurus sanjuanensis. A, incomplete posterior cervical vertebra in right lateral view. B, interpretive line drawing of image in A; solid grey fill indicates broken bone surfaces. Abbreviations: acdl, anterior centrodiapophyseal lamina; clf, centrum lateral fossa; cprl, centroprezygapophyseal lamina; dia, diapophysis; lam, lamina dividing the prezygapophyseal centrodiapophyseal fossa; pcdl, posterior centrodiapophyseal lamina; podl, postzygodiapophyseal lamina; pp, parapophysis; prcdf1, dorsal prezygapophyseal centrodiapophyseal fossa; prcdf2, ventral prezygapophyseal centrodiapophyseal fossa; prdl, prezygodiapophyseal lamina; prz, prezygapophysis. Solid line in scale bar = 10 cm.
Figure 5. BIBE 45854 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod
Figure 5. BIBE 45854, Alamosaurus sanjuanensis, cervical vertebrae in ventral view. Photos taken at various preparation stages for each vertebra. A, cervical vertebra 6. B, cervical vertebra 7 and prezygapophysis of cervical vertebra 8. C, cervical vertebra 8 and posterior rim of centrum from cervical vertebra 7; angle of photo taken in ventral slightly left lateral view. D, cervical vertebra 9. E, cervical vertebra 10 and posterior edge of centrum of cervical vertebra 9. F, cervical vertebra 11 and posterior end of cervical vertebra 10. G, line drawing of cervical vertebra 12, derived from sketch made during preparation of specimen. H, cervical vertebra 13 and posterior end of centrum of cervical vertebra 12. I, cervical vertebra 14 and posterior end of centrum of cervical vertebra 13. Abbreviations: C, part of cervical centrum; for, foramen; prz8, prezygapophysis of cervical vertebra 8. All scale bars = 20 cm.
Figure 4. BIBE 45854, Alamosaurus sanjuanensis. A, cervical vertebra 10 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod
Figure 4. BIBE 45854, Alamosaurus sanjuanensis. A, cervical vertebra 10 in dorsal view during preparation, with neural spine removed. B, interpretive line drawing of image in A. Somphospondylus internal structure is visible in broken neural spine laminae in A. Solid grey fill in B indicates broken bone surfaces. Dashed lines indicate approximate borders of anterior condyle and prezygapophyses not attached to centrum at this stage of preparation. Abbreviations: ant, anterior; crib, cervical rib; podl, postzygodiapophyseal lamina; ppr, posterior process of posterior centrodiapophyseal lamina; sdf, spinodiapophyseal fossa; spof, spinopostzygapophyseal fossa; sprf, spinoprezygapophyseal fossa; spol, spinopostzygapophyseal lamina; sprl, spinoprezygapophyseal lamina; tprl, intraprezygapophyseal lamina. Divisions on metre scale bar are 10 cm.
Figure 8. BIBE 45854 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod
Figure 8. BIBE 45854, Alamosaurus sanjuanensis. Close-up views of autapomorphic lamina dividing the prezygapophyseal centrodiapophyseal fossa into dorsal and ventral sub-fossae. A, cervical vertebra 13, left lamina and fossae in anterolateral view. B, cervical vertebra 13, right lamina and fossae in anterolateral view. C, cervical vertebra 14, centrum and arch laminae and fossae in lateral view during preparation of specimen. D, line drawing of view in C. E, cervical vertebra 14, stereophotopairs of right prezygapophyseal centrodiapophyseal fossa and dividing lamina in anterolateral view. Solid grey fill indicates broken bone surface. Abbreviations: acdl, anterior centrodiapophyseal lamina; C, cervical vertebra; cdf, centrodiapophyseal fossa; clf, centrum lateral fossa; cpol, centropostzygapophyseal lamina; cprl, centroprezygapophyseal lamina; crib, cervical rib; dia, diapophysis; lam, lamina dividing the prezygapophyseal centrodiapophyseal fossa; pcdl, posterior centrodiapophyseal lamina; pocdf, postzygapophyseal centrodiapophyseal fossa; podl, postzygodiapophyseal lamina; pp, parapophysis; prcdf1, dorsal prezygapophyseal centrodiapophyseal fossa; prcdf2, ventral prezygapophyseal centrodiapophyseal fossa; prdl, prezygodiapophyseal lamina; prz, prezygapophysis. Scale bar in A shows 10 cm divisions; scale bars in B, C and E = 10 cm.
Figure 2 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod
Figure 2. Diagrammatic skeletal drawing of Alamosaurus sanjuanensis based on composite skeleton reconstruction exhibited at the Perot Museum of Nature and Science, in Dallas, Texas. Reconstruction based on BIBE 45854, TMM 41541-1, USNM 15560 and the distal caudal series of a not-yet-named South American titanosaur. Human silhouette represents a 1.8 m tall individual. Grey-filled elements indicate parts of reconstruction based upon estimates of anatomy or modifications of other taxa.
Figure 7. BIBE 45854 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod
Figure 7. BIBE 45854, Alamosaurus sanjuanensis posterior cervical vertebrae in anterior view. A, cervical vertebra 12. B, cervical vertebra 13; security strap lashing vertebra to shipping pallet visible in photo. C, cervical vertebra 14; temporary plaster transportation base obscures ventral edges of vertebra. Photos taken during late stages of specimen preparation. Abbreviations: C, cervical vertebra, identifies parts of adjacent vertebrae in images; cprl, centroprezygapophyseal lamina; llns, lateral lamina of neural spine; nc, neural canal; poz, postzygapophysis; poz 11, postzygapophysis of cervical 11; poz 12, postzygapophysis of cervical 12; prsl, prespinal lamina; sprf, spinoprezygapophyseal fossa; sprl, spinoprezygapophyseal lamina; tprl, intraprezygapophyseal lamina. Scale bar = 50 cm.
Figure 17 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod
Figure 17. Results of the second analysis. Single resolved tree (length = 145 evolutionary steps, consistency index = 0.6966, retention index = 0.7124) generated by the second analysis based on data matrix of Gonźalez Riga & Ortiz David (2014) after Drusilasaura was deleted from the analysis, and set on a geological time scale (Gradstein et al. 2012). Thickened branch sections indicate approximate temporal range for each terminal taxon. Temporal ranges of taxa were taken from multiple sources (Borsuk-Bialynicka 1977; Upchurch et al. 2004; Weishampel et al. 2004; de Jesus Faria et al. 2015). Open circles at nodes indicate a node-defined clade name. Arcs on branches indicate a stem-defined lineage name. See Table 1 for name definitions. Lo. = Lognkosauria. Op. = Opisthocoelicaudiinae. Numbers at nodes indicate bootstrap and decay index (Bremer value), formatted as decay index/bootstrap value. A dash (-) indicates a decay index of 0 and/or bootstrap value <50%.
Figure 14 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod
Figure 14. Hypotheses for the phylogenetic position of Alamosaurus sanjuanensis in previous cladistic analyses. A, Alamosaurus as the sister taxon to Opisthocoelicaudia, or on the stem lineage Opisthocoelicaudiinae (Sereno 1998; Curry Rogers 2005; Wilson 2002; Calvo et al. 2007a; Gonźalez Riga et al. 2009; Zaher et al. 2011; Gonźalez Riga & Ortiz David 2014); B, Alamosaurus as a member of stem lineage Saltasaurinae (Salgado et al. 1997; Calvo & Gonźalez Riga 2003; Gonźalez Riga 2003; Mannion et al. 2013; Saegusa & Ikeda 2014); C, Alamosaurus as a saltasaurid in a basal polytomy with Saltasaurinae and Opisthocoelicaudia (Lacovara et al. 2014); D, Alamosaurus as a lithostrotian titanosaur outside Saltasauridae (Upchurch 1998; Upchurch et al. 2004; Carballido et al. 2011; Carballido & Sander 2014; Poropat et al. 2015). Abbreviations: Op. = Opisthocoelicaudiinae; Sa. = Saltasaurinae.
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.