Scientific data

In 2017, fluid and gas samples were collected across the Costa Rican Arc. He and Ne isotopes, C isotopes as well as total organic and inorganic carbon concentrations were measured. The samples (n = 24) from 2017 are accompanied by (n = 17) samples collected in 2008, 2010 and 2012. He-isotopes ranged from arc-like (6.8 R_A) to crustal (0.5 R_A). Measured dissolved inorganic carbon (DIC) δ¹³C_VPDB values varied from 3.55 to −21.57‰, with dissolved organic carbon (DOC) following the trends of DIC. Gas phase CO₂ only occurs within ~20 km of the arc; δ¹³C_VPDB values varied from −0.84 to −5.23‰. Onsite, pH, conductivity, temperature and dissolved oxygen (DO) were measured; pH ranged from 0.9–10.0, conductivity from 200–91,900 μS/cm, temperatures from 23–89 °C and DO from 2–84%. Data were used to develop a model which suggests that ~91 ± 4.0% of carbon released from the slab/mantle beneath the Costa Rican forearc is sequestered within the crust by calcite deposition with an additional 3.3 ± 1.3% incorporated into autotrophic biomass.

Aedes aegypti and Ae. albopictus are the main vectors transmitting dengue and chikungunya viruses. Despite being pathogens of global public health importance, knowledge of their vectors’ global distribution remains patchy and sparse. A global geographic database of known occurrences of Ae. aegypti and Ae. albopictus between 1960 and 2014 was compiled. Herein we present the database, which comprises occurrence data linked to point or polygon locations, derived from peer-reviewed literature and unpublished studies including national entomological surveys and expert networks. We describe all data collection processes, as well as geo-positioning methods, database management and quality-control procedures. This is the first comprehensive global database of Ae. aegypti and Ae. albopictus occurrence, consisting of 19,930 and 22,137 geo-positioned occurrence records respectively. Both datasets can be used for a variety of mapping and spatial analyses of the vectors and, by inference, the diseases they transmit.

Automatic phylogenetic inference plays an increasingly important role in computational historical linguistics. Most pertinent work is currently based onexpert cognate judgments. This limits the scope of this approach to a small number of well-studied language families. We used machine learning techniques to compile data suitable for phylogenetic inference from the ASJP database, a collection of almost 7,000 phonetically transcribed word lists over 40 concepts, covering two thirds of the extant world-wide linguistic diversity. First, we estimatedPointwise Mutual Informationscores between sound classes using weighted sequence alignment and general-purpose optimization. From this we computed a dissimilarity matrix over all ASJP word lists. This matrix is suitable fordistance-basedphylogenetic inference. Second, we appliedcognate clusteringto the ASJP data, using supervised training of an SVM classifier on expert cognacy judgments. Third, we defined two types of binarycharacters, based on automatically inferred cognate classes and on sound-class occurrences. Several tests are reported demonstrating the suitability of these characters forcharacter-basedphylogenetic inference.

A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 10¹² satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km² and 45 km². The mean density of good-quality observations is 13 km⁻² yr⁻¹. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr⁻¹ of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

This Australian benthic data set (BENTHOZ-2015) consists of an expert-annotated set of georeferenced benthic images and associated sensor data, captured by an autonomous underwater vehicle (AUV) around Australia. This type of data is of interest to marine scientists studying benthic habitats and organisms. AUVs collect georeferenced images over an area with consistent illumination and altitude, and make it possible to generate broad scale, photo-realistic 3D maps. Marine scientists then typically spend several minutes on each of thousands of images, labeling substratum type and biota at a subset of points. Labels from four Australian research groups were combined using the CATAMI classification scheme, a hierarchical classification scheme based on taxonomy and morphology for scoring marine imagery. This data set consists of 407,968 expert labeled points from around the Australian coast, with associated images, geolocation and other sensor data. The robotic surveys that collected this data form part of Australia's Integrated Marine Observing System (IMOS) ongoing benthic monitoring program. There is reuse potential in marine science, robotics, and computer vision research.

Long-term datasets of number and size of lakes over the Tibetan Plateau (TP) are among the most critical components for better understanding the interactions among the cryosphere, hydrosphere, and atmosphere at regional and global scales. Due to the harsh environment and the scarcity of data over the TP, data accumulation and sharing become more valuable for scientists worldwide to make new discoveries in this region. This paper, for the first time, presents a comprehensive and freely available data set of lakes’ status (name, location, shape, area, perimeter, etc.) over the TP region dating back to the 1960s, including three time series, i.e., the 1960s, 2005, and 2014, derived from ground survey (the 1960s) or high-spatial-resolution satellite images from the China-Brazil Earth Resources Satellite (CBERS) (2005) and China’s newly launched GaoFen-1 (GF-1, which means high-resolution images in Chinese) satellite (2014). The data set could provide scientists with useful information for revealing environmental changes and mechanisms over the TP region.

Machine-accessible metadata file describing the reported data (ISA-Tab format)

China is the world’s top energy consumer and CO₂ emitter, accounting for 30% of global emissions. Compiling an accurate accounting of China’s CO₂ emissions is the first step in implementing reduction policies. However, no annual, officially published emissions data exist for China. The current emissions estimated by academic institutes and scholars exhibit great discrepancies. The gap between the different emissions estimates is approximately equal to the total emissions of the Russian Federation (the 4th highest emitter globally) in 2011. In this study, we constructed the time-series of CO₂ emission inventories for China and its 30 provinces. We followed the Intergovernmental Panel on Climate Change (IPCC) emissions accounting method with a territorial administrative scope. The inventories include energy-related emissions (17 fossil fuels in 47 sectors) and process-related emissions (cement production). The first version of our dataset presents emission inventories from 1997 to 2015. We will update the dataset annually. The uniformly formatted emission inventories provide data support for further emission-related research as well as emissions reduction policy-making in China.

Climate-related sea level changes in the world coastal zones result from the superposition of the global mean rise due to ocean warming and land ice melt, regional changes caused by non-uniform ocean thermal expansion and salinity changes, and by the solid Earth response to current water mass redistribution and associated gravity change, plus small-scale coastal processes (e.g., shelf currents, wind & waves changes, fresh water input from rivers, etc.). So far, satellite altimetry has provided global gridded sea level time series up to 10–15 km to the coast only, preventing estimation of sea level changes very close to the coast. Here we present a 16-year-long (June 2002 to May 2018), high-resolution (20-Hz), along-track sea level dataset at monthly interval, together with associated sea level trends, at 429 coastal sites in six regions (Northeast Atlantic, Mediterranean Sea, Western Africa, North Indian Ocean, Southeast Asia and Australia). This new coastal sea level product is based on complete reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 missions.