{"id":45,"date":"2024-04-12T14:29:14","date_gmt":"2024-04-12T11:29:14","guid":{"rendered":"https:\/\/sisu.ut.ee\/digitalenglishstudies\/digital-tools-self-test\/"},"modified":"2024-04-12T14:29:14","modified_gmt":"2024-04-12T11:29:14","slug":"digital-tools-self-test","status":"publish","type":"page","link":"https:\/\/sisu.ut.ee\/digitalenglishstudies\/digital-tools-self-test\/","title":{"rendered":"Digital Tools: Self-Test"},"content":{"rendered":"
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This test is based on the following source:\u00a0Gardiner, E., & Musto, R. (2015). The Digital Humanities: A Primer for Students and Scholars. Cambridge: Cambridge University Press.<\/p>\n<\/div>\n\n

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Have a look at some examples of text-based tools using the\u00a0the Appendix\u00a0from\u00a0Gardiner, E., & Musto, R. (2015).\u00a0The Digital Humanities: A Primer for Students and Scholars<\/i>. Cambridge: Cambridge University Press<\/a>.
The simplest and most familiar example is the document comparison feature in Microsoft Word. The most basic function is taking two different versions of the same document and letting these tools highlight the differences. Most sophisticated tools can perform high-end linguistic analysis, such as tagging parts of speech (POS), creating concordances, collating versions, analysing sentiments and keyword density\/prominence, visualizing patterns, exploring intertextual parallels and modelling topics.<\/td>\n\t\t\t\t\t\t
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On a most basic level, this is simply adding notes or glosses to a document, for instance, putting sticky-note comments on a PDF file for personal use. But it can also be done on web pages and HTML files and shared among a community of readers. This process usually involves a body, an anchor and a marker: that is, the text of the note, the material to which it specifically refers and the way the connection is indicated (e.g. with a circle or underline). These markers are by now common and well-known and they derive from the same notation culture first formulated in medieval manuscripts.<\/td>\n\t\t\t\t\t\t
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Every text in computer format is encoded with tags, whether this is apparent to the user or not. Everything from font and point size, bold, italics and underline, line and paragraph spacing, justification and superscripts are the result of such coding tags. Common formats include RTF, plain text and robustly coded text. Text converters transform all these tags from on format to another so they can be used in different applications. Originally many of these converters were stand-alone applications. Now they are add-ons, or they are embedded within a program so that a user can, for example, create a PDF, an HTML, or an ASCII file from a Microsoft Word document or create an EPUB file directly from an Adobe InDesign file.<\/td>\n\t\t\t\t\t\t
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These tools or applications generally allow users to perform the following operations in text documents: write, search, cut, paste, format, do and undo, check spelling and grammar, outline and generate tables of contents. They can also include capabilities for HTML processing. Among humanities scholars these are among the most commonly used digital tools.<\/td>\n\t\t\t\t\t\t
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When text material is incorporated into scholarly research, it often first needs to be converted into information that can be analysed for patterns. Developing software to derive this information from text has been a major undertaking of several digital humanities efforts. These programs extract data from text according to certain parameters and deliver the data in useful file formats.<\/td>\n\t\t\t\t\t\t
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There are several different types of tools for this process that automatically convert input into a standard text file format.\r\nOptical Character Recognition (ORC). These tools that automatically recognize characters and create documents from digital images of text. This is particularly effective for standard type, such as printed books and magazines, but great advances have been made on recognizing handwritten documents and a vast array of non-Western alphabets.\r\n\r\nHandwriting Recognition (HWR). These tools allow users to transcribe handwriting and produce documents. Their effectiveness for reading manuscript books has evolved greatly over the past decade, but they still require much direct intervention or \u201cinstruction\u201d on the part of a researcher.\r\n\r\nMusic Recognition. These tools can process a printed score and create editable music files.\r\n\r\nSpeech Recognition. Speech recognition software enables a user to automatically convert audio files, such as mp3s, to text. It is particularly useful for personal notes, but also for interviews, and can be applied to both user-created materials and materials downloaded from other sources.<\/td>\n\t\t\t\t\t\t
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These tools take text and create various visual representations of texts and words, such as semantic maps and word clouds.<\/td>\n\t\t\t\t\t\t
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There are several different types of tools that assist a user in converting images or recordings of words into digital information in a standard text file format. There are also tools that facilitate crowdsourcing documents on the web. Through the New York Public Library\u2019s What\u2019s on the Menu?, for example, participants have transcribed more than one million dishes from more than ten thousand menus.\r\n\r\nSpeech to Text Transcription. These tools allow users to transcribe audio files in various formats. Many of these facilitate the process by eliminating the need to alternate between an audio player and a text editor. For instance, a user can load an audio file of a speech and have tools to control the audio on the same page where there is a window for transcribing the text.\r\n\r\nText to Text Transcription. These tools allow users to make transcriptions of the digital images of documents in the same interface, presenting the image alongside a text-editing window. For instance, a user can upload an image of a handwritten letter in one window and transcribe the letter into text format in a window alongside.<\/td>\n\t\t\t\t\t\t
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Question 1 of 4<\/p><\/div>

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Have a look at some examples of outcome based tools using the\u00a0the Appendix\u00a0from\u00a0Gardiner, E., & Musto, R. (2015).\u00a0The Digital Humanities: A Primer for Students and Scholars<\/i>. Cambridge: Cambridge University Press<\/a>.\u00a0<\/p>

Informal and pre- or postpublication communication with fellow scholars to share research questions or results was traditionally carried out through letter-writing, then by phone or fax and in the digital age variably through forums, chat rooms, RSS feeds, wikis, listservs and e-mail. This is a way of discussing or sharing information on the web by uploading posts (discrete, usually brief notices). Some are maintained by groups of scholars involved in similar, related or the same projects.<\/td>\n\t\t\t\t\t\t
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Idea gathering is at the core of much scholarly research, and it is a group or individual technique for generating ideas with the effort focused on creating lists of as many spontaneous ideas as possible without evaluation. It is often used in engineering and business and quite amenable to digital culture, if not to the traditional model of the solitary reflective humanist.<\/td>\n\t\t\t\t\t\t
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In the environment of digital humanities, collaboration is important and sometimes disruptive, again because humanities scholars are generally solitary workers. There are tools that facilitate collaboration on anything from text annotation to reviewing and coding to simple document sharing. Tools are also available to help conference organizers pull together their meeting by topic, date, time and other criteria.<\/td>\n\t\t\t\t\t\t
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These tools provide the means for more efficient communication, particularly on projects. While many scholars still use e-mail as a basic communication method, many other specialized applications have emerged to set up meetings, virtual and video conferencing, social networking, desktop sharing and web-based discussions.<\/td>\n\t\t\t\t\t\t
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Borrowed from the business and publishing worlds, in which schedules and coordination of forces are critical, such tools are available to help researchers manage their projects and organize their materials for more efficient workflow. Examples include OneNote, Pliny, Zotero.<\/td>\n\t\t\t\t\t\t
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The process of publishing materials in digital environments offers many opportunities for sharing research and scholarship in various stages of development. From web publication to print, there are tools to make the process easier for scholars to create volumes, edit content, manage workflows, track manuscripts, manage journal and dissertation submissions, create page layouts, share metadata and create e-books.<\/td>\n\t\t\t\t\t\t
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The opportunity for pre- and postpublication review is one advantage of online publishing. There are a few specialized tools to help with organizing everything from comments to peer review.<\/td>\n\t\t\t\t\t\t
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Most users are familiar with search engines like Google, but others are also available that have special capabilities or features and might sometimes better fit a researcher\u2019s needs.<\/td>\n\t\t\t\t\t\t
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Question 2 of 4<\/p><\/div>

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Have a look at some examples of image and sound based tools using the\u00a0the Appendix\u00a0from\u00a0Gardiner, E., & Musto, R. (2015).\u00a0The Digital Humanities: A Primer for Students and Scholars<\/i>. Cambridge: Cambridge University Press<\/a>.\u00a0<\/p>

Scholars can create images in a digital environment or convert analog images to a digital format. Many software applications are available, often bundled with a computer or a device, such as a digital camera, smartphone or tablet. Some of these tools also provide a drawing environment with graphics editing and styling capabilities.<\/td>\n\t\t\t\t\t\t
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This involves taking a two-dimensional image that has been converted into digital format, making enhancements such as sharpening, changing color balances, saturation and exposure, cropping or straightening; annotating by adding metadata for location, date, content and so forth.<\/td>\n\t\t\t\t\t\t
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To be useful, once data is gathered, it must be inspected, cleaned, transformed and modelled to discover useful information, arrive at conclusions and support decision making. There are tools to assist with qualitative and quantitative data analysis, processing complex phenomena in text and multimedia, grammatical structure and natural language, sequential events and geographical names. Many of these maintain the traditional philological role of humanistic work: identifying, collating, and contextualizing text to properly understand its full meaning.<\/td>\n\t\t\t\t\t\t
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This process creates a mathematical representation of a three-dimensional object that can then be processed to be displayed in two-dimensional space. This processing can include modelling, alternation and animation.<\/td>\n\t\t\t\t\t\t
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This process creates a three-dimensional solid object based on computer-generated models. It is an additive process, that is, layers of material are added successively to achieve the exact computer-designed pattern in real space. This contrasts with traditional machining techniques, such as the lathe or chisel, which involve the removal of material by tooling processes such as drilling or cutting.<\/td>\n\t\t\t\t\t\t
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These control the alternation of digital acoustic and video files and can include enhancement, cleaning, mixing and cutting, annotation and compression. For instance, a sound file of a speech can be enhanced to remove background noise that interferes with its clarity; or background noises might be added for dramatic effect, like the sound of aircraft behind a World War II speech.<\/td>\n\t\t\t\t\t\t
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Question 3 of 4<\/p><\/div>

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Have a look at some examples of data-based tools\u00a0using the\u00a0the Appendix\u00a0from\u00a0Gardiner, E., & Musto, R. (2015).\u00a0The Digital Humanities: A Primer for Students and Scholars<\/i>. Cambridge: Cambridge University Press<\/a>.\u00a0<\/p>

Software systems designed for defining, creating, querying, updating and administering databases \u2013 large collections of data \u2013 from XML-driven databases to dedicated databases.<\/td>\n\t\t\t\t\t\t
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This can be a large part of any scholar\u2019s work. Much of this is done manually, using Database Management Systems to store and manipulate collected data. However, in some disciplines data can be collected through surveys and polls administered electronically. Whatever the method, there are tools to make the collection of data efficient, thorough and systematic.<\/td>\n\t\t\t\t\t\t
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To be useful, once data is gathered, it must be inspected, cleaned, transformed and modelled to discover useful information, arrive at conclusions and support decision making. There are tools to assist with qualitative and quantitative data analysis, processing complex phenomena in text and multimedia, grammatical structure and natural language, sequential events and geographical names. Many of these maintain the traditional philological role of humanistic work: identifying, collating, and contextualizing text to properly understand its full meaning.<\/td>\n\t\t\t\t\t\t
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Once data is gathered, for it to remain useful it must be clearly defined, standardized, quality controlled, stored, monitored, and secured. Both commercial and academic organizations have worked to develop tools that try to insure the preservation and integrity of data. These tools also facilitate querying, managing, enhancing, sharing and publishing data.<\/td>\n\t\t\t\t\t\t
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These applications create visual representations of structured data based on lexical, linguistic, geographical, tonal, temporal and a wide variety of other parameters.<\/td>\n\t\t\t\t\t\t
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These tools deal specifically with geographical data, otherwise called cartography. These tools may use GIS, GPS or other geospatial data to create base maps, overlays, historic maps, interactive maps and maps with timelines and then to share them with users or collaborators.<\/td>\n\t\t\t\t\t\t
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Question 4 of 4<\/p><\/div>

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