该数据集包含：（1）用于构建人工神经网络（ANN）集合模型模拟海面二甲基硫化物（DMS）浓度的匹配和分选数据；（2）ANN模型模拟的1998年至2017年全球每日海面二甲基硫化物浓度以及计算的总传输速度（Kt）和海气通量。该ANN集合模型的输入变量包括叶绿素a、海面温度（SST）、混合层深度（MLD）、硝酸盐、磷酸盐、硅酸盐、溶解氧（DO）、向下短波辐射（DSWF）和海面盐度（SSS）。模拟数据集的空间分辨率为 1°×1°。DMS 浓度、Kt 和通量的单位分别为 nmol L-1、m s-1 和 μmol S m-2 d -1 。", "ds_source": "
用于训练机器学习模型的原位 DMS 测量数据来自全球表层海水 DMS（GSSD）数据库。GSSD 数据库包含从 1972 年 3 月 11 日至 2017 年 8 月 27 日期间 266 次巡航和固定地点观测活动（https://saga.pmel.noaa.gov/dms/， 获得的共计 87801 个 DMS 测量数据。", "ds_process_way": "
基于9个环境因子开发了人工神经网络集成模型，该模型在预测DMS浓度方面表现出较高的准确性和泛化性。随后，建立了1998年至2017年期间全球海面DMS浓度和通量数据集（1°×1°），日分辨率为1°。", "ds_quality": "
数据质量良好。", "ds_acq_start_time": "1998-01-01 00:00:00", "ds_acq_end_time": "2017-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 5750860442, "ds_files_count": 3, "ds_format": "mat", "ds_space_res": "1度", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "8febe133-febd-4fec-aa6d-eb665a0d9a90.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2024-03-27 15:36:20", "last_updated": "2026-01-14 10:35:49", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6451.2024", "i18n": { "en": { "title": "A 20-year (1998-2017) global sea surface dimethyl sulfide gridded dataset with daily resolution", "ds_format": "mat", "ds_source": "
The in-situ DMS measurement data used to train machine learning models comes from the Global Surface Seawater DMS (GSSD) database. The GSSD database contains 266 cruise and fixed location observation activities from March 11, 1972 to August 27, 2017（ https://saga.pmel.noaa.gov/dms/ A total of 87801 DMS measurement data were obtained.", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
This dataset includes: (1) matching and sorting data for constructing an artificial neural network (ANN) ensemble model to simulate the concentration of dimethyl sulfide (DMS) on the sea surface; (2) The ANN model simulates the global daily sea surface concentrations of dimethyl sulfide from 1998 to 2017, as well as the calculated total transport velocity (Kt) and air sea flux. The input variables of this ANN ensemble model include chlorophyll a, sea surface temperature (SST), mixed layer depth (MLD), nitrate, phosphate, silicate, dissolved oxygen (DO), downward shortwave radiation (DSWF), and sea surface salinity (SSS). The spatial resolution of the simulated dataset is 1 °× 1 °. The units of DMS concentration, Kt, and flux are nmol L-1, ms-1, and μ mol S m-2d-1, respectively.
", "ds_time_res": "日", "ds_acq_place": "Global", "ds_space_res": "1度", "ds_projection": "", "ds_process_way": "An artificial neural network ensemble model was developed based on 9 environmental factors, which demonstrated high accuracy and generalization in predicting DMS concentration. Subsequently, a global sea surface DMS concentration and flux dataset (1 °× 1 °) was established from 1998 to 2017, with a daily resolution of 1 °.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "ds_topic_tags": [ "DMS", "海面", "二甲基硫醚" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017 ], "ds_contributors": [ { "true_name": "陈莹", "email": "yingchen@fudan.edu.cn", "work_for": "复旦大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "陈莹", "email": "yingchen@fudan.edu.cn", "work_for": "复旦大学", "country": "中国" } ], "ds_managers": [ { "true_name": "陈莹", "email": "yingchen@fudan.edu.cn", "work_for": "复旦大学", "country": "中国" } ], "category": "其他" }