ALBERT

Description: This dataset presents daily / hourly raw data on atmospheric pollen count and identification in Sierra Nevada (Granada, Spain) during the period June-September of the years 2016-2022 (with the exception of 2020 due to COVID-19 pandemic). The sampling was carried out using a Hirst-type suction volumetric collector (model Lanzoni 2000, Lanzoni srl, Bologna, Italy), located in the University Hostel, Hoya de la Mora (37º, 05'N, 3º 23'W, 2500 m a.s.l.), in Sierra Nevada Natural and National Park, southeastern of the Iberian Peninsula. The sampler works permanently, 24h / day, sucking in a constant volume of air of 10 l / min., and depositing all the particular material they contain on a filter impregnating with diluted silicone. The subsequent optical microscopy analysis of the samples, following the standardized protocol of the Spanish Aerobiology Network (Galán et al., 2007), enables the quali-quantitative identification of the different pollen types at the taxonomic level of family, genus and in some cases, even species. The resulting dataset contains records of the pollen types from the most representative wind-pollinated plants of the natural vegetation of the surroundings: juniper groves, pine groves, oak groves and psychroxerophilic pasture. Data on pollen transported from other nearby or remote locations of a radius of up to 30 km around the sampler are also included, the latter in the frequent episodes of Saharan dust intrusion in the southeast of the peninsula. In order to offer greater practicality to potential users, the data set is provided in both a wide and long format. In both formats 0 values indicate not pollen count for a taxon that was being sampled that year (identified at least once time). Regarding null values, in the long format indicate technical errors, while in the wide format indicate also that no information is available for a taxon over a whole sampling year. Date/time data correspond to CET and CEST time zone. This data set provides valuable information on atmospheric pollen in a recognized plant biodiversity hotspot within the Mediterranean context, which is essential for assessing the status of endemic species highly dependent on stable environmental conditions, and their response to the impact of climate change. In this context, data can help to establish conservation and recovery plans for the species whose survival is most threatened.

Description: The SIRGAS reference frame realization (SIR17P01) covers the period from April 17, 2011 (GPS week 1632) to January 28, 2017 (GPS week 1933). It includes only weekly solutions referring to the IGS08/IGb08 reference frame. This new SIRGAS realization is aligned to the IGS14 reference frame and it is consistent with the igs14.atx ground antenna calibrations. SIR17P01 includes positions and velocities of 345 stations referring tothe IGS14, epoch 2015.0. Its estimated precision is ±1.2 mm (horizontal) and ±2.5 mm (vertical) for the station positions at the reference epoch, and ±0.7 mm/a (horizontal) and ±1.1 mm/a (vertical)for the velocities.

Description: We provide a present-day continuous surface-kinematics model for the entire Latin American and Caribbean region (VEMOS2017). VEMOS2017 was derived from pointwise station velocities inferred at 515 geodetic sites from January 1, 2014 to January 28, 2017 using a geodetic least-squares collocation approach with empirically determined covariance functions. VEMOS2017 describes the present-day deformation in Latin America and the Caribbean and continues the surface-kinematics model represented by VEMOS2015, which is valid from March 14, 2010 to April 11, 2015 (see doi:10.1594/PANGAEA.863132). VEMOS2017 covers the region from 120°W, 55°S to 35°W, 32°N with a spatial resolution of 1° x 1°. The average uncertainty of VEMOS2017 is assessed to be ±1.0 mm/a in the north-south direction and ±1.7 mm/a in the east-west direction. The maximum uncertainty values (up to ±15 mm/a) occur at the zones affected by recent strong earthquakes (in the Maule area, the northern part of Chile, Ecuador and Costa Rica). The best uncertainty values (about ±0.1 mm/a) result in the stable eastern part of the South American plate.

Description: This dataset comprises data obtained during a 6 week-long exposure experiment of mussels of the family Mytilidae to two types of microplastics, namely polymethyl methacrylate (PMMA) and polyvinylchloride (PVC) particles and two types of natural inorganic microparticles, namely diatoms and red clay. This data was obtained from May to September 2019 in five different bioregions, which are Tasmania, Chile, Japan, Cabo Verde and Israel. We recorded body condition index, byssus thread production in 24 hours, clearance rate of the food algae and respiration rate at the end of six weeks of exposure to the four different microparticles (with an exception of the groups exposed to PVC and red clay in Tasmania, which lasted 5 weeks). Mussels were exposed to three concentrations of each particle type, which were 1.5, 15 and 150 mg/l and one group to no particles at all as control.

Description: The coastal lagoon “Estero El Soldado” is a Ramsar site (no. 1982) and Natural Protected Area managed by the Sonora Ecology and Sustainable Development Commission (CEDES). In this coastal lagoon, we have installed a monitoring station that includes an eddy covariance system (EC), meteorological, and water instruments since June 2018, and we contribute to the MexFlux community. The project aims to understand better energy, water, and carbon exchange in the water-air continuum. At this site, seagrass grows during the winter-spring months (December-June). The data presented here is from 2019. Funding sources for this project at ITSON: Mexico's National Council for Science and Technology (Consejo Nacional de Ciencia y Tecnología, CONACYT) with project number CONACYT-278608. In this project, we collaborate with UNAM-Sisal and CINVESTAV-Mérida.

Description: This dataset contains the yearly maps of land use and land cover classification for Amazon biome, Brazil, from 2000 to 2019 at 250 meters of spatial resolution. We used image time series from MOD13Q1 product from MODIS (collection 6), with four bands (NDVI, EVI, near-infrared, and mid-infrared) as data input. A deep learning classification MLP network consisting of 4 hidden layers with 512 units was trained using a set of 33,052 time series of 12 known classes from both natural and anthropic land covers. Quality assessment using 5-fold cross-validation of the training samples indicates an overall accuracy of 99.22% and the following user's and producer's accuracy for the land cover classes: ProdAcc UserAcc Forest 99.80% 99.86% Pasture 98.72% 98.04% Soy_Corn 98.92% 99.06% Soy_Cotton 99.23% 99.25% Fallow_Cotton 95.74% 96.43% Millet_Cotton 100.00% 97.98% Soy_Fallow 99.76% 99.09% Savanna2 99.94% 99.47% Savanna1 98.18% 99.06% Wetlands 99.31% 98.19% Soy_Millet 76.67% 84.66% Soy_Sunflower 84.62% 78.57%

Description: Geochemistry data of a high-resolution peat core from the Past Global Changes - Carbon in Peat on EArth through Time (PAGES_C-PEAT) Project.

Description: 14C and 210Pb age determination of a high-resolution peat core from the Past Global Changes - Carbon in Peat on EArth through Time (PAGES_C-PEAT) Project.

Description: 14C and 210Pb age determination of a high-resolution peat core from the Past Global Changes - Carbon in Peat on EArth through Time (PAGES_C-PEAT) Project.