资料新闻学

title: 基于资料的新闻报导.
content: Telling stories based on the data is the primary goal. The findings from data can be transformed into any form of journalistic writing. Visualizations can be used to create a clear understanding of a complex situation. Furthermore, elements of storytelling can be used to illustrate what the findings actually mean, from the perspective of someone who is affected by a development. This connection between data and story can be viewed as a "new arc" trying to span the gap between developments that are relevant, but poorly understood, to a story that is verifiable, trustworthy, relevant and easy to remember.
title: 资料品质.
content: In many investigations the data that can be found might have omissions or is misleading. As one layer of data-driven journalism a critical examination of the data quality is important. In other cases the data might not be public or is not in the right format for further analysis, e.g. is only available in a PDF. Here the process of data-driven journalism can turn into stories about data quality or refusals to provide the data by institutions. As the practice as a whole is in early development steps, examinations of data sources, data sets, data quality and data format are therefore an equally important part of this work.
title: 寻找资料.
content: Data can be obtained directly from governmental databases such as data.gov, data.gov.uk and World Bank Data API but also by placing Freedom of Information requests to government agencies; some requests are made and aggregated on websites like the UK's What Do They Know. While there is a worldwide trend towards opening data, there are national differences as to what extend that information is freely available in usable formats. If the data is in a webpage, scrapers are used to generate a spreadsheet. Examples of scrapers are: ScraperWiki, Firefox plugin OutWit Hub or Needlebase (note: Needlebase will be retired June 1, 2012). In other cases OCR-Software can be used to get data from PDFs.
title: 资料清洗.
content: Usually data is not in a format that is easy to visualize. Examples being that there are too many data points or that the rows and columns need to be sorted differently. Another issue is that once investigated many datasets need to be cleaned, structured and transformed. Various open source tools like Google Refine, Data Wrangler and Google Spreadsheets allow uploading, extracting or formatting data.
title: 资料视觉化.
content: To visualize data in the form of graphs and charts, applications such as Many Eyes or Tableau Public are available. Yahoo! Pipes and Open Heat Map are examples of tools that enable the creation of maps based on data spreadsheets. The number of options and platforms is expanding. Some new offerings provide options to search, display and embed data, an example being Timetric.
title: 出版资料故事.
content: There are different options to publish data and visualizations. A basic approach is to attach the data to single stories, similar to embedding web videos. More advanced concepts allow to create single dossiers, e.g. to display a number of visualizations, articles and links to the data on one page. Often such specials have to be coded individually, as many Content Management Systems are designed to display single posts based on the date of publication.
title: 散布资料.
content: Providing access to existing data is another phase, which is gaining importance. Think of the sites as "marketplaces" (commercial or not), where datasets can be found easily by others. Especially of the insights for an article where gained from Open Data, journalists should provide a link to the data they used for others to investigate (potentially starting another cycle of interogation, leading to new insights).  Providing access to data and enabling groups to discuss what information could be extracted is the main idea behind Buzzdata, a site using the concepts of social media such as sharing and following to create a community for data investigations.  Other platforms (which can be used both to gather or to distribute data):

Summary: 资料新闻学(台湾的叫法,在中国大陆称之为数据新闻)是指透过对大量资料集进行分析与筛检后来产出新闻报导(故事)的一种新闻处理程序。在资料新闻学中,我们常常会使用到网络上可自由取得的开放资料,然后使用开放源代码软件来处理分析。资料新闻学希望能服务大众、协助消费者、经理管理人、政治人物来了解固定出现的模式,并根据出现的现像拟定策略。因此,资料新闻学将会使新闻记者在社会上扮演新的角色。

Data-driven journalism: Data-driven journalism, often shortened to "ddj", a term in use since 2009, is a journalistic process based on analyzing and filtering large data sets for the purpose of creating or elevating a news story. Many data-driven stories begin with newly available resources such as open source software, open access publishing and open data, while others are products of public records requests or leaked materials. This approach to journalism builds on older practices, most notably on computer-assisted reporting (CAR) a label used mainly in the US for decades. Other labels for partially similar approaches are "precision journalism", based on a book by Philipp Meyer, published in 1972, where he advocated the use of techniques from social sciences in researching stories.