Geoscience Data Journal最新文献

Understanding the fine-scale spatial distribution of heavy metal contamination is crucial for effective environmental capacity control and targeted treatment of polluted areas. This article presents the latest dataset on the occurrence of common heavy metals in the soils of the Yangtze River Basin. The dataset was compiled by reviewing peer-reviewed literature published between 2000 and 2020. Rigorous quality control procedures were employed to ensure the accuracy of the data, including the extraction of detailed geographic locations and concentrations of heavy metals. The dataset includes 7867 records of heavy metal occurrences (Zn: 1045, Cu: 1140, Pb: 1261, Cr: 980, Cd: 1242, Ni: 649, As: 821, Hg: 729) in the soils of the Yangtze River Basin, distributed at four scale levels: province, prefecture, county, and township or finer. The results indicate that the distribution of heavy metal concentrations is relatively scattered, with higher concentrations in cities and regions with developed industry and agriculture. Cd has the highest exceedance rate (33.90%), indicating significant local contamination. Heavy metals, such as Zn at 11.96%, Ni at 12.63%, and As at 9.74%, also exceeded standard levels at certain sampling points. Cr had the lowest exceedance rate of 1.33%. This updated dataset provides essential information on the current status of heavy metals contamination in the soils of the Yangtze River Basin. It can be used for further ecological and health risk assessments and for developing strategies to remediate and prevent heavy metal contamination in the region.

Following sustained development, numerous palaeontology databases and datasets of various types have been created. However, the lack of a unified standard language to describe knowledge and unclear sharing mechanisms between different databases and datasets has limited the large-scale integration and application of paleontological data. The knowledge graph, as a key technology for semantic translation and data fusion, offers a possible solution to these challenges. Given the potential of knowledge graphs to overcome these obstacles, this paper presents a practical approach to express paleontological knowledge in a knowledge graph via the resource description framework language. By delving into the structured data associated with calcareous nannofossil biozones (the UC zone, CC zone and NC zone), we propose an ontology to describe the semantic units and logical relationships of paleontological biozones and species and then integrate relevant species records from unstructured research reports to construct a knowledge graph for calcareous nannofossils, that integrates multisource paleobiological data and knowledge reconstruction. Our focus lies in detailing the technical aspects of constructing a paleontological knowledge graph. The results demonstrate that knowledge graphs can integrate semistructured and unstructured paleontological data from various sources. This work aims to assist palaeontologists in building and utilizing knowledge graphs, serving as an initial effort for future paleontological knowledge reasoning.

This study presents a comprehensive dataset of hydrological information gathered from five key eastern basins in Jalisco, Mexico. The dataset encompasses approximately 50 limnological variables and phytoplankton counts specifically for one of these basins. Water-quality data were collected by the State Water Commission of Jalisco, adhering to the methods outlined in the Official Mexican Norm ‘NOM-127’. Monthly samplings were conducted to assess environmental variables such as pH, temperature, oxygen, nutrients and heavy metals. Monitoring has been ongoing for three basins since 2009, while the remaining two basins have been monitored since 2015 and 2020. Phytoplankton data were obtained from monthly samples taken by the University of Guadalajara between 2014 and 2019 in Lake Cajititlán. The original data were cleaned and organized using tidy data principles, with codes accessible on GitHub. To facilitate data exploration and visualization, we developed a user-friendly web application with the Shiny package in R. This application enables users to explore the dataset through summary statistics tables, time series plots and phytoplankton community analysis. The dataset is accessible on Zenodo. The presented data hold significance for environmental and water-quality assessment and applications in machine learning, neural network models, community ecology and broader environmental research. Notably, the raw data, publicly accessible from the State Water Commission of Jalisco, have been previously utilized for these purposes. This dataset offers value due to its diverse limnological and phytoplankton variables, an extended time frame of availability, a curated and streamlined accessibility process and the inclusion of a web application for intuitive exploration and visualization.

The Standardized Precipitation Evapotranspiration Index (SPEI) is a widely recognized and effective tool for monitoring meteorological droughts. However, existing SPEI datasets suffer from spatial discontinuity or coarse spatial resolution problems, which limits their applications at the local level for drought monitoring research. Therefore, we calculated the SPEI index at meteorological stations, combined with the Global Precipitation Measurement (GPM) Precipitation (Pre), Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), ERA5-Land Shortwave Radiation (SR), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) datasets and Random Forest Regression (RFR) model, developed a high spatial resolution (1 km) SPEI (HSPEI) datasets with multiple time scales in mainland China from 2001 to 2022. Compared to other SPEI datasets, the HSPEI datasets have higher spatial resolution and can effectively identify the detailed characteristics of drought in mainland China from 2001 to 2022. Overall, the HSPEI datasets can be effectively applied to the research of different droughts in China from 2001 to 2022.

Data rescuers worldwide have been trying to retrieve millions of valuable weather historical records so the observations contained in those records are preserved, searchable, analysable and machine readable. The majority of the records are written by hand, in print or cursive handwriting. Automatic transcriptions to date have not been reliable or sufficiently accurate on handwritten data so most of the historical records are transcribed manually. Recent attempts integrate artificial intelligence (AI) to automatically transcribe the historical records but the results have not been promising. Currently there is no end-to-end workflow to automatically transcribe historical handwritten tabular records into digital datasets. We propose a workflow that uses AI to automate the handwriting transcription process. The workflow is tested using the historical climate records from the Data Rescue: Archives and Weather (DRAW) project. This workflow is composed of five steps: (1) image pre-processing, (2) text line segmentation, (3) bounding boxes detection, (4) AI-enabled optical character recognition (OCR) and (5) layout re-arrangement. These steps are modular to better accommodate future advances (e.g., new image training data, better layout detectors). We hope the workflow proposed can serve as a guideline that is easily replicable and can be utilized to transcribe other historical datasets.

Closed basin accounts for about one-fifth of the global land area and is an important part of the global terrestrial carbon cycle. Due to its relatively close geographical environment and independent carbon cycling system, it is an ideal place to study regional carbon cycling. Here we present a carbon-related proxy dataset for the Shiyang River Basin in the eastern part of the Hexi Corridor. The dataset collected carbon-related indicator data for 997 sediment samples from 14 profiles, 92 surface sediment samples and 25 groundwater samples. It includes total nitrogen (TN), total organic carbon (TOC), inorganic carbon (IC), carbon-nitrogen ratio (C/N), organic carbon isotopes (δ¹³C_org), carbonate carbon isotopes (δ¹³C_carb), oxygen isotopes (δ¹⁸O) and other proxy indicator data, as well as profile and groundwater age data. These data will play an important role in studying organic carbon sinks, inorganic carbon sinks, carbon cycling processes and environmental changes in the closed basin. This dataset can be downloaded from https://doi.org/10.5281/zenodo.10252702.

Precipitation is a critical part of the global hydrological cycle that determines the distribution of water resources. It is also an essential meteorological variable used as input for hydroclimatic models and projections. However, precipitation data frequently lack complete series, especially at daily and sub-daily precipitation stations, which are usually large, bulky, and complex. To address this, gap filling is commonly used to produce complete hydrometeorological data series without missing values. Several gap-filling methods have been developed and improved. This study seeks to fill the gaps of 201 daily precipitation time series in Central Italy by localizing the approach used to generate the Serially Complete dataset for the Planet Earth (SC-Earth). This method combines the outcome of 15 strategies based on four various gap-filling techniques (quantile mapping, spatial interpolation, machine learning, and multi-strategy merging). These strategies employ the daily dataset of the neighbouring stations and the matched ERA5 data to estimate missing values at the target stations. Both raw data and the final serially complete station datasets (SCDs) underwent comprehensive quality control. Many accuracy indicators have been utilized to evaluate the performance of the strategies' estimations and the final SCD, such as Correlation Coefficient (CC), Root mean square error (RMSE), Relative bias (Bias %), and Kling-Gupta efficiency (KGE″). Multi-strategy merging strategy based on the Modified Kling-Gupta efficiency (MS₁) shows the highest performance as an individual precipitation gap-filling strategy. However, the machine learning strategy using random forest (ML₃) has the most outstanding share in the final estimates among all other strategies. In the end, the temporal–spatial performance of the final SCD is promising and depends on the pattern of the missing values (MV%). The mean values of KGE″, CC, variability (α), and bias term (β) are 0.9, 0.93, 1.064, and 4.98 × 10⁻⁷, respectively.

In this study, high-resolution CO₂ concentration data were generated for East Asia to analyse long-term changes in atmospheric CO₂ concentrations, as East Asia is an important region for understanding the global carbon cycle. Using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), atmospheric CO₂ concentrations were simulated in East Asia at a resolution of 9 km for a period of 10 years (2009–2018). The generated CO₂ concentration data include CO₂ concentrations, biogenic CO₂ concentrations, anthropogenic CO₂ concentrations, oceanic CO₂ concentrations, biospheric CO₂ uptake, biospheric CO₂ release and meteorological variables at 3-h intervals. The simulated high-resolution CO₂ concentrations, biogenic CO₂ concentrations and anthropogenic CO₂ concentrations are stored in NetCDF-4 (Network Common Data Form, version 4) format and are available for download at https://doi.org/10.7910/DVN/PJTBF3. The simulated annual mean surface CO₂ concentrations in East Asia were 391.027 ppm in 2009 and 412.949 ppm in 2018, indicating an increase of 21.922 ppm over the 10-year period with appropriate seasonal variabilities. The monthly mean CO₂ concentrations in East Asia were verified using surface CO₂ observations and satellite column-averaged CO₂ mole fraction (XCO₂) from Orbiting Carbon Observatory 2 (OCO-2). Based on surface CO₂ observations and OCO-2 XCO₂ concentrations, the average root-mean-square error (RMSE) of the simulated CO₂ concentrations in WRF-Chem was 2.474 and 0.374 ppm, respectively, which is smaller than the average RMSE of the low-resolution CarbonTracker 2019B (CT2019B) simulation. Therefore, the simulated high-resolution atmospheric CO₂ concentrations in East Asia in WRF-Chem over 10 years are reliable data that resemble the observed values and could be highly valuable in understanding the carbon cycle in East Asia.

Understanding of past droughts has been mostly shaped by meteorological data, with relatively less known about the human aspects of droughts, their socio-economic impacts, as well as choices people make in response to droughts in different environmental and socio-political contexts. The lack of data that systematically record and categorize drought impacts is an important reason for this disparity. In this paper, we present an Irish drought impacts database (IDID) containing 6094 newspaper reports and 11,351 individual impact records for the island of Ireland, covering the period 1733–2019. Relevant articles were identified through systematic searching of the Irish Newspaper Archives, and recorded impacts were categorized using a modified version of the classification scheme employed by the European drought impact inventory (EDII). Drawing on the wealth and diversity of content provided by the newspapers, the IDID database provides information on the documented temporal and geographical extent of drought events, their socio-economic and political contexts, their consequences, mitigation strategies employed and their change over time. The IDID also facilitates analysis of long-term patterns in drought incidence, individual impact categories, as well as detailed insight into the impacts of individual drought events over nearly three centuries of Ireland's history. In addition, by allowing an examination of the coherence between meteorological records and identified impacts, it advances our understanding of the influences that contemporary economic, political, environmental and societal events had on the human experience, perception and impact of droughts. This new open-access database, therefore, provides opportunities for improving understanding of drought vulnerability and is an important step in developing greater capacity to cope with and respond to future droughts on the island of Ireland.

Open research data refer to publicly available scientific information that can be accessed free of charge, usually provided by public data sources. Users must comply with specific requirements set by the institutions providing the data and always acknowledge the source of the data when processing, transmitting, storing or publishing it. One of the tasks of the BrineRIS project is the mapping of brine resources, requiring reliable data on the location of exploration facilities, environmental characteristics, brine exploitation parameters and formal and legal information. These data come from a review of various archives, databases and survey results. Initially, information on the location of the sources should be obtained, which may be available in publicly accessible databases. Next, geological and hydrogeological parameters, which can be obtained from scientific papers and reports, are useful. An important part of the project is also the analysis of legal regulations concerning water extraction and environmental protection. Therefore, data should be obtained from various sources, such as public administration, state institutions or research units. These will serve to develop the database needed to perform further analyses within the BrineRIS research project. It is therefore crucial to carefully collect, analyse and assess the usefulness of the data.