Data · dataset · 2026
Sampling Data Set of Phytoplankton in Major Rivers and Lakes of Suzhou City from 2022 to 2023
Listed in ScienceDB
Description
Dataset Name Observation Dataset of Phytoplankton Abundance and Biomass in Major Rivers and Lakes of Suzhou City in 2022–2023 (Based on: "Spatio-Temporal Changes and Driving Factors of Phytoplankton Functional Groups in Suzhou Urban Water Network", Applied Ecology, 2026, 37(5): 1675–1684) 2. Data Source and Purpose This dataset is derived from a systematic survey of the typical urban water network in Suzhou City from 2022 to 2023, covering major rivers, urban rivers and major lakes.
The data is used to depict the spatiotemporal variation characteristics of phytoplankton abundance, biomass and functional groups under strong human activity interference and controlled hydrology conditions, and to support subsequent water ecological assessment and river-lake management research. 3. Research Area and Sampling Design Region scope: Suzhou City area (approximately 30°47′–32°02′N, 119°55′–121°20′E), located in the eastern part of the Taihu Lake Basin.Water body types:Rivers: Yangtze River, Loujiang River, Xujiang River, Wangyu River, Liuhuo River, Su Dong River, Taipu River, Beijing-Hangzhou Grand Canal, etc.
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(a total of 82 sampling points);Lakes: Taihu Lake, Yangcheng Lake, Dushu Lake, Dianshan Lake, Guilei Lake, Cheng Lake, Cao Lake, You Lake, Jin Cang Lake, etc. (20 sampling points).Number of sampling points: A total of 102 fixed monitoring points were set (20 lakes, 82 rivers), and their spatial distribution is shown in Figure 1 of the paper (page 3).Sampling time and hydrological period:2022 flood season: July2022 non-flood season: October2023 non-flood season: May2023 flood season: SeptemberTheoretical sample size is 4 times × 102 points = 408.
Due to water level and dam operation, the actual effective samples obtained are 400 (77 lakes, 323 rivers).4. Data file format 2022 flood season phytoplankton data table (e.g. Phyto_2022_Flood.csv / .xlsx) The header consists of a three-row hierarchical structure, followed by the sample rows: 1) Line 1: Door-level classification The columns are labeled as "Cyanobacteria", "Green Algae", "Diatoms", "Cryptophytes", "Bryophytes", "Haptophytes", "Chrysophytes", "Dinophytes", etc., corresponding to the phyla to which the species belong. The first few columns are empty (corresponding to the metadata columns: sample number, sampling date, etc.). 2) Line 2: Genus Name (Chinese) For example: Anabaena genus, Nostoc genus, Anabaena flexilis genus, Microcystis genus, Scenedesmus genus, Spirulina genus, Anabaena circinalis genus, Chlamydomonas genus, Cryptophyceae genus, Chlorophyceae genus, Chlamydomonadae genus, Chlorella genus, Pyrrophyceae genus, Rhizoclonium genus, Cryptomonad genus, etc. Corresponding one-to-one with the "door" above, these form two levels of information: "door" and "category". 3) Row 3: Species name (in Chinese + Latin) and basic information fields The first two columns are: Sample number: such as WJ1, WJ2, etc. Sampling date: For example, 20220711, indicating the sampling date. Starting from the third column, it is the species column, with the Chinese name followed by the Latin name, for example: Microcystis spp. Copper-green microcystis (Microcystis aeruginosa) Chlorophyte (Chroococcus spp.) …… (Refer to the complete list in the table) 4) Rows 4 and below: Sample data rows Each row corresponds to one sample (one sampling point × one sampling time), for example: WJ1 20220711 0.0220 … 1.49006 1.49006 WJ2 20220717 0.0017 … 16.51310 16.51310 Each species is listed as its value in the sample (abundance or biomass, with the same unit as the original data). 5) The coordinate positions are shown in file 0. When users publish their papers or achievements, they should cite: Wei Fankai, Xu Ligang, Li Wenxuan, Song Tao.
Spatiotemporal Changes and Driving Factors of Phytoplankton Functional Groups in the Urban Water Network of Suzhou. Chinese Journal of Applied Ecology, 2026, 37(5): 1675–1684.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.cjae.00217 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Tabular 65%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.cjae.00217 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[modality].local:modality:tabular | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |