参考文献RIS文件

RIS格式是一种参考文献管理软件的标准文本文件格式，用于交换引用信息。它最初是由Research Information Systems公司为其参考文献管理软件Reference Manager开发的，因此得名RIS（Research Information Systems）。
RIS format is a plain text file format for reference management software, used for exchanging citation information.

RIS文件是纯文本文件，包含了一系列的标签和与之对应的值，用于描述文献的各种信息，如作者、标题、出版年份、期刊名称、卷号、页码等。每个条目（即一篇文献的信息）以一个或多个这样的标签-值对来表示。每个标签由两个字母组成，后面紧跟两个空格和一个连字符再加一个空格，然后是相应的值。不同的条目之间通过一个空行或特定的结束标签（ER -）来分隔。

An RIS file is a plain text file that displays a lot of information such as authors, journals, publication year, etc., in the form of tags and values. An example of a tag and its value is TY - JOUR, where “TY” is the tag representing the type of reference, and “JOUR” indicates that the reference is a journal article. Please note, there are two spaces to the left of the dash. The end of an RIS file is marked by ER -.

下面是一个RIS文件示例/What a RIS file looks like

TY  - JOUR
AU  - Crossa, J.
AU  - Pérez, P.
AU  - Hickey, J.
AU  - Burgueño, J.
AU  - Ornella, L.
AU  - Cerón-Rojas, J.
AU  - Zhang, X.
AU  - Dreisigacker, S.
AU  - Babu, R.
AU  - Li, Y.
AU  - Bonnett, D.
AU  - Mathews, K.
PY  - 2014
DA  - 2014/01/01
TI  - Genomic prediction in CIMMYT maize and wheat breeding programs
JO  - Heredity
SP  - 48
EP  - 60
VL  - 112
IS  - 1
AB  - Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center’s (CIMMYT’s) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT’s maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.
SN  - 1365-2540
UR  - https://doi.org/10.1038/hdy.2013.16
DO  - 10.1038/hdy.2013.16
ID  - Crossa2014
ER  -