five

SBC LTER: Reef: Kelp Forest Community Dynamics: Fish abundance (Reformatted to the ecocomDP Design Pattern)|生态学数据集|鱼类种群动态数据集

收藏
DataONE2021-07-21 更新2024-06-08 收录
生态学
鱼类种群动态
下载链接:
https://search.dataone.org/view/https://pasta.lternet.edu/package/metadata/eml/edi/189/2
下载链接
链接失效反馈
资源简介:
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sbc/17/35. The abstract below was extracted from the Level 0 data package and is included for context: These data describe the abundance and size of fish species as part of SBCLTER's kelp forest monitoring program to track long-term patterns in species abundance and diversity. This study began in 2000 in the Santa Barbara Channel, California, USA. The abundance and size of all taxa of resident kelp forest fish encountered along permanent transects are recorded at nine reef sites located along the mainland coast of the Santa Barbara Channel and at two sites on the north side of Santa Cruz Island. These sites reflect several oceanographic regimes in the channel and vary in distance from sources of terrestrial runoff. In these surveys, fish were counted in either a 40x2m benthic quadrat, or in the water parcel 0-2m off the bottom over the same area. The two tables in this data package include: 1) The annual benthic fish community survey which was conducted on 11 reefs once a year around late July or early August; and 2) The monthly fish survey which was conducted once a month at a subset of the sites (3 of the annual sites) The time period of data collection for the annual benthic fish community survey varied among the 11 kelp forest sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. The monthly fish survey at ABUR (Transect 1, 2, and 3), AQUE (Transect1), and MOHK (Transect 1) in 2002. The Transect 2 and 3 at ABUR were discontinued in June 2006. See Methods for more information.   The primary research objective of the Santa Barbara Coastal LTER is to investigate the importance of land and ocean processes in structuring giant kelp (Macrocystis pyrifera ) forest ecosystems. As in many temperate regions, the shallow rocky reefs in the Santa Barbara Channel, California, are dominated by giant kelp forests. Because of their close proximity to shore, kelp forests are influenced by physical and biological processes occurring on land as well as in the open ocean. SBC LTER research focuses on measuring and modeling the patterns, transport, and processing of material constituents (e.g., nutrients, carbon, sediment, organisms, and pollutants) from terrestrial watersheds and the coastal ocean to these reefs. Specifically, we are examining the effects of these material inputs on the primary production of kelp, and the population dynamics, community structure, and trophic interactions of kelp forest ecosystems.
创建时间:
2021-07-21
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4098个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

校园防欺凌 AI语音预警

                        校园防欺凌 AI语音预警系统特点1、敏感词检测   可端侧进行分析,如区域内出现风险预示词语,如骂人、霸凌、呼救等词语,接直接触发预警到中心。2、分贝强声检测    实时采集并上传分贝值,不对语音音频进行采集,尊重学生的隐私权。另外,降低常规声音(如雷声、雨声、打鼾声等)的分贝值。3、异常声检测    可独立识别音频特性及类型通过对环境内不同声音进行分析比对,确定其声源类型,区分出有风险的声音(如砸玻璃声、人员尖叫、哭声等)并自动触发报警。4、内置语音播报可自定义语音文件,随时随地进行全局广播。语音合成芯片支持多种语音模式,例如文字转语音,真人声录制,特定音效定制等。

郑州数据交易中心 收录

中国交通事故深度调查(CIDAS)数据集

交通事故深度调查数据通过采用科学系统方法现场调查中国道路上实际发生交通事故相关的道路环境、道路交通行为、车辆损坏、人员损伤信息,以探究碰撞事故中车损和人伤机理。目前已积累深度调查事故10000余例,单个案例信息包含人、车 、路和环境多维信息组成的3000多个字段。该数据集可作为深入分析中国道路交通事故工况特征,探索事故预防和损伤防护措施的关键数据源,为制定汽车安全法规和标准、完善汽车测评试验规程、

北方大数据交易中心 收录

YOLO Drone Detection Dataset

为了促进无人机检测模型的开发和评估,我们引入了一个新颖且全面的数据集,专门为训练和测试无人机检测算法而设计。该数据集来源于Kaggle上的公开数据集,包含在各种环境和摄像机视角下捕获的多样化的带注释图像。数据集包括无人机实例以及其他常见对象,以实现强大的检测和分类。

github 收录

Materials Project

材料项目是一组标有不同属性的化合物。数据集链接: MP 2018.6.1(69,239 个材料) MP 2019.4.1(133,420 个材料)

OpenDataLab 收录

LIDC-IDRI

LIDC-IDRI 数据集包含来自四位经验丰富的胸部放射科医师的病变注释。 LIDC-IDRI 包含来自 1010 名肺部患者的 1018 份低剂量肺部 CT。

OpenDataLab 收录