{ "created": "2024-07-18 11:26:57", "updated": "2026-04-28 18:53:21", "id": "4ec031e5-9c5a-4de2-95d2-bbd9fbfcb22d", "version": 6, "ds_topic": null, "title_cn": "全球海面二甲基硫逐日网格数据集(1998-2017年)", "title_en": "Global Daily Grid Dataset of Dimethyl Sulfur on the Sea Surface (1998-2017)", "ds_abstract": "

  该数据集包含:(1)匹配和分类数据，用于构建人工神经网络(ANN)集合模型来模拟海面二甲硫醚(DMS)浓度;(2)利用人工神经网络模型模拟1998 ~ 2017年全球DMS日海面浓度，计算总输送速度(Kt)和海气通量。

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  该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-2d-1。", "ds_source": "

  Zenodo网站https://zenodo.org/records/7898187", "ds_process_way": "

  利用人工神经网络集成模型模拟预测。", "ds_quality": "

  数据质量较好。", "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": 5751641543, "ds_files_count": 2, "ds_format": "mat", "ds_space_res": "1°", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "4ec031e5-9c5a-4de2-95d2-bbd9fbfcb22d.jpg", "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": [ "170.60" ], "quality_level": 3, "publish_time": "2024-07-26 17:03:17", "last_updated": "2026-01-14 11:07:47", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6674.2024", "i18n": { "en": { "title": "Global Daily Grid Dataset of Dimethyl Sulfur on the Sea Surface (1998-2017)", "ds_format": "mat", "ds_source": "

    Zenodo website https://zenodo.org/records/7898187", "ds_quality": "

    The data quality is good.", "ds_ref_way": "", "ds_abstract": "

    This dataset includes: (1) matching and classification data, used to construct an artificial neural network (ANN) ensemble model to simulate the concentration of dimethyl sulfide (DMS) in the sea surface; (2) Using an artificial neural network model to simulate the daily sea surface concentration of global DMS from 1998 to 2017, calculate the total transport velocity (Kt) and air sea flux.

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    The input variables of this 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, m · S-1, and μ mol · S m-2d-1, respectively.

", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "1°", "ds_projection": "", "ds_process_way": "

    Using artificial neural network ensemble model to simulate and predict.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "二甲基硫醚", "DMD", "海洋" ], "ds_subject_tags": [ "海洋科学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 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": "水文" }