data deposition in repositories

英文版DMP模板及详细说明➢Instructions and footnotes in blue must not appear in the text.➢For options [in square brackets]: the option that applies must be chosen.➢For fields in [grey in square brackets] (even if they are part of an option as specified in the previous item): enter the appropriate data.Structure of the templateThe template is a set of questions that you should answer with a level of detail appropriate to the project.It is not required to provide detailed answers to all the questions in the first version of the DMP that needs to be submitted by month 6 of the project. Rather, the DMP is intended to be a living document in which information can be made available on a finer level of granularity through updates as the implementation of the project progresses and when significant changes occur. Therefore, DMPs should have a clear version number and include a timetable for updates. As a minimum, the DMP should be updated in the context of the periodic evaluation/assessment of the project. If there are no other periodic reviews envisaged within the grant agreement, an update needs to be made in time for the final review at the latest.In the following the main sections to be covered by the DMP are outlined. At the end of the document, Table 1 contains a summary of these elements in bullet form.This template itself may be updated as the policy evolves.Project1 Number: [insert project reference number]Project Acronym: [insert acronym]Project title: [insert project title]DATA MANAGEMENT PLAN1The term ‘project’ used in this template equates to an ‘action’ in certain other Horizon 2020 documentation1. Data SummaryWhat is the purpose of the data collection/generation and its relation to the objectives of the project?What types and formats of data will the project generate/collect?Will you re-use any existing data and how?What is the origin of the data?What is the expected size of the data?To whom might it be useful ('data utility')?2. FAIR data2. 1. Making data findable, including provisions for metadataAre the data produced and/or used in the project discoverable with metadata, identifiable and locatable by means of a standard identification mechanism (e.g. persistent and unique identifiers such as Digital Object Identifiers)? What naming conventions do you follow?Will search keywords be provided that optimize possibilities for re-use?Do you provide clear version numbers?What metadata will be created? In case metadata standards do not exist in your discipline, please outline what type of metadata will be created and how.2.2. Making data openly accessibleWhich data produced and/or used in the project will be made openly available as the default? If certain datasets cannot be shared (or need to be shared under restrictions), explain why, clearly separating legal and contractual reasons from voluntary restrictions.Note that in multi-beneficiary projects it is also possible for specific beneficiaries to keep their data closed if relevant provisions are made in the consortium agreement and are in line with the reasons for opting out.How will the data be made accessible (e.g. by deposition in a repository)?What methods or software tools are needed to access the data?Is documentation about the software needed to access the data included?Is it possible to include the relevant software (e.g. in open source code)?Where will the data and associated metadata, documentation and code be deposited? Preference should be given to certified repositories which support open access where possible.Have you explored appropriate arrangements with the identified repository?If there are restrictions on use, how will access be provided?Is there a need for a data access committee?Are there well described conditions for access (i.e. a machine readable license)?How will the identity of the person accessing the data be ascertained?2.3. Making data interoperableAre the data produced in the project interoperable, that is allowing data exchange and re-use between researchers, institutions, organisations, countries, etc. (i.e. adhering to standards for formats, as much as possible compliant with available (open) software applications, and in particular facilitating re-combinations with different datasets from different origins)?What data and metadata vocabularies, standards or methodologies will you follow to make your data interoperable?Will you be using standard vocabularies for all data types present in your data set, to allow inter-disciplinary interoperability?In case it is unavoidable that you use uncommon or generate project specific ontologies or vocabularies, will you provide mappings to more commonly used ontologies?2.4. Increase data re-use (through clarifying licences)How will the data be licensed to permit the widest re-use possible?When will the data be made available for re-use? If an embargo is sought to give time to publish or seek patents, specify why and how long this will apply, bearing in mind that research data should be made available as soon as possible.Are the data produced and/or used in the project useable by third parties, in particular after the end of the project? If the re-use of some data is restricted, explain why.How long is it intended that the data remains re-usable?Are data quality assurance processes described?Further to the FAIR principles, DMPs should also address:3. Allocation of resourcesWhat are the costs for making data FAIR in your project?How will these be covered? Note that costs related to open access to research data are eligible as part of the Horizon 2020 grant (if compliant with the Grant Agreement conditions).Who will be responsible for data management in your project?Are the resources for long term preservation discussed (costs and potential value, who decides and how what data will be kept and for how long)?4. Data securityWhat provisions are in place for data security (including data recovery as well as secure storage and transfer of sensitive data)?Is the data safely stored in certified repositories for long term preservation and curation?5. Ethical aspectsAre there any ethical or legal issues that can have an impact on data sharing? These can also be discussed in the context of the ethics review. If relevant, include references to ethics deliverables and ethics chapter in the Description of the Action (DoA).Is informed consent for data sharing and long term preservation included in questionnaires dealing with personal data?6. Other issuesDo you make use of other national/funder/sectorial/departmental procedures for data management? If yes, which ones?7. Further support in developing your DMPThe Research Data Alliance provides a Metadata Standards Directory that can be searched for discipline-specific standards and associated tools.The EUDAT B2SHARE tool includes a built-in license wizard that facilitates the selection of an adequate license for research data.Useful listings of repositories include:Registry of Research Data RepositoriesSome repositories like Zenodo, an OpenAIRE and CERN collaboration), allow researchers to deposit both publications and data, while providing tools to link them.Other useful tools include DMP online and platforms for making individual scientific observations available such as ScienceMatters.SUMMARY TABLE 1FAIR Data Management at a glance: issues to cover in your Horizon 2020 DMPThis table provides a summary of the Data Management Plan (DMP) issues to be addressed, as outlined above.678。

data deposition in repositories -回复“data deposition in repositories”指的是将数据存放在仓库中的过程。

近年来，随着数据科学的快速发展，研究人员越来越意识到数据共享的重要性。

构建公开可访问的数据仓库既有助于数据的保存与备份，也方便了其他研究人员使用和验证数据。

本文将介绍数据存放仓库的背景、重要性以及一般的数据存放过程。

# 1. 背景数据存放仓库的兴起主要得益于以下几个方面：1.科学共享：数据是科学研究的核心，数据的共享对于快速推动科学进展至关重要。

通过数据存放仓库，研究人员可以方便地共享、沟通和合作。

2.增加可重复性：科学研究的重要性在于可重复性。

通过将数据存放在公开仓库中，其他研究人员可根据相同的数据重新分析、验证和验证现有的研究结果。

3.数据保护与备份：数据存放仓库提供了一个安全的环境来存放和备份数据。

无论是因硬盘故障、病毒入侵还是人为错误，数据仓库都保证数据的安全性。

# 2. 重要性数据存放在仓库中具有多种重要性：1.可追溯性：数据仓库要求对数据进行标准化、元数据的描述和版本控制，这样其他人可以准确地追溯研究数据的来源和处理过程。

2.开放获取：公开可访问的数据仓库使得数据资料具有更大的可访问性，任何人都可以使用这些数据进行进一步的研究。

3.快速复用：通过存放在仓库中的数据，其他研究人员可以更快速地利用现有的数据进行探索、分析和研究。

4.数据审查：数据存放仓库允许其他研究人员和领域专家对数据进行审查和验证，以确保数据的质量和可信度。

# 3. 存放过程下面是一般的数据存在仓库中的步骤：1.数据准备：在将数据存放到仓库中之前，需要对数据进行清理、标准化和格式化。

这一步骤旨在确保数据的一致性和可读性。

2.选择仓库：选择一个适合自己需求的数据存放仓库平台。

目前有许多仓库可供选择，如Zenodo、figshare、Dryad等。

3.注册账户：选择一个合适的数据存放仓库后，需要注册一个账户。

1、Bilingual 双语Chinese English bilingual text 中英对照2、Data warehouse and Data Mining 数据仓库与数据挖掘3、classification 分类systematize classification 使分类系统化4、preprocess 预处理The theory and algorithms of automatic fingerprint identification system (AFIS) preprocess are systematically illustrated.摘要系统阐述了自动指纹识别系统预处理的理论、算法5、angle 角度6、organizations 组织central organizations 中央机关7、OLTP On-Line Transactional Processing 在线事物处理8、OLAP On-Line Analytical Processing 在线分析处理9、Incorporated 包含、包括、组成公司A corporation is an incorporated body 公司是一种组建的实体10、unique 唯一的、独特的unique technique 独特的手法11、Capabilities 功能Evaluate the capabilities of suppliers 评估供应商的能力12、features 特征13、complex 复杂的14、information consistency 信息整合15、incompatible 不兼容的16、inconsistent 不一致的Those two are temperamentally incompatible 他们两人脾气不对17、utility 利用marginal utility 边际效用18、Internal integration 内部整合19、summarizes 总结20、application-oritend 应用对象21、subject-oritend 面向主题的22、time-varient 随时间变化的23、tomb data 历史数据24、seldom 极少Advice is seldom welcome 忠言多逆耳25、previous 先前的the previous quarter 上一季26、implicit 含蓄implicit criticism 含蓄的批评27、data dredging 数据捕捞28、credit risk 信用风险29、Inventory forecasting 库存预测30、business intelligence（BI）商业智能31、cell 单元32、Data cure 数据立方体33、attribute 属性34、granular 粒状35、metadata 元数据36、independent 独立的37、prototype 原型38、overall 总体39、mature 成熟40、combination 组合41、feedback 反馈42、approach 态度43、scope 范围44、specific 特定的45、data mart 数据集市46、dependent 从属的47、motivate 刺激、激励Motivate and withstand higher working pressure个性积极，愿意承受压力.敢于克服困难48、extensive 广泛49、transaction 交易50、suit 诉讼suit pending 案件正在审理中51、isolate 孤立We decided to isolate the patients.我们决定隔离病人52、consolidation 合并So our Party really does need consolidation 所以，我们党确实存在一个整顿的问题53、throughput 吞吐量Design of a Web Site Throughput Analysis SystemWeb网站流量分析系统设计收藏指正54、Knowledge Discovery（KDD）55、non-trivial(有价值的）--Extraction interesting (non-trivial(有价值的), implicit（固有的）, previously unknown and potentially useful) patterns or knowledge from huge amounts of data.56、archeology 考古57、alternative 替代58、Statistics 统计、统计学population statistics 人口统计59、feature 特点A facial feature 面貌特征60、concise 简洁a remarkable concise report 一份非常简洁扼要的报告61、issue 发行issue price 发行价格62、heterogeneous (异类的)--Constructed by integrating multiple, heterogeneous (异类的)data sources63、multiple 多种Multiple attachments多实习64、consistent（一贯）、encode（编码）ensure consistency in naming conventions,encoding structures, attribute measures, etc.确保一致性在命名约定，编码结构，属性措施，等等。

文献信息：文献标题：A Study of Data Mining with Big Data（大数据挖掘研究）国外作者：VH Shastri，V Sreeprada文献出处：《International Journal of Emerging Trends and Technology in Computer Science》,2016,38(2):99-103字数统计：英文2291单词，12196字符；中文3868汉字外文文献：A Study of Data Mining with Big DataAbstract Data has become an important part of every economy, industry, organization, business, function and individual. Big Data is a term used to identify large data sets typically whose size is larger than the typical data base. Big data introduces unique computational and statistical challenges. Big Data are at present expanding in most of the domains of engineering and science. Data mining helps to extract useful data from the huge data sets due to its volume, variability and velocity. This article presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective.Keywords: Big Data, Data Mining, HACE theorem, structured and unstructured.I.IntroductionBig Data refers to enormous amount of structured data and unstructured data thatoverflow the organization. If this data is properly used, it can lead to meaningful information. Big data includes a large number of data which requires a lot of processing in real time. It provides a room to discover new values, to understand in-depth knowledge from hidden values and provide a space to manage the data effectively. A database is an organized collection of logically related data which can be easily managed, updated and accessed. Data mining is a process discovering interesting knowledge such as associations, patterns, changes, anomalies and significant structures from large amount of data stored in the databases or other repositories.Big Data includes 3 V’s as its characteristics. They are volume, velocity and variety. V olume means the amount of data generated every second. The data is in state of rest. It is also known for its scale characteristics. Velocity is the speed with which the data is generated. It should have high speed data. The data generated from social media is an example. Variety means different types of data can be taken such as audio, video or documents. It can be numerals, images, time series, arrays etc.Data Mining analyses the data from different perspectives and summarizing it into useful information that can be used for business solutions and predicting the future trends. Data mining (DM), also called Knowledge Discovery in Databases (KDD) or Knowledge Discovery and Data Mining, is the process of searching large volumes of data automatically for patterns such as association rules. It applies many computational techniques from statistics, information retrieval, machine learning and pattern recognition. Data mining extract only required patterns from the database in a short time span. Based on the type of patterns to be mined, data mining tasks can be classified into summarization, classification, clustering, association and trends analysis.Big Data is expanding in all domains including science and engineering fields including physical, biological and biomedical sciences.II.BIG DATA with DATA MININGGenerally big data refers to a collection of large volumes of data and these data are generated from various sources like internet, social-media, business organization, sensors etc. We can extract some useful information with the help of Data Mining. It is a technique for discovering patterns as well as descriptive, understandable, models from a large scale of data.V olume is the size of the data which is larger than petabytes and terabytes. The scale and rise of size makes it difficult to store and analyse using traditional tools. Big Data should be used to mine large amounts of data within the predefined period of time. Traditional database systems were designed to address small amounts of data which were structured and consistent, whereas Big Data includes wide variety of data such as geospatial data, audio, video, unstructured text and so on.Big Data mining refers to the activity of going through big data sets to look for relevant information. To process large volumes of data from different sources quickly, Hadoop is used. Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment. Its distributed supports fast data transfer rates among nodes and allows the system to continue operating uninterrupted at times of node failure. It runs Map Reduce for distributed data processing and is works with structured and unstructured data.III.BIG DATA characteristics- HACE THEOREM.We have large volume of heterogeneous data. There exists a complex relationship among the data. We need to discover useful information from this voluminous data.Let us imagine a scenario in which the blind people are asked to draw elephant. The information collected by each blind people may think the trunk as wall, leg as tree, body as wall and tail as rope. The blind men can exchange information with each other.Figure1: Blind men and the giant elephantSome of the characteristics that include are:i.Vast data with heterogeneous and diverse sources: One of the fundamental characteristics of big data is the large volume of data represented by heterogeneous and diverse dimensions. For example in the biomedical world, a single human being is represented as name, age, gender, family history etc., For X-ray and CT scan images and videos are used. Heterogeneity refers to the different types of representations of same individual and diverse refers to the variety of features to represent single information.ii.Autonomous with distributed and de-centralized control: the sources are autonomous, i.e., automatically generated; it generates information without any centralized control. We can compare it with World Wide Web (WWW) where each server provides a certain amount of information without depending on other servers.plex and evolving relationships: As the size of the data becomes infinitely large, the relationship that exists is also large. In early stages, when data is small, there is no complexity in relationships among the data. Data generated from social media and other sources have complex relationships.IV.TOOLS:OPEN SOURCE REVOLUTIONLarge companies such as Facebook, Yahoo, Twitter, LinkedIn benefit and contribute work on open source projects. In Big Data Mining, there are many open source initiatives. The most popular of them are:Apache Mahout:Scalable machine learning and data mining open source software based mainly in Hadoop. It has implementations of a wide range of machine learning and data mining algorithms: clustering, classification, collaborative filtering and frequent patternmining.R: open source programming language and software environment designed for statistical computing and visualization. R was designed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand beginning in 1993 and is used for statistical analysis of very large data sets.MOA: Stream data mining open source software to perform data mining in real time. It has implementations of classification, regression; clustering and frequent item set mining and frequent graph mining. It started as a project of the Machine Learning group of University of Waikato, New Zealand, famous for the WEKA software. The streams framework provides an environment for defining and running stream processes using simple XML based definitions and is able to use MOA, Android and Storm.SAMOA: It is a new upcoming software project for distributed stream mining that will combine S4 and Storm with MOA.Vow pal Wabbit: open source project started at Yahoo! Research and continuing at Microsoft Research to design a fast, scalable, useful learning algorithm. VW is able to learn from terafeature datasets. It can exceed the throughput of any single machine networkinterface when doing linear learning, via parallel learning.V.DATA MINING for BIG DATAData mining is the process by which data is analysed coming from different sources discovers useful information. Data Mining contains several algorithms which fall into 4 categories. They are:1.Association Rule2.Clustering3.Classification4.RegressionAssociation is used to search relationship between variables. It is applied in searching for frequently visited items. In short it establishes relationship among objects. Clustering discovers groups and structures in the data.Classification deals with associating an unknown structure to a known structure. Regression finds a function to model the data.The different data mining algorithms are:Table 1. Classification of AlgorithmsData Mining algorithms can be converted into big map reduce algorithm based on parallel computing basis.Table 2. Differences between Data Mining and Big DataVI.Challenges in BIG DATAMeeting the challenges with BIG Data is difficult. The volume is increasing every day. The velocity is increasing by the internet connected devices. The variety is also expanding and the organizations’ capability to capture and process the data is limited.The following are the challenges in area of Big Data when it is handled:1.Data capture and storage2.Data transmission3.Data curation4.Data analysis5.Data visualizationAccording to, challenges of big data mining are divided into 3 tiers.The first tier is the setup of data mining algorithms. The second tier includesrmation sharing and Data Privacy.2.Domain and Application Knowledge.The third one includes local learning and model fusion for multiple information sources.3.Mining from sparse, uncertain and incomplete data.4.Mining complex and dynamic data.Figure 2: Phases of Big Data ChallengesGenerally mining of data from different data sources is tedious as size of data is larger. Big data is stored at different places and collecting those data will be a tedious task and applying basic data mining algorithms will be an obstacle for it. Next we need to consider the privacy of data. The third case is mining algorithms. When we are applying data mining algorithms to these subsets of data the result may not be that much accurate.VII.Forecast of the futureThere are some challenges that researchers and practitioners will have to deal during the next years:Analytics Architecture:It is not clear yet how an optimal architecture of analytics systems should be to deal with historic data and with real-time data at the same time. An interesting proposal is the Lambda architecture of Nathan Marz. The Lambda Architecture solves the problem of computing arbitrary functions on arbitrary data in real time by decomposing the problem into three layers: the batch layer, theserving layer, and the speed layer. It combines in the same system Hadoop for the batch layer, and Storm for the speed layer. The properties of the system are: robust and fault tolerant, scalable, general, and extensible, allows ad hoc queries, minimal maintenance, and debuggable.Statistical significance: It is important to achieve significant statistical results, and not be fooled by randomness. As Efron explains in his book about Large Scale Inference, it is easy to go wrong with huge data sets and thousands of questions to answer at once.Distributed mining: Many data mining techniques are not trivial to paralyze. To have distributed versions of some methods, a lot of research is needed with practical and theoretical analysis to provide new methods.Time evolving data: Data may be evolving over time, so it is important that the Big Data mining techniques should be able to adapt and in some cases to detect change first. For example, the data stream mining field has very powerful techniques for this task.Compression: Dealing with Big Data, the quantity of space needed to store it is very relevant. There are two main approaches: compression where we don’t loose anything, or sampling where we choose what is thedata that is more representative. Using compression, we may take more time and less space, so we can consider it as a transformation from time to space. Using sampling, we are loosing information, but the gains inspace may be in orders of magnitude. For example Feldman et al use core sets to reduce the complexity of Big Data problems. Core sets are small sets that provably approximate the original data for a given problem. Using merge- reduce the small sets can then be used for solving hard machine learning problems in parallel.Visualization: A main task of Big Data analysis is how to visualize the results. As the data is so big, it is very difficult to find user-friendly visualizations. New techniques, and frameworks to tell and show stories will be needed, as for examplethe photographs, infographics and essays in the beautiful book ”The Human Face of Big Data”.Hidden Big Data: Large quantities of useful data are getting lost since new data is largely untagged and unstructured data. The 2012 IDC studyon Big Data explains that in 2012, 23% (643 exabytes) of the digital universe would be useful for Big Data if tagged and analyzed. However, currently only 3% of the potentially useful data is tagged, and even less is analyzed.VIII.CONCLUSIONThe amounts of data is growing exponentially due to social networking sites, search and retrieval engines, media sharing sites, stock trading sites, news sources and so on. Big Data is becoming the new area for scientific data research and for business applications.Data mining techniques can be applied on big data to acquire some useful information from large datasets. They can be used together to acquire some useful picture from the data.Big Data analysis tools like Map Reduce over Hadoop and HDFS helps organization.中文译文：大数据挖掘研究摘要数据已经成为各个经济、行业、组织、企业、职能和个人的重要组成部分。

What is Data Mining?Many people treat data mining as a synonym for another popularly used term, “Knowledge Discovery in Databases”, or KDD. Alternatively, others view data mining as simply an essential step in the process of knowledge discovery in databases. Knowledge discovery consists of an iterative sequence of the following steps: · data cleaning: to remove noise or irrelevant data,· data integration: where multiple data sources may be combined,· data selection : where data relevant to the analysis task are retrieved from the database,·data transformation : where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance,· data mining: an essential process where intelligent methods are applied in order to extract data patterns,·pattern evaluation: to identify the truly interesting patterns representing knowledge based on some interestingness measures, and·knowledge presentation: where visualization and knowledge representation techniques are used to present the mined knowledge to the user .The data mining step may interact with the user or a knowledge base. The interesting patterns are presented to the user, and may be stored as new knowledge in the knowledge base. Note that according to this view, data mining is only one step in the entire process, albeit an essential one since it uncovers hidden patterns for evaluation.We agree that data mining is a knowledge discovery process. However, in industry, in media, and in the database resea rch milieu, the term “data mining” is becoming more popular than the longer term of “knowledge discovery in databases”. Therefore, in this book, we choose to use the term “data mining”. We adopt a broad view of data mining functionality: data mining is the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories.Based on this view, the architecture of a typical data mining system may have the following major components:1. Database, data warehouse, or other information repository. This is one or a set of databases, data warehouses, spread sheets, or other kinds of information repositories. Data cleaning and data integration techniques may be performed on the data.2. Database or data warehouse server. The database or data warehouse server is responsible for fetching the relevant data, based on the user’s data mining request.3. Knowledge base. This is the domain knowledge that is used to guide the search, or evaluate the interestingness of resulting patterns. Such knowledge can include concept hierarchies, used to organize attributes or attribute values into different levels of abstraction. Knowledge such as user beliefs, which can be used to assess a pattern’s i nterestingness based on its unexpectedness, may also be included. Other examples of domain knowledge are additional interestingness constraints or thresholds, and metadata (e.g., describing data from multiple heterogeneous sources).4. Data mining engine. This is essential to the data mining system and ideally consists of a set of functional modules for tasks such as characterization, association analysis, classification, evolution and deviation analysis.5. Pattern evaluation module. This component typically employs interestingness measures and interacts with the data mining modules so as to focus the search towards interesting patterns. It may access interestingness thresholds stored in the knowledge base. Alternatively, the pattern evaluation module may be integrated with the mining module, depending on the implementation of the data mining method used. For efficient data mining, it is highly recommended to push the evaluation of pattern interestingness as deep as possible into the mining process so as to confine the search to only the interesting patterns.6. Graphical user interface. This module communicates between users and the data mining system, allowing the user to interact with the system by specifying a data mining query or task, providing information to help focus the search, and performing exploratory data mining based on the intermediate data mining results. In addition, this component allows the user to browse database and data warehouse schemas or data structures, evaluate mined patterns, and visualize the patterns in different forms.From a data warehouse perspective, data mining can be viewed as an advanced stage of on-1ine analytical processing (OLAP). However, data mining goes far beyond the narrow scope of summarization-style analytical processing of data warehouse systems by incorporating more advanced techniques for data understanding.While there may be many “data mining systems” on the market, not all of them can perform true data mining. A data analysis system that does not handle large amounts of data can at most be categorized as a machine learning system, a statistical data analysis tool, or an experimental system prototype. A system that can only perform data or information retrieval, including finding aggregate values, or that performs deductive query answering in large databases should be more appropriately categorized as either a database system, an information retrieval system, or a deductive database system.Data mining involves an integration of techniques from mult1ple disciplines such as database technology, statistics, machine learning, high performance computing, pattern recognition, neural networks, data visualization, information retrieval, image and signal processing, and spatial data analysis. We adopt a database perspective in our presentation of data mining in this book. That is, emphasis is placed on efficient and scalable data mining techniques for large databases. By performing data mining, interesting knowledge, regularities, or high-level information can be extracted from databases and viewed or browsed from different angles. The discovered knowledge can be applied to decision making, process control, information management, query processing, and so on. Therefore, data mining is considered as one of the most important frontiers in database systems and one of the most promising, new database applications in the information industry.A classification of data mining systemsData mining is an interdisciplinary field, the confluence of a set of disciplines, including database systems, statistics, machine learning, visualization, and information science. Moreover, depending on the data mining approach used, techniques from other disciplines may be applied, such as neural networks, fuzzy and or rough set theory, knowledge representation, inductive logic programming, or high performance computing. Depending on the kinds of data to be mined or on the given data mining application, the data mining system may also integrate techniques from spatial data analysis, Information retrieval, pattern recognition, image analysis, signal processing, computer graphics, Web technology, economics, or psychology.Because of the diversity of disciplines contributing to data mining, data mining research is expected to generate a large variety of data mining systems. Therefore, it is necessary to provide a clear classification of data mining systems. Such a classification may help potential users distinguish data mining systems and identifythose that best match their needs. Data mining systems can be categorized according to various criteria, as follows.1) Classification according to the kinds of databases mined.A data mining system can be classified according to the kinds of databases mined. Database systems themselves can be classified according to different criteria (such as data models, or the types of data or applications involved), each of which may require its own data mining technique. Data mining systems can therefore be classified accordingly.For instance, if classifying according to data models, we may have a relational, transactional, object-oriented, object-relational, or data warehouse mining system. If classifying according to the special types of data handled, we may have a spatial, time -series, text, or multimedia data mining system , or a World-Wide Web mining system . Other system types include heterogeneous data mining systems, and legacy data mining systems.2) Classification according to the kinds of knowledge mined.Data mining systems can be categorized according to the kinds of knowledge they mine, i.e., based on data mining functionalities, such as characterization, discrimination, association, classification, clustering, trend and evolution analysis, deviation analysis , similarity analysis, etc. A comprehensive data mining system usually provides multiple and/or integrated data mining functionalities.Moreover, data mining systems can also be distinguished based on the granularity or levels of abstraction of the knowledge mined, including generalized knowledge(at a high level of abstraction), primitive-level knowledge(at a raw data level), or knowledge at multiple levels (considering several levels of abstraction). An advanced data mining system should facilitate the discovery of knowledge at multiple levels of abstraction.3) Classification according to the kinds of techniques utilized.Data mining systems can also be categorized according to the underlying data mining techniques employed. These techniques can be described according to the degree of user interaction involved (e.g., autonomous systems, interactive exploratory systems, query-driven systems), or the methods of data analysis employed(e.g., database-oriented or data warehouse-oriented techniques, machine learning, statistics, visualization, pattern recognition, neural networks, and so on ) .A sophisticated data mining system will often adopt multiple data mining techniques or work out aneffective, integrated technique which combines the merits of a few individual approaches.什么是数据挖掘？许多人把数据挖掘视为另一个常用的术语—数据库中的知识发现或KDD的同义词。

data deposition in repositories -回复数据存储在仓库中的意义及其重要性在当今数字化时代，数据几乎无处不在。

无论是个人照片、公司财务报表还是科学研究数据，数据都扮演着至关重要的角色。

数据的存储和管理变得越来越关键，这就产生了数据仓库这一概念。

数据仓库是一个特定的存储系统，用于存储和管理各种类型和规模的数据。

它可以是一个物理设备，也可以是一个虚拟环境，但在任何情况下，数据仓库都是一个用于存储、保护和组织数据的地方。

数据仓库有着广泛的应用，包括但不限于以下几个领域：1. 企业业务数据仓库：数据仓库为企业提供了一个集中管理和组织数据的平台，从而帮助企业更好地了解其业务情况。

通过在数据仓库中存储和分析销售数据、客户数据和供应链数据等，企业能够获得对其业务运作的深入理解，并作出相应的战略决策。

2. 科学研究数据仓库：科学研究是一个数据密集型的领域，研究人员需要存储和管理大量的实验数据、研究结果等。

数据仓库可以为科学研究人员提供一个安全的存储和共享数据的环境，使得他们能够更好地进行数据分析和合作研究。

此外，数据仓库还可以帮助科学家对其研究成果进行备份和保护，以防止数据丢失。

3. 政府数据仓库：政府机构需要大量的数据来支持其政策制定和决策。

政府数据仓库是一个重要的工具，可以帮助政府机构在合规和政策制定方面进行数据分析和监测。

此外，政府数据仓库还可以提供一个共享数据的平台，促进不同机构之间的信息共享和协作。

不仅是数据存储的重要性，选择合适的数据仓库也至关重要。

以下是一些值得注意的因素：1. 数据安全性：在存储数据时，安全性是一个非常重要的考虑因素。

数据仓库需要提供可靠的安全措施，以防止未经授权的访问和数据泄露。

常见的安全措施包括访问控制、数据加密和备份策略等。

2. 数据完整性：数据完整性是指数据的准确性和一致性。

数据仓库需要确保存储的数据是完整和准确的，以便用户能够依赖这些数据进行业务分析和决策。

Common Vocabulary of Sedimentology堆积学常用词汇Aabandoned channel 荒弃河流abandoned delta 荒弃的三角洲ablation breccia 融化角砾岩abrasion terrace 海蚀阶地absolute age 绝对年纪absorpting 吸附性 absorption汲取性abundance of trace element 微量元素丰度abyss(deep sea) 深海abyssal basin 深海盆地abyssal deposit 深海堆积abyssal facies 深海相abyssal ooze 深海软泥abyssal plain 深海平原abyssal subcompensational basin 深海欠赔偿盆地accretion ripple 加积波痕accretion topography 加积地形accretional plain 加积平原accumulation terrace 聚积阶地ACD(aragonite compensation depth) 文石补偿深度acicular texture 针状结构 acidsialic stage 酸性硅铝阶段active continental margin 活动大陆边沿active crust 活动地壳adhesion ripple 粘附波痕adhesion wart 粘附瘤痕advanced dune 行进沙丘aeolian dune facies 风成沙丘相aeolian ripple marks 风成波痕aeolian rock 风成岩agglomerate 集块岩agglomeratic texture 集块结构agglutinated grain texture 胶粒结构aggradation 加积作用 air-heave structure 气胀结构 aleuropelitictexture 粉沙泥质结构algal mat 藻席algal mound 藻丘algal ooid藻鲕algal peloid藻球粒algal reef 藻礁algal reef limestone 藻礁灰岩alkali lake 碱湖 alkali-feldspar碱性长石 allitized stage 铝铁土阶段 allochem 异化颗粒allochemical limestone 异化颗粒灰岩allochemical phosphorite 异化磷块岩allochthonous limestone 异地灰岩allochthonous phosphorite 异地磷块岩alluvial apron 冲积裙alluvial facies 冲积相 alluvial fan冲积扇 alluvial plain 冲积平原alluvial terrace 冲积阶地 alteredtuff 蚀变凝灰岩 ammonoid 菊石类amorphous texture 胶状结构amphibian 两栖动物amygdaloidal structure 杏仁结构anadiagenesis 后生成岩作用analcimolith 方沸石岩analysis of rocks and minerals岩矿剖析angiosperm 被子植物angular 棱角状angular gravel 角砾angular unconformity角度不整合anhydrite 硬石膏 anhydrock硬石膏岩 anisotropic fabric异向组构 antecedent river先成河Anthichnium花趾迹antidune cross bedding 逆行沙丘交织层理apatite 磷灰石aphanitic texture 隐晶结构apparent age 表观年纪apposition fabric 同堆积组构aquatolysis 陆解作用aragonite 文石archaeocyathid 古杯类Archaeopteryx 鼻祖鸟Archean Era 远古代Arenicolites沙蚤迹arenite 砂屑岩，净砂岩argillite泥板岩arid climate干旱天气arkose 长石砂岩arsenopyrite 毒砂Arthropoda节肢动物ascending springy 上涨泉ash content 灰分ash fragment 灰屑asphalt 地沥青asphalt mound 沥青丘asphaltenes 沥青质assimilation同化作用asymmetrical ripple不对称波痕atoll环礁atmosphere 大气圈aulacogen 拗拉谷authigenic mineral 自生矿物autunite 钙铀云母 auxiliarymineral 次要矿物 axis 地轴BB-C sequence B —C 层序back reef facies 礁后相back arc basin 弧后盆地backshore zone 后滨带backshore facies 后滨相back swamp 河漫沼泽back swamp facies 河漫沼泽相backward erosion向源侵害backwash 回流bacteria 细菌bafflestone 障积岩baffling texture障积结构bahamite 巴哈马石ball and pillow structure砂球枕结构banded bedding 带状层理banded limestone facies 缎带灰岩相bank reef堤礁bank seismic facies 滩地震相barchan 新月型沙丘barite 重晶石barrier beach 障壁滩barrier bar 障壁沙坝barrier island障壁岛barrier island system 障壁岛系统barrier reef 障壁礁，堡礁basal arkose 基底长石砂岩basal cementation 基底式胶结basal conglomerate 底砾岩basalt 玄武岩baselap 底超basement 基底basic rock基性岩basinal lake 无口湖batholith岩基bathymetric map等深线图bauxitic rock铝土岩bauxitic rocks铝土岩类bayment 海湾bayment sediment 海湾堆积beach 海滩beach conglomerate 滨岸砾岩beach cusp 滩角beach environment and facies 海滩环境和相beach face 滩面beach facies 海滩相beach profile 海滩剖面beach ridge 滩脊beach ridge facies 海滩砂脊相beach rock 海滩岩 beachsystem 海滩系统Beaconites 灯塔迹bed 层bed load 床沙载荷bed migration 床沙迁徙bed thickness 层厚bedding 层理bedding plane structures 层面结构bedding structures 层理结构bedform 床沙形态benthos 底栖动物beryl 绿柱石biaxial crystal三轴晶bimodal cross bedding 双向交织层理bimodal current 双向流bimodal distribution 双峰散布 binding texture 粘结结构 bio-absorption 生物吸附bio-assimilation 生物汲取 bio-diagenesis生物成岩 bio-mineralization 生物成矿biochemical deposition 生物化学堆积biochemical transportation 生物化学搬运biochemical weathering 生物化学风化biochron 生物时bioclast 生物碎屑bioclastic limestone 生物碎屑灰岩bioclastic sparite 亮晶生物碎屑灰岩bioclastic micrite 生物碎屑微晶灰岩biodeposition生物堆积biofacies 生态相biofacies-paleogeographic map生物相古地理图biofixation 生物固定bioframework pore 生物骨架孔biogenic lamination structure 生物层理结构biogeochemistry(organic geochemistry) 有机地球化学bioherm生物层biohermal facies生物层相biohermal activity生物活动biohermal community生物群落biohermal marker生物标记化合物biohermal trail生物古迹biohermal weathering生物风化biomechanical deposition生物机械堆积biomechanical transportation生物机械搬运biophosphorite生物磷块岩biophysical weathering生物物理风化biosphere 生物圈biostratigraphic unit生物地层单位biostratigraphy生物地层学biosurface 生物面biota 生物群biotite黑云母bioturbation facies 生物扰动相bioturbation structure 生物扰动结构biozone 生物带bird fecal phosphorite鸟粪石磷块岩bird-foot delta鸟足状三角洲birdeye pore 鸟眼孔birdeye structure 鸟眼结构bitter lake 盐湖bitumen沥青bivalve 双壳类black shale 黑色页岩bladed 叶片状blister mat泡状藻席blue-green algae(cyanophyte) 蓝绿藻body fossils 实体化石boehmite 一水软铝石boehmitic rock 一水铝土岩boghead coal 藻煤 boracite方硼石 borax 硼砂boring 生物钻孔bottom cast 底模bottom current底流bottom current facies 底流相bottom mark 底痕bottom moraine facies 底积相bottom reef facies 礁底相bottom set 底积层 boulder 漂砾boulder conglomerate 漂砾砾岩Bouma sequence 鲍马层序boundstone 粘结岩 brachiopod腕足动物 brackish-water lake 半咸水湖 braid index 游荡性指数braided river 辫状河breaker zone 碎浪带breccia 角砾岩broken stage 破裂阶段brown algae 褐藻bruise 撞痕brush cast 刷模bryophyte苔藓植物bryozoan 苔藓虫类bubble impression 泡沫痕buildup岩隆bulkrock analysis全岩剖析burrow 潜穴CC14 age method C14 法caking property粘结性calcarenite 砂屑灰岩calcarenitic texture砂屑结构calcareous cement 钙质胶结物calcareous concretion 钙质结核calcareous quartzarenite 钙质石英岩calcareous shale 钙质页岩calcarenaceous turbidite facies 钙屑浊积岩相calcilutite泥屑灰岩calcilutic texture泥屑结构calcirudite砾屑灰岩calcirutidic texture砾屑结构calcisiltic texture粉屑结构calcisiltite粉屑灰岩calcite 方解石Caledonian movement 加里东运动caliche 钙结岩Cambrian Period寒武纪cap reef facies 礁盖相capillary action dolomitization毛细管浓缩白云化作用caprock 盖层captured river 袭夺河carbonhydrate 碳水化合物carbon isotope 碳同位素carbonaceous shale 炭质页岩carbonate flysch suite 碳酸盐岩复理石建筑carbonate grain 碳酸盐颗粒carbonate minerals 碳酸盐矿物carbonate platform碳酸盐台地carbonate rocks 碳酸盐岩类carbonatite 碳酸岩Carboniferous Period石炭纪carnallite 光卤石chemical structures 化学成因结构cast solution pore 铸模孔chemical weathering 化学风化cataclasite 碎裂岩chenier facies 千尼尔相catagenesis 后生作用chert 燧石岩catagenetic concretion 后生结核chevron mark 锯齿痕catagenetic conglomerate 后生砾岩chicken wire structure 鸡笼铁丝网结构catagenetic dolostone 后生白云岩Chirotherium 掌趾迹catagenetic mineral 后生矿物chlorite 绿泥石catastrophism 灾变论Chondrichthyes 软骨鱼类cathodoluminescence analysis 阴极发光剖析Chondrites 均分潜迹cave deposit 洞窟堆积chronostratigraphic unit 年月地层单位cave-collapse breccia 洞窟塌陷角砾岩chronostratigraphy 年月地层学CCD(calcite compensation depth) 方解石补chute 串沟偿深度chute cut-off 串沟截直CDT scale CDT 标准cirque 冰斗Ce anomaly 铈异样classic tectonics 经典大地结构学celestine 天青石clast 碎屑cement 胶结物clastic material 碎屑物质cement textures 胶结物结构clastic composition analysis 碎屑成分剖析cementation 胶结作用clastic mineral 碎屑矿物cementing zone 胶结带clastic model 碎屑模型Cenozoic Era 重生代clastic texture 碎屑结构center of subsidence 沉降中心clay 黏土cephalopoda 头足类clay mineral 黏土矿物chalcedony 玉髓claystone 黏土岩chalk 白垩climate 天气chalk carbonate suite 白垩碳酸岩建筑climatic arkose 天气长石砂岩chamosite 鲕绿泥石climbing ripple 爬生波痕channel-fill facies 河流充填相climbing ripple cross lamination 爬升沙纹层channel bar 心滩理chaotic carbonate facies 异地碳酸盐相clot 凝块石chaotic sediment 混淆堆积物cluster analysis 聚类剖析charophyte 轮藻CM plot C-M 图chemical analysis 化学剖析coal 煤chemical composition analysis 化学成分剖析coal geology 煤田地质学chemical sedimentary deposit 化学堆积矿床coal-accumulating process 聚煤作用chemical sedimentary differentiation 化学沉coal-forming process 成煤作用积分异coalification 煤化作用coaly monoterrigenous suite单陆屑含煤建筑coarse conglomerate 粗砾岩coarse sand 粗砂coarse silt 粗粉砂coarse siltstone 粗粉砂岩coast 海岸coast line 岸线coastal bay system 湾岸系统coastal lake area 滨湖区coastal lake facies 滨湖相coastal plain 海岸平原coastal reef 岸礁coastal topography 海岸地形coated grain 包粒coated grain phosphorite 包粒磷块岩cobble 粗砾coccolith颗石藻cock ’ s comb structure鸡冠状结构coefficient of the distribution 分派系数Coelenterata 腔肠动物 coking coal 焦煤coking property结焦性cold climate严寒天气cold water carbonate 冷水碳酸盐collapse breccia 滑塌角砾央colloform mat 胶状藻席 colloidsolution 胶体溶液 collophane 胶磷矿 compaction 压实作用 complexdelta 复合三角洲 compositionalmaturity 成分红熟度 compound ooid复鲕 compound ripple 复合波痕compound structures 复合成因结构comprehensive terms 综合术语concave flood plain 河漫滩concavo-convex contact 凹凸接触concretion 结核condensed section 凝缩层 cone-in-cone structure 叠锥结构conformity 整合conglomerate 砾岩conglomerates 砾岩类connecting texture 连生结构conodont 牙形石类consequent river 顺向河 consolidation固结作用 constructive delta 建设性三角洲 contact cementation 接触式胶结contact metamorphism 接触变质contemporaneous concretion 同生结核contemporaneous conglomerate 同生砾岩continental crust 陆壳continental drift hypothesis大陆漂移说continental glacier大陆冰川continental growth大陆增生continental rift大陆裂谷continental rise 陆隆continental slope陆坡contour current 等深流contourite 等深积岩（等深岩）contourite drift 等深岩丘contourite facies等深积岩相convergent margin 汇聚边沿convolute bedding 包卷层理coppery carbonate rock 含铜碳酸盐岩coppery conglomerate 含铜砾岩coppery rock 铜质岩coppery rocks 铜质岩类coppery sandstone 含铜砂岩coppery shale 含铜页岩coquina 介壳灰岩coral 珊瑚coral reef 珊瑚礁coral reef limestone 珊瑚礁灰岩coral reef facies 礁核相correlative analysis有关剖析coset 层系组Cosmorhaphe 丽线迹country rock围岩cover 盖层crack 裂隙crater lake 火口湖cratonic basin 克拉通盆地crawling trail爬行踪CRER(Cretaceous Events and Rhythms)白垩纪事件与韵律Cretaceous Period 白垩纪crevasse splay 决堤扇crevasse-splay facies 决堤扇相Crinoid海百合类cristobalite 方英石cross bedding 交织层理cross bedding orientation交织层理定向crude oil 原油Cruziana 克鲁兹迹Cruziana assemblage 克鲁兹迹组合cryptograin texture 隐粒结构Cryptozoic Eon 隐生宙crystal cast 晶体印模crystal fragment 晶屑crystal grain texture晶粒结构crystal optics 晶体光学crystal tuff 晶屑凝灰岩crystallized ooid 变晶鲕crystalline dolostone 结晶白云岩crystalline granular texture 晶粒结构crystallization differentiation 结晶分异作用crystallo petrology 结晶岩石学crystalloblastic texture 变晶结构cumulative frequency curve频次积累曲线cumulative probability curve概率积累曲线current mark 流痕current ripple流水流痕current ripple cross lamination 流水沙纹层理current rose 流水玫瑰花图curty bedding 卷曲层理curved ripple 歪曲波痕cuspate delta 鸟嘴状三角洲cut-off 裁弯取直cutoff basin 关闭盆地 cyclesequence 旋回层序cyclostratigraphy 旋回地层学Cylindrichnus 柱管迹Cylindricum 柱踪迹Ddammed lake 堰塞湖damp climate 湿润天气debris flow泥石流debris flow facies泥石流相decomposition分解作用dedolomitization去白云化作用deep lake area 深湖区 deeplake facies 深湖相 deepwater lagoon 深水泻湖 deep-water basin 深水盆地deep-water environment 深水环境deep-water shelf 深水陆棚 deep-water system 深水堆积系统deformation 变形deformation fabric 形变结构degradation 降解作用degypsification 去膏化作用dehydration 脱水作用delta 三角洲delta environment and facies三角洲环境和相delta front 三角洲前缘delta lobate 三角洲叶状体delta plain三角洲平原delta plain facies 三角洲平原相delta system 三角洲系统deluvial facies坡积相deluvial zone坡积带density current密度流denudation 剥蚀作用depocenter 堆积中心depositional bauxitic rock堆积型铝土岩depositional cycle堆积旋回depositional plain堆积平原depositional system 堆积系统depositional system analysis堆积系统剖析desert 荒漠desert environment and facies 荒漠环境和相desert facies 荒漠相desert lake 荒漠湖泊desert lake facies 荒漠湖泊相desert system 荒漠系统 desertvarnish 荒漠漆 destructivedelta 损坏性三角洲determination of rocks and minerals 岩矿判定deviation ooid 偏爱鲕development in sedimentology堆积学进展Devonian Period 泥盆纪diagenesis 成岩作用 diageneticconcretion 成岩结核 diagenetic conglomerate 成岩砾岩 diageneticcrack 成岩裂隙 diageneticdolostone 成岩白云岩 diagenetic environment 成岩环境 diageneticmineral 成岩矿物 diagenetic trap成岩圈闭 diagonal bar 斜列沙坝diagonal bedding斜层理diamictite混积岩diapiric structure底辟结构diara 沙洲diaspore一水硬铝石diastem 堆积停留diatactive varve粒度递变纹泥diatom 硅藻diatom ooze 硅藻软泥diatomite 硅藻岩diatreme breccia 迸发角砾岩dickite地开石Dictyodora网锥迹differential thermal analysis差热剖析differential weathering差别风化Dikaka 双叉迹dike 岩脉Dinosaur 恐龙dip 偏向Diplichnites双趾迹Diplocraterion双环迹direct runoff地表径流dirty sandstone 脏砂岩discriminant analysis鉴别剖析dish structure 盘状结构disorganized 乱杂型disorganized bedding杂乱层理dispersal system 分别系dissected basin 切割盆地dissolution溶解dissolved load 溶解载荷dissolved material 溶解物质dissolving zone溶解带distal bar 远砂坝distal bar facies 远砂坝相distal facies 远源相distal fan 外扇distal onlap 远端上超distal turbidite远源浊积岩distorted ooid变形鲕distributary分流distributary backswamp facies 分流河流河漫沼泽相distributary channel 分流河流distributary channel facies 分流河流相distributary levee facies分流河流堤岸相distributary mouth bar河口砂坝distributary mouth bar facies河口砂坝相distribution of REE稀土元素分派distribution of trace element 微量元素分派distrophic lake 缺养湖disturbed bedding 扰动层理dolarenite 砂屑白云岩doll concretion钙结核dololaminite纹层状白云岩dololutite泥屑白云岩dolomicrite微晶白云岩dolomite 白云石dolomitic limestone 云灰岩 dolomitic phosphorite 白云质磷块岩dolomitization 白云石化作用dolomitized limestone 白云化灰岩dolorudite 砾屑白云岩dolosiltite粉屑白云岩dolostone 白云岩dolostone evaporite suite 白云岩型蒸发岩建造dolostones 白云岩类domelike stromatolite 穹状叠层石dorag dolomitization 混淆白云化作用double mud drapes 双黏土层downcutting 下切作用downlap 下超downslope ripple下移波痕draa 臂状沙丘drainage basin 流域drainage system 水系drift dam冰碛坝drift plain冰碛平原drowned platform淹没台地drowned valley溺谷drusy texture 丛生结构dune 沙丘dune facies 沙丘相dwelling trail居住迹Eearly diagenesis 初期成岩作用earth 地球earth ’ s core地核earth ’ s crust地壳earth ’ s mantle地幔Echinoderm 棘皮动物Echinoid海胆类ecology 生态学economic geology 经济地质学edgewise structure 竹叶状结构effusive rock 喷出岩Eh value Eh 值electronic probe analysis 电子探针剖析elevated crust 抬生地壳eluvial breccia残积角砾岩eluvial facies残积相eluvial zone残积带embayment 海湾enclosed sea 关闭海end moraine facies 终冰碛相end peneplain 终期准平原endobiont 内栖动物endogenetic sediments 内成堆积物endogenic deposit 内生矿床endorheic basin 内流盆地Eocene Epoch 始新世eolian basin 风蚀盆地eolian cross bedding 风成交织层理eolian dune 风成沙丘eolianite 风成岩eonothem 宇epeiric sea 陆表海epibionts 底表生物epidiagenesis 表生成岩作用epidiagenetic environment表生成岩环境epigenesis 表生作用epigenetic concretion表生结核epigenetic deposit 后生矿床epiglyph顶面痕epimatrix外杂基epoch of mineralization成矿期epsilon cross bedding ε交织层理epsomite 泻利盐equant 等轴状erathem 界erosion 侵害作用erosional basis 侵害基准面erosional plain侵害平原erosional terrace 侵害阶地erosional topography 侵害地形escape structure 逃逸结构esker 蛇形丘essential mineral 主要矿物estuarine facies 河口湾相estuary 河口湾ething mark刻蚀痕eugeosyncline 优地槽euryhaline environment广盐度环境euryhaline lagoon 广盐泻湖euryhaline organism 广盐性生物eurythermal organism 广温性生物eutrophic lake兹养湖evaporation 蒸发生用evaporite 蒸发岩evaporite platform 蒸发盐台地evaporite-solution breccia 盐溶角砾岩evaporites 蒸发岩类evaporitive pumping dolomitization蒸发泵白云化作用excrement 排泄物exinite壳质组exogenetic sediments 表成堆积物experimental sedimentology 实验堆积学explanation for orientation 定向解说Ffabric 组构fabric of carbonate rock 碳酸盐岩组构fabric of claystone 黏土岩组构 facies相facies arrangement 相摆列facies association 相组合facies belt 相带facies change 相变facies fossil 指相化石facies group 相组facies map 相图facies mineral 指相矿物facies model 相模式facies province 相区facies sequence 相序列fan-shaped delta 扇形三角洲faro 小环礁fault 断层fault basin断陷盆地fauna 动物群fecal peloid粪球粒feeding trail进食迹feldspar 长石feldsparthic litharenite 长石岩屑砂岩feldsparthic quartzarenite 长石石英砂岩felty structure 毡状结构fenestral structure 窗孔结构ferroalumino suite 铝土铁质建筑ferrodolomite 铁白云石 ferromanganese concretion 铁锰结核 ferruginous cement 铁质胶结物 ferruginous quartzarenite 铁质石英砂岩 ferruginous rocks 铁质岩类ferruginous rock 铁质岩ferruginous shale 铁质页岩fibrous texture纤状结构filled valley冲淤谷filled-lake plain淤积湖平原film mat薄膜状藻席film texture薄膜结构fine conglomerate细砾岩fine sand 细砂fine silt细粉砂fine siltstone细粉砂岩finger bar 指状砂坝firth三角港fixed dune固定沙丘flame structure 火焰结构flaser bedding 脉状层理flat bedding平展层理flat mat 平展藻席flocculation 聚沉作用，絮凝作用flocculation by condensation 浓缩聚沉flocculation by electrolyte 电解质聚沉flood 洪水flooded lake 河漫湖flooded lake facies河漫湖泊相floodplain洪积平原floodplain facies洪积平原相flora 植物群flow system流动系fluid流体fluid dynamics流体动力学fluidized sediment flow液化堆积物流flume experiment 水槽实验fluorescence analysis 荧光剖析fluorite萤石fluroapatite氟磷灰石flute cast 槽模flute cast orientation槽模定向fluival environment and facies河流环境和相fluival system冲积系统fluival-dominated delta河控三角洲fluxoturbidite滑塌浊积岩flysch复理石flysch facies复理石相flyschoid类复理石flysch suite复理石建筑foliaton叶理footprint足印foraminifera reef limestone 有孔虫礁灰岩Foraminiferan 有孔虫类fore reef facies 礁前相forearc basin 弧前盆地foredeep basin 前渊盆地foreland basin 前陆盆地foreset 前积层foreshore facies 前滨相foreslope facies 前缘斜坡相forslope of platform 台地前缘斜坡formation 组fossil 化石fossil microtextures 生物显微结构fossil orientation 化石定向framework grain 骨粒francolite 碳氟磷灰石freeze thaw weathering 冻融风化frequency curve 频次曲线fresh water dolomitization 淡水白云石化作用fresh water dolostone 淡水白云岩 fresh-water lake 淡水湖freshened lagoon 淡化泻湖freshening environment 淡化环境front moraine facies 前积相front sand sheet facies 前缘席状砂相frost weathering 寒冻风化frost 霜面Froude ’ s Number 福劳德数fusulinid 蜓类fungi真菌Gganister 致密硅岩Gastropod 腹足类gelatinous mat 凝胶状藻席gelatinous phosphorite 胶状磷块岩geochemical methods 地球化学方法geochronologic scale 地质年月表geological record地质记录geomorphologic unit地貌单元geomorphology 地貌学 geopetalstructure 示底结构 geophysicalmethods 地球物理方法 geosuture 地缝合线geosyncline 地槽giant ripple巨波痕gibbsite 三水铝石gibbsitic rock三水铝土岩Gilbert type delta 吉尔伯特型三角洲glacial climate 冰川天气glacial environment and facies 冰川环境和相glacial erratics 冰川漂砾glacial morphology 冰川地貌glacial system 冰川系统glacial tongue 冰舌glacial valley冰蚀谷glacier 冰川glaciodelta 冰成三角洲 glaciodelta facies冰成三角洲相 glaciofluvial environment冰川冲积环境glaciofluvial facies 冰水冲积相 glaciofluvial terrace 冰水阶地 glaciolacustrine environment 冰川湖泊环境 glaciolacutrine facies 冰川湖泊相 glaciomarine environment 冰海环境 glaciomarine facies 冰海相glauconite 海绿石glauconitic quartzarenite海绿石石英砂岩global climate change 全世界天气变迁global rhythm and event 全世界韵律和事件global sea level fluctuation 全世界海平面起落global sedimentary geology program 全世界沉积地质计划global sedimentology全世界堆积学global volcanism 全世界火山作用 globigerina ooze 抱球虫软泥gneiss 片麻岩gobi 沙漠gobi facies 沙漠相Gondwana 冈瓦纳古陆 Gondwanaland bridge 冈瓦纳陆桥 grade scale粒级标准graded bedding 粒序层理grain dolostone 颗粒白云岩grain flow 颗粒流grain index 颗粒指数grain limestone 颗粒灰岩grain orientation 颗粒定向grain phosphorite 粒屑磷块岩grain size 粒度grain size analysis 粒度剖析grain size distribution 粒度散布grain size parameter 粒度参数grain supported 颗粒支撑grainstone 颗粒岩granite 花岗岩granular texture 粒状结构granule 细砾grapestone 葡萄石graptolite 笔石类gravel 砾gravel fabric砾石组构gravity cementation重力胶结gravity flow重力流greywacke 灰瓦克岩，硬砂岩，杂砂岩grazing trail 觅食迹green algae(chorophyte) 绿藻grey biterrigenous suite 灰色复陆屑建筑graywacke (greywacke) 硬砂岩greywacke suite 杂砂岩建筑groove cast 沟模group 群growth fabric生长组构guide fossil标记化石guyot 海底平顶山Gymnosperm 裸子植物gypsolith石膏岩gypsum 石膏gypsum evaporite suite 膏盐型蒸发岩建筑Hhail print雹痕halides 卤化物halite 石盐halloysite埃洛石halmyrolysis海解作用hardground structure 硬底结构heating stage 显微镜热台heavy mineral 重矿物heavy mineral analysis 重矿物剖析Helminthoida蠕踪迹Helminthopsis拟蠕踪迹helocline 盐度跃层hematite 赤铁矿hematitic rock赤铁岩hemipelagic carbonate facies 半远洋碳酸盐相hemipelagic environment半远洋环境hemipelagic facies model半远洋相模式hermatypic coral造礁珊瑚herringbone cross bedding 羽状交织层理heulanditic rock片沸石岩hiatus 堆积中断high moor 高位沼泽high water level 热潮面 high-angle cross bedding 高角度交织层理high-density current 高密度流 high-energy ooid 高能鲕 high-magnesian calcite 高镁方解石 high-ranksandstone 高级杂砂岩 high-sinuosity river 高弯度河highmoor peat 高沼泥炭highstand system tract 高水位系统域hill丘陵histogram 直方图history of sedimentology堆积学史Holocene Epoch 崭新世homopycnal flow等密度流honey comb structure 蜂窝状结构horizontal bedding 水平层理host rock 主岩hot sping 热泉hot water dolomitization热水白云石化作用hot water sedimentary rock热水堆积岩hot water sedimentation热水堆积作用HREE 重稀土元素humic acid腐殖酸humic substance 腐殖质humic-type kerogen腐殖型干酪根huminite腐殖组hummocky cross bedding丘状交织层理hybrid sediments混淆堆积物hydrated zone 水合带hydration 水合作用hydrocarbon烃hydrogen isotope 氢同位素hydrolysis水解作用hydrolysis zone水解带hydrosphere 水圈hydrothermal deposit热液矿床hydrothermally cemented rock热泉胶结岩Hyolithid软舌螺类hypabyssal rock 浅成岩hypergenic mineral 表生矿物Iice crystal cast 冰晶模ice sheet 冰盖ichnology古迹学ideal section 理想剖面igneous petrology火成岩石学igneous rock 火成岩illite 伊利石imbricated structure 叠瓦结构illite claystone 伊利石黏土岩immersion method 油浸法immersion oil 浸油indentation 凹坑index fossil 标准化石index of meandering 曲流指数index of sinuosity 弯度指数indication of oil and gas 油气显示Indo-China movement 印支运动inertinite 惰性组infrared absorption spectrum analysis 红外光谱剖析injective structure 注入结构inland basin 内地盆地 inlanddrainage 内地水系inland sabkha 内地萨勃哈inland sabkha facies 内地萨勃哈相inner bar 内沙坝inner bar facies 内沙坝相inner shelf内地棚inner shelf facies 内地棚相inter-reef facies 礁间相interarc basin 弧间盆地intercalated beds 隔层intercontinental rift 陆间裂谷interdistributary bay分流间湾interference colour干预色interference ripple干预波痕interference figure干预图interformational conglomerate 层间砾岩intergranular pore 粒间孔intergranular solution pore 粒间溶孔interlayered bedding 互层层理 intermittent suspended load 间歇悬浮载荷intermontane basin 山间盆地internal tide 内潮汐 internal-tidedeposits 内潮汐堆积 internalwave 内波 internal-wave deposits内波堆积International Association of Sedimentologists (IAS)国际堆积学家协会International Union of Geological Sciences (IUGS)国际地科联interstitial water孔隙水interstitial material填隙物interstitial zone潮间带intra-arc basin 弧内盆地intrabasinal environment and facies内源堆积环境和相intrabasinal lake 盆内湖intrabasinal rock 盆内岩intrabasinal sediment 内源堆积物intraclast 内碎屑intraclastic limestone 内碎屑灰岩intraclastic phosphorite 内碎屑磷灰岩intracratonic basin 克拉通内盆地intraformational breccia 层内角砾岩intragranular pore 粒内孔 intragranular solution pore 粒内溶孔 intramicrite 内碎屑微晶灰岩 intramontane basin 山间盆地intrasparite 内碎屑亮晶灰岩 iinversegraded bedding 逆粒序层理 ionabsorption 离子吸附作用ion exchange 离子互换作用isolated platform孤立台地isolated ripple孤立波痕isolithic map等岩性图isopach cementation 等厚环边胶结isopachous map 等厚线图Isopodichnus 等趾迹isotope abundance 同位素丰度isotope composition 同位素构成isotope dating 同位素定年isotope fractionation 同位素分馏isotope fractionation factor 同位素分馏系数isotope ratio 同位素比值 isotope standard 同位素标准isotopic age 同位素年纪isotopic geochemistry 同位素地球化学isotropic fabric 等向组构Jjasper rock 碧玉岩jasper suite 碧石建筑Jurassic Period 侏罗纪Kkame 冰碛kaolinic claystone 高岭石黏土岩kaolinite 高岭石karst breccia 岩溶角砾岩karst lake 岩溶湖karst morphology岩溶地貌kerogen 干酪根kidney-like hematitic rock 肾状赤铁岩kidney-shaped texture 肾状结构 knoband tail mark 丘尾痕kurtosis 尖度Llacustrine 湖泊的，湖成的lacustrine system 湖泊系统lag facies 滞留相lagoon 泻湖lagoon facies 泻湖相lake delta 湖泊三角洲lake delta facies 湖泊三角洲相lamellar fibrous texture 层纤结构lamina 细层laminar flow 层流laminated texture 片状结构landslide 滑坡late diagenesis 后期成岩作用lateral accretion 侧向加积lateral erosion 侧向侵害lateral facies change 侧向相变laterite 红土lateritic bauxitic rock 红土型铝土岩laterized stage 红土阶段lattice structure 晶格状结构lava fragment 浆屑layered moraine 层状冰积物leaching zone 淋滤带legend 图例lenticular bedding 透镜状层理lenticulite 扁豆状熔灰岩levee facies 堤岸相Liesegang banding 李泽冈环lime clast 灰屑lime dolostone 灰云岩lime mud 灰泥lime mudstone 灰泥岩lime-mud matrix 灰泥基质lime-mud mound 灰泥丘limestone 石灰岩limestones 灰岩类limnic basin 陆相含煤盆地limnogenic rock 淡水堆积岩limonite 褐铁矿limonite rock褐铁岩linear contact 线接触linear sand bar 线形砂坝lineation线理link single crystal texture 连生单晶结构litharenite 岩屑砂岩lithic arkose岩屑长石砂岩lithic fragment岩石碎屑lithic graywacke岩屑杂砂岩lithic quartzarenite岩屑石英砂岩lithic tuff岩屑凝灰岩lithofacies岩相lithofacies map岩相图lithofication石化作用lithoherm岩礁lithologic column 岩性柱状图lithologic section 岩性剖面lithosphere 岩石圈 lithostratigraphic unit 岩性地层单位 lithostratigraphy岩石地层学 lithotype 煤岩种类 littoral dune 沿岸沙丘littoral zone滨海带load cast 重荷模lobate bedding 叶状层理loess 黄土loess concretion 黄土结核loess facies 黄土相loess field 黄土原logging测井lognormal distribution 对数正态散布longitudinal bar 纵向砂坝 longitudinal section 纵剖面 longshore bar 沿岸砂坝 longshore current 沿岸流Lophocteniun 菊瓣迹 low moor 低位沼泽low water level低潮面low-angle cross bedding 低角度交织层理low-density current 低密度流low-density turbidity current 低密度浊流low-rank sandstone 初级杂砂岩low-sinuosity river 低弯度河lower flow regime 下部流态lowsand system tract 低水位系统域LREE 轻稀土元素lump 团块lump limestone 团块灰岩lump micrite limestone 团块微晶灰岩lump phosphorite 团块磷块岩lump sparite limestone 亮晶团块灰岩Mmacroclastic rock粗碎屑岩macrofossil 大化石macroid 大包粒magma 岩浆magmatic deposit 岩浆矿床magmatic rock岩浆岩magmatism 岩浆作用magnesite 菱镁矿magnetic element 地磁因素magnetic pole 地磁极magnetic pole wandering 地磁极游移magnetic stratigraphy 磁性地层学magnetite 磁铁矿magnetitic rock磁铁岩main river干流mammal 哺乳动物manganiferous carbonate rock 锰质碳酸盐岩manganiferous claystone 锰质黏土岩manganiferous concretion 锰结核manganiferous concretional rock 锰质结核岩manganiferous rock 锰质岩 manganiferous rocks 锰质岩类 manganiferous siliceous rock 锰质硅质岩manganiferous clastic rock 锰质碎屑岩manganite 水锰矿mantle convection 地幔对流marchasite 白铁矿marginal platform边沿台地marginal sea 陆缘海marine bench 海蚀台marine cliff海蚀崖marine diagenetic environment 海水成岩环境marine drift 漂流marine environment and facies 大海环境和相marine facies 海相marine phreatic zone 海水潜流带marine salina 滨海盐沼marine vadose zone 海水渗流带Markov chain analysis 马尔可夫链剖析marl 泥灰岩marsh 沼泽marsh environment and facies 沼泽环境和相massive bedding 块状层理 massive flow 块体流matrix杂基matrix supported 杂基支撑maturity analysis 成熟度剖析maturity index 成熟度指数mean 均值mean grain size 均匀粒度mean high water level 均匀热潮面mean low water level 均匀低潮面mean range 均匀潮差 mean sealevel 均匀海平面meander belt 曲流带meander scroll 曲流涡形坝meandering river蛇形河meandering river曲流河measured section 实测剖面mechanical sedimentary differentiation机械堆积分异mechanical transportation and deposition机械搬运和堆积medial moraine facies中碛相median grain size 中值粒度Mediterranean sea 地中海medium conglomerate 中粒砾岩medium of transportation and sedimenation搬运和堆积介质medium sand 中砂megaripple 大波痕meltout till融出碛member 段meniscus cementation 新月形胶结Merostomichnites肢口迹Mesozoic Era 中生代 metallicmineral 金属矿物 metallic ore 金属矿产 metallogenetic epoch 成矿时代metallogenic belt 成矿带metallogenic province 成矿区metamorphic age 变质年纪metamorphic belt 变质带metamorphic deposit 变质矿床metamorphic facies 变质相metamorphic grade 变质程度metamorphic petrology 变质岩石学metamorphic rock 变质岩metamorphism 变质作用meteoric diagenetic environment淡水成岩环境meteoric phreatic zone 淡水潜流带meteoric vadose zone 淡水渗流带meteoric water 大气降水mica 云母micrite微晶micrite cementation泥晶胶结micritic allochemical limestone泥晶异化粒灰岩。