1. bookTom 2 (2017): Zeszyt 1 (February 2017)
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Smart Data for Digital Humanities

Data publikacji: 18 Feb 2017
Tom & Zeszyt: Tom 2 (2017) - Zeszyt 1 (February 2017)
Zakres stron: 1 - 12
Otrzymano: 13 Jan 2017
Przyjęty: 16 Jan 2017
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
2543-683X
Pierwsze wydanie
30 Mar 2017
Częstotliwość wydawania
4 razy w roku
Języki
Angielski
Abstract

The emergence of “Big Data” has been a dramatic development in recent years. Alongside it, a lesser-known but equally important set of concepts and practices has also come into being—“Smart Data.” This paper shares the author’s understanding of what, why, how, who, where, and which data in relation to Smart Data and digital humanities. It concludes that, challenges and opportunities co-exist, but it is certain that Smart Data, the ability to achieve big insights from trusted, contextualized, relevant, cognitive, predictive, and consumable data at any scale, will continue to have extraordinary value in digital humanities.

The emergence of “Big Data” has been a dramatic development in recent years. Alongside it, a lesser-known but equally important set of concepts and practices has also come into being—“Smart Data.”

Marcia Lei Zeng is Professor of Library and Information Science at Kent State University. She holds a Ph.D. from the School of Information Sciences at the University of Pittsburgh and an M.A. from Wuhan University in China. Her major research interests include knowledge organization systems (KOS), Linked Data, metadata and markup languages, smart data and big data, database quality control, semantic technologies, and digital humanities. Her scholarly publications consist of more than 90 papers and five books, as well as over 200 national and international conference presentations and invited lectures. Her research projects have received funding from the US National Science Foundation (NSF), Institute of Museum and Library Services (IMLS), OCLC Online Computer Library Center, Fulbright, and other foundations. Dr. Zeng has chaired or served on committees, working groups, and executive boards for the International Federation of Library Associations and Institutions (IFLA), Special Libraries Association (SLA), Association for Information Science and Technology (ASIS&T), the US National Information Standards Organization (NISO), the International Organization for Standardization (ISO), Dublin Core Metadata Initiative (DCMI), International Society for Knowledge Organization (ISKO), and the World Wide Web Consortium (W3C).

<sec id="j_jdis-2017-0001_s_001_s_001_w2aab2b8c32b1b7b1ab1b2b1Aa"><div>WHAT is Smart Data?</div><p>Big data has been characterized by multiple “V”s, with the number of “V”’s still increasing. Volume, Velocity, and Variety have been joined by Variability and Veracity (refer to <a ref-type="fig" href="#j_jdis-2017-0001_fig_001_w2aab2b8c32b1b7b1ab1b2b1b2aAa">Figure 1</a>). Big Data can bring big Value, if used appropriately, because it is now possible to find the hidden patterns, the unexpected correlations, and the surprising connections within large datasets through effective processing (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_008_w2aab2b8c32b1b7b1ab2ab8Aa">Gardner, 2012</a>). The realization of the last “V”, Value, is dependent on “Smart Data,” the “ability to achieve big insights from trusted, contextualized, relevant, cognitive, predictive, and consumable data at any scale, great or small” (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_013_w2aab2b8c32b1b7b1ab2ac13Aa">Kobielus, 2016</a>, p. 8). Simply speaking, Smart Data makes sense out of Big Data. It provides value from harnessing the challenges posed by Volume, Velocity, Variety and Veracity of Big Data, in-turn providing actionable information and improving decision making (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_020_w2aab2b8c32b1b7b1ab2ac20Aa">Sheth, 2014</a>). Smart Data “is the way in which different data sources (including Big Data) are brought together, correlated, analyzed, etc., to be able to feed decision-making and action processes” (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_011_w2aab2b8c32b1b7b1ab2ac11Aa">Iafrate, 2015</a>, p. 13) (<a ref-type="fig" href="#j_jdis-2017-0001_fig_001_w2aab2b8c32b1b7b1ab1b2b1b2aAa">Figure 1</a>).</p><p><figure id="j_jdis-2017-0001_fig_001_w2aab2b8c32b1b7b1ab1b2b1b2aAa" position="float" fig-type="figure"><h2>Figure 1</h2><figCaption><p>Big Data and Smart Data.</p></figCaption><img xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_jdis-2017-0001_fig_001.jpg" src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_001.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220707T020852Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=839eb146587a702bdd3365a1cb8d7aa017f4780e4aa6da692f07fecd95f3a08c" class="mw-100"></img></figure></p></sec><sec id="j_jdis-2017-0001_s_001_s_002_w2aab2b8c32b1b7b1ab1b2b2Aa"><div>WHY Smart Data?</div><p>Data in the 21<sup>st</sup> century, like oil in the 18<sup>th</sup> century, is an untapped asset that holds immense value for those who can learn to extract and use it. “Data is the new oil” (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_010_w2aab2b8c32b1b7b1ab2ac10Aa">Humby, 2006</a>) has become a defining phrase used by many in recent years as the evidence became more and more convincing. “However, in its raw form, data is just like crude oil; it needs to be refined and processed in order to generate real value. Data has to be cleaned, transformed, and analyzed to unlock its hidden potential” (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_021_w2aab2b8c32b1b7b1ab2ac21Aa">TiECON East, 2014</a>). According to a 2012 report on the “digital universe”—a measure of all the digital data created, replicated, and consumed in a single year—“even with a generous estimate, the amount of information in the digital universe that is ‘tagged’ accounts for only about 3% of the digital universe in 2012, and that which is analyzed is half a percent of the digital universe” (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_009_w2aab2b8c32b1b7b1ab2ab9Aa">Gantz & Reinsel, 2012</a>, p. 3). Extracting Value from Big Data characterized by the other “V”s presents both great challenges and inestimable opportunities. Only after it has been tamed through organization and integration processes is such data turned into Smart Data that reflects the research priorities of a particular discipline or field. These tamed results, as Smart Data inquiries, can then be used to provide comprehensive analyses and generate new products and services (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_008_w2aab2b8c32b1b7b1ab2ab8Aa">Gardner, 2012</a>; <a ref-type="bibr" href="#j_jdis-2017-0001_ref_015_w2aab2b8c32b1b7b1ab2ac15Aa">Mukerjee, 2014</a>; <a ref-type="bibr" href="#j_jdis-2017-0001_ref_019_w2aab2b8c32b1b7b1ab2ac19Aa">Schöch, 2013</a>; <a ref-type="bibr" href="#j_jdis-2017-0001_ref_021_w2aab2b8c32b1b7b1ab2ac21Aa">TiECON East, 2014</a>).</p></sec><sec id="j_jdis-2017-0001_s_001_s_003_w2aab2b8c32b1b7b1ab1b2b3Aa"><div>HOW to transform Big Data into Smart Data?</div><p>A look at the topics presented at Smart Data conferences since 2015 may provide a good overview of the technologies involved in Smart Data strategies of achieving big insights from trusted, contextualized, relevant, cognitive, predictive, and consumable data at any scale. These include: cognitive computing, deep learning, machine learning, artificial intelligence, predictive analytics, graph databases, machine intelligence, voice processing, semantic technologies, autonomous vehicles, Big Data, data science, Internet of Things (IoT), text analysis, Resource Description Framework (RDF), knowledge graphs, contextual computing, Linked Data, deep reasoning, ontologies, JSON-LD<fn id="j_jdis-2017-0001_fn_001_w2aab2b8c32b1b7b1ab1b2b3b1b1Aa" symbol="①"><p>JSON-LD is a lightweight Linked Data format</p></fn>, common sense, natural language processing (NLP), and semantic search (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_006_w2aab2b8c32b1b7b1ab2ab6Aa">DATAVERSITY, 2017</a>). These topics are closely interrelated and overlapping. For example, deep learning shows the great potential in natural language processing; cognitive computing uses machine learning to find deep patterns (including those not obviously statistical) within complex, unstructured, and streaming data. Some of the topics have moved beyond the original territory conveyed by these labels for years. For example, “artificial intelligence” is a field that has changed dramatically in the 21<sup>st</sup> century. Meanwhile, the topics of the Smart Data conferences reflect the varied applications of the W3C standards for the Semantic Web, including—but not limited to—RDF, Linked Data, ontologies, graph databases, semantic search, and other semantic technologies (<a ref-type="fig" href="#j_jdis-2017-0001_fig_002_w2aab2b8c32b1b7b1ab1b2b3b2aAa">Figure 2</a>).</p><p><figure id="j_jdis-2017-0001_fig_002_w2aab2b8c32b1b7b1ab1b2b3b2aAa" position="float" fig-type="figure"><h2>Figure 2</h2><figCaption><p>Smart Data Conference 2017 tracks, including combined co-tracks (marked by arrows). Source: Compiled according to the program at <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://smartdata2017.dataversity.net/">http://smartdata2017.dataversity.net/</ext-link>.</p></figCaption><img xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_jdis-2017-0001_fig_002.jpg" src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_002.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220707T020852Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=f6335fccfdfbacd6f6698c92e18e2f5159c14023900430c1430e222d03f1c5c1" class="mw-100"></img></figure></p></sec><sec id="j_jdis-2017-0001_s_001_s_004_w2aab2b8c32b1b7b1ab1b2b4Aa"><div>WHO is making/using Smart Data?</div><p>Efforts to tame Big Data using Smart Data strategies have been made by various experts including natural scientists, engineers, business executives and financial analysts, practitioners of medicine, and government agents. In humanities, the word “Smart Data” is not universally used, even though the approaches can be recognized in many research projects over the last six years. Since 2009, through the <italic>Digging into Data Challenge</italic> program (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://diggingintodata.org/">https://diggingintodata.org/</ext-link>), research funders from more than 10 countries have funded dozens of projects aimed at research questions in the humanities and/or social sciences. The sponsors in the USA include the National Endowment for the Humanities (NEH), the National Science Foundation (NSF), and the Institute of Museum and Library Services (IMLS). Based on the project descriptions of the last three rounds (the most recent, the 4<sup>th</sup> round has not announced final winners as of the date this paper was written), the resources include mainly unstructured data assets originating in ancient times, while structured datasets created in the digital age are also used. The domains and areas of interests are widely spread in the humanities and social sciences. Technologically, large-scale data analyses have been applied to research questions in the fields using the Smart Data approaches (refer to the above “How” section). Methodologically, the projects are interdisciplinary and strive to show how best to tap data in large scale and diverse formats in order to search for key insights while also ensuring access to such data by humanities and social science researchers through new technology-supported tools (<a ref-type="fig" href="#j_jdis-2017-0001_fig_003_w2aab2b8c32b1b7b1ab1b2b4b2aAa">Figure 3</a>).</p><p><figure id="j_jdis-2017-0001_fig_003_w2aab2b8c32b1b7b1ab1b2b4b2aAa" position="float" fig-type="figure"><h2>Figure 3</h2><figCaption><p>Domains/areas of interests, resources, and technologies expressed in the project descriptions of <italic>Digging into Data Challenge</italic> Round 1, 2, and 3, 2009–2013. Source: Compiled based on the project descriptions retrieved from the website at <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://dev.diggingintodata.org/awards">https://dev.diggingintodata.org/awards</ext-link>.</p></figCaption><img xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_jdis-2017-0001_fig_003.jpg" src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_003.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220707T020852Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=1d9333df1cdc846bbee6e8ad300f730e857304809b9c49a9b467e72db32e1600" class="mw-100"></img></figure></p><p>A newly launched nationwide contest encouraging the use of data from <italic>Chronicling America’</italic>s digital repository of historic US newspapers, as well as the new Humanities Access Grant funded by the <a ref-type="bibr" href="#j_jdis-2017-0001_ref_016_w2aab2b8c32b1b7b1ab2ac16Aa">NEH (2016)</a>, are further signs of initiatives taking place at the intersection between the humanities and digital technologies. While the multifaceted landscape of digital humanities is yet to be fully understood, the highly competitive Digital Humanities (DH) conferences could give us more clues. The self-tagged topics of submissions from DH 2013 to DH 2016 conferences reflected a multi-disciplinary nature: <italic>text analysis</italic> was number one in submission count, followed by <italic>historical studies</italic>, <italic>data mining/text mining</italic>, <italic>archives & repositories</italic>, <italic>literary studies</italic>, and <italic>data visualization</italic>. The 2017 count, which separated topics from disciplines, shows that those top topics are joined by <italic>interdisciplinary collaboration</italic> and <italic>corpora and corpus activities</italic>. The disciplines that have more than 100 submissions are: <italic>computer science</italic>, <italic>literary studies</italic>, <italic>library and information science</italic>, <italic>cultural studies</italic>, and <italic>historical studies</italic>. A notable finding is that submissions from <italic>film and media studies</italic> have greatly increased compared to previous years, as have other <italic>non-textual</italic> disciplines. There has also been a steady increase of new authors entering the field and of co-authorship of submissions (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_023_w2aab2b8c32b1b7b1ab2ac23Aa">Weingart, 2016</a>; <a ref-type="bibr" href="#j_jdis-2017-0001_ref_024_w2aab2b8c32b1b7b1ab2ac24Aa">2017</a>). Overall, a wide range of disciplines and approaches are seen in the humanities to reach “bigger smart data” or “smarter big data” (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_019_w2aab2b8c32b1b7b1ab2ac19Aa">Schöch, 2013</a>), as demonstrated by the outcomes presented at digital humanities conferences, the government funded research projects, and new initiatives and publications all over the world in the past six years.</p></sec><sec id="j_jdis-2017-0001_s_001_s_005_w2aab2b8c32b1b7b1ab1b2b5Aa"><div>WHERE is the distinctive mark in Digital Humanities?</div><p>It might be natural that, when thinking about digital humanities in the data-intensive research projects, people would look for distinctive marks toward the direction of technologies. However, as <a ref-type="bibr" href="#j_jdis-2017-0001_ref_019_w2aab2b8c32b1b7b1ab2ac19Aa">Schöch (2013)</a> pointed out, the distinctive mark of Big Data in the humanities seemed to be a methodological shift rather than a primarily technological one. Further scrutiny of the methodological shift in humanities highlights the role of Big Data and Smart Data for every field of knowledge. In short, the relationship between Big Data and Smart Data can be characterized as “what it is” and “what it is for” (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_011_w2aab2b8c32b1b7b1ab2ac11Aa">Iafrate, 2015</a>). This view of turning Big Data into Smart Data brings us back to the well-known Data-Information-Knowledge-Wisdom (DIKW) pyramid (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_025_w2aab2b8c32b1b7b1ab2ac25Aa">Zeleny, 1987</a>; <a ref-type="bibr" href="#j_jdis-2017-0001_ref_001_w2aab2b8c32b1b7b1ab2ab1Aa">Ackoff, 1988</a>) which represents the most basic strategy for understanding a world that far exceeds our brains’ capacity by filtering, winnowing, and otherwise reducing it to something more meaningful (<a ref-type="fig" href="#j_jdis-2017-0001_fig_004_w2aab2b8c32b1b7b1ab1b2b5b2aAa">Figure 4</a>).</p><p><figure id="j_jdis-2017-0001_fig_004_w2aab2b8c32b1b7b1ab1b2b5b2aAa" position="float" fig-type="figure"><h2>Figure 4</h2><figCaption><p>The Data-Information-Knowledge-Wisdom (DIKW) pyramid.</p></figCaption><img xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_jdis-2017-0001_fig_004.jpg" src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_004.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220707T020852Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=0843848a35d1f8dab06df7114e25d55dfad19e59c917f6380f8206db023b761a" class="mw-100"></img></figure></p><p>Nevertheless, the Smart Data approach is not simply a replication of the DIKW path because Smart Data is based on Big Data’s methodology, which assumes the ability to reveal the <italic>unknown-unknowns</italic> (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_004_w2aab2b8c32b1b7b1ab2ab4Aa">Borne, 2013</a>) instead of taking the approach that one knows to do something in order to prove or disapprove the <italic>known-unknowns</italic> (<a ref-type="fig" href="#j_jdis-2017-0001_fig_005_w2aab2b8c32b1b7b1ab1b2b5b4aAa">Figure 5</a>). This is a fundamental advancement of Smart Data that distinguishes it from other approaches that follow the more traditional blueprint of hypothesizing, modeling, and testing (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_002_w2aab2b8c32b1b7b1ab2ab2Aa">Anderson, 2008</a>).</p><p><figure id="j_jdis-2017-0001_fig_005_w2aab2b8c32b1b7b1ab1b2b5b4aAa" position="float" fig-type="figure"><h2>Figure 5</h2><figCaption><p>The unknown-unknowns.</p></figCaption><img xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_jdis-2017-0001_fig_005.jpg" src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_005.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220707T020852Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=b217eeaa0713dcb7ecff91119821dd7e4382dc460e7f5d0c26a8e4eec0ad221f" class="mw-100"></img></figure></p><p>One good example of revealing the unknown-unknowns through Smart Data is the research project “A network framework of cultural history” published in <italic>Science</italic> and also on <italic>Nature Video</italic> (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_017_w2aab2b8c32b1b7b1ab2ac17Aa">Schich et al., 2014a</a>; <a ref-type="bibr" href="#j_jdis-2017-0001_ref_018_w2aab2b8c32b1b7b1ab2ac18Aa">2014b</a>). A multidisciplinary research team provided a macroscopic perspective of the cultural history of Europe and North America across 3,000 years, using simple—but large—datasets of the birth and death locations of more than 150,000 notable individuals, which revealed previously undocumented human mobility patterns and cultural attraction dynamics. Incorporating this analyzed data, the 3,000 year, large-scale patterns in European and American cultural life are visualized and brought to life, enlightening the formation of intellectual and cultural centers, the rising and crumbling of empires, and other influential factors, all beyond the scope of specific events or narrow time intervals (Recommend watching the video at <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://www.youtube.com/watch?v=4gIhRkCcD4U">https://www.youtube.com/watch?v=4gIhRkCcD4U</ext-link>). The value of the knowledge is incredible and the big insights are achieved from trusted, contextualized, relevant, cognitive, predictive, and consumable data (the original sources are structured data from Freebase (now Wikidata), the General Artist Lexicon (AKL), and the Getty Union List of Artist Names (ULAN)) (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_017_w2aab2b8c32b1b7b1ab2ac17Aa">Schich et al., 2014a</a>). This example not only demonstrates the potential of the Smart Data approach in sociology, anthropology, and history in general but also indicates a significant methodological advancement in the humanities.</p></sec><sec id="j_jdis-2017-0001_s_001_s_006_w2aab2b8c32b1b7b1ab1b2b6Aa"><div>WHICH DATA can be found in supporting research and scholarship in Digital Humanities?</div><p>When putting Big Data and Smart Data into the context of digital humanities, a key concept that needs to be agreed upon is the use of the term “data.” In the digital age, it is common for people to only think of data in terms of digitally available formats. The connection between digital data and data analytics is correct, but we need to fully understand that the terms “data” and “digital data” are not equivalent. The types of data are also not limited to quantitative data. The Reference Model for an Open Archival Information System (OAIS) defined data as a “reinterpretable representation of information in a formalized manner suitable for communication, interpretation, or processing” while offering examples of data as: a sequence of bits, a table of numbers, the characters on a page, the recording of sounds made by a person speaking, or a moon rock specimen. This definition of “data” was given within the context of “information,” which is “Any type of knowledge that can be exchanged. In an exchange, it is represented by data” (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_005_w2aab2b8c32b1b7b1ab2ab5Aa">Consultative Committee for Space Data Systems, 2012</a>, p. 1–10 and p. 1–12). After a comprehensive review of the definitions and terminology for “data” in her book titled <italic>Big data</italic>, <italic>little data</italic>, <italic>no data: Scholarship in the networked world</italic>, <a ref-type="bibr" href="#j_jdis-2017-0001_ref_003_w2aab2b8c32b1b7b1ab2ab3Aa">Borgman (2015)</a> presented an overarching summary that “data are representations of observations, objects, or other entities used as evidence of phenomena for the purpose of research or scholarship” (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_003_w2aab2b8c32b1b7b1ab2ab3Aa">Borgman, 2015</a>, p. 28).</p><p>In the data resources that are usually served through libraries, archives, and museums (LAMs) and other information institutions, the types of data, which are available in the largest quantity, have the diversity in type, nature, and quality, and are the most challenging to process, belong to <italic>unstructured data</italic> found in documents and other information-bearing objects (textual or non-textual, digitized or non-digitized) in all kinds of formats (examples can be found in <a ref-type="fig" href="#j_jdis-2017-0001_fig_003_w2aab2b8c32b1b7b1ab1b2b4b2aAa">Figure 3</a>). These primary data resources are held in special collections, archives, oral history files, annual reports, provenance indexes, and inventories, to name just a few. The nature of such data is quite different from, for instance, that of the data used by the “digital universe” that “is made up of images and videos on mobile phones uploaded to YouTube, digital movies populating the pixels of our high-definition TVs, banking data swiped in an ATM, security footage at airports and major events such as the Olympic Games, subatomic collisions recorded by the Large Hadron Collider at CERN, transponders recording highway tolls, voice calls zipping through digital phone lines, and texting as a widespread means of communications” (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_009_w2aab2b8c32b1b7b1ab2ab9Aa">Gantz & Reinsel, 2012</a>, p. 1). Such a “digital universe” may not be the major or only source for humanities researchers.</p><p>In fact, one primary challenge in applying the Smart Data approach to digital humanities is the availability of data resources for those in need of historical data that one could not obtain through Web crawling or scraping. There is no doubt that Smart Data approaches that have been tested and implemented in business and industry can be applied to the digital humanities. Nevertheless, how to “datafy” the unstructured data (i.e. turn the heritage materials into not only machine-readable but also machine-processable resources, and reconstruct through digitization pipelines) before the researchers can make use of data analytics technologies? This fundamental question might explain why, for digital humanities, the Smart Data approach emphasizes the organization and integration processes to transform unstructured data to structured and semi-structured data (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_012_w2aab2b8c32b1b7b1ab2ac12Aa">Kaplan, 2015</a>; <a ref-type="bibr" href="#j_jdis-2017-0001_ref_014_w2aab2b8c32b1b7b1ab2ac14Aa">Mayer-Schönberger & Cukier 2013; Schöch, 2013</a>).</p><p>In addition to the <italic>unstructured data</italic> discussed above, LAMs and other information institutions also provide tremendous opportunities for humanities researchers to dig nuggets of gold from <italic>semi-structured data</italic> (examples include the intellectual works encoded following the Text Encoding Initiative (TEI) guidelines, archival finding aids, value-added or tagged resources that exist in all kinds of formats, and the unstructured portions of otherwise structured datasets) as well as <italic>structured data</italic> (including bibliographies, indexing and abstracting databases, citation indexes, catalogs of all kinds, special collection portals, metadata repositories, curated research datasets, and name authorities) (<a ref-type="fig" href="#j_jdis-2017-0001_fig_006_w2aab2b8c32b1b7b1ab1b2b6b5aAa">Figure 6</a>).</p><p><figure id="j_jdis-2017-0001_fig_006_w2aab2b8c32b1b7b1ab1b2b6b5aAa" position="float" fig-type="figure"><h2>Figure 6</h2><figCaption><p>Examples of the data resources provided by libraries, archives, and museums (LAMs).</p></figCaption><img xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_jdis-2017-0001_fig_006.jpg" src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_006.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220707T020852Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=45e4bfac0885d877fda76df06b5427befcb68192ae831eb63b01de08f456a798" class="mw-100"></img></figure></p><p>These datasets might be relatively small in volume and have limited heterogeneity in comparison with Big Data, but they are clean, explicit, trusted, and value-added, and their creation is governed mostly by human decisions. More promisingly, they are among the resources most likely to be freely accessible (non-proprietary and non-commercial). These make them treasures for all humanities researchers and beyond. In his speech titled “Contextual Computing with Knowledge Graphs and the Web of Entities” at Smart Data Online 2016, Richard Wallis, a well-known pioneer of the library community’s Linked Open Data movement, provided his vision of <italic>contextual computing</italic>, in which he listed elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task, and goal. The revolutionary work of WorldCat Linked Data and the WorldCat Entities experiment at OCLC are among the successful cases. By providing millions of entities of intellectual works, places, concepts, persons, organizations, events, and other types of tamed data together, the WorldCat Entities shows how the structured data provided by LAMs can enrich knowledge graphs and Linked Open Data datasets infinitely (<a ref-type="bibr" href="#j_jdis-2017-0001_ref_022_w2aab2b8c32b1b7b1ab2ac22Aa">Wallis, 2016</a>).</p><p>In the processes that transform unstructured data to structured and semi-structured data, the Smart Data strategy drives data service providers to aim at machine-<italic>understandable</italic>, -<italic>processable</italic>, and -<italic>actionable</italic> (instead of merely machine-<italic>readable</italic>) data, to provide accurate data in the processes of interlinking, citing, transferring, rights-permission management, use and reuse, and to enable both one-to-many usages and high efficiency processing of data for digital humanities.</p></sec></sec><sec id="j_jdis-2017-0001_s_002_w2aab2b8c32b1b7b1ab1b3Aa"><div>Conclusion</div><p>Today, advanced technologies, under the umbrella of Big Data and Smart Data, allow researchers of the humanities to join the mainstream of the digital age with new abilities as never before: to access and reuse large volumes of diverse data; to unearth patterns and connections formerly hidden from view; to reconstruct the past; to discover the impact and value of qualitative and quantitative variables in both real and virtual environments; and to bring the knowledge of the complex intricacies of human society to light. Challenges and opportunities co-exist, but it is certain that Smart Data, the ability to achieve big insights from trusted, contextualized, relevant, cognitive, predictive, and consumable data at any scale, will continue to have extraordinary value in digital humanities.</p></sec></div></div></div></div><div id="pane-4" class="SeriesTab_card__26XnC SeriesTab_tab-pane__3pc7y card tab-pane" role="tabpanel" aria-labelledby="tab-4"><div class="SeriesTab_card-header__1DTAS card-header d-md-none pl-0" role="tab" id="heading-4"><h4 class="mb-0"><a data-toggle="collapse" href="#collapse-4" data-parent="#content" aria-expanded="false" aria-controls="collapse-4" style="padding:24px 0">Ilustracje i tabele<svg aria-hidden="true" focusable="false" data-prefix="fas" data-icon="chevron-down" class="svg-inline--fa fa-chevron-down fa-w-14 " role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path fill="currentColor" d="M207.029 381.476L12.686 187.132c-9.373-9.373-9.373-24.569 0-33.941l22.667-22.667c9.357-9.357 24.522-9.375 33.901-.04L224 284.505l154.745-154.021c9.379-9.335 24.544-9.317 33.901.04l22.667 22.667c9.373 9.373 9.373 24.569 0 33.941L240.971 381.476c-9.373 9.372-24.569 9.372-33.942 0z"></path></svg></a></h4></div><div id="collapse-4" class="SeriesTab_seriesTabCollapse__2csiF collapse" role="tabpanel" aria-labelledby="heading-4" data-parent="#content"><div class="SeriesTab_series-tab-body__1tZ1H SeriesTab_card-body__31JEh card-body Article_figures-tables__2SC5X"><figure><h4 class="mb-4"></h4><img src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_007.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220707T020852Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=01194de9e8f77d8954ceeec864384ebf0088ceb36f1d7e6f52bb6422f0ddf4e6" class="mw-100"/><figcaption class="fw-500"></figcaption></figure><figure><h4 class="mb-4">Figure 1</h4><img src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_001.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220707T020852Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=839eb146587a702bdd3365a1cb8d7aa017f4780e4aa6da692f07fecd95f3a08c" alt="Big Data and Smart Data." class="mw-100"/><figcaption class="fw-500">Big Data and Smart Data.</figcaption></figure><figure><h4 class="mb-4">Figure 2</h4><img src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_002.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220707T020852Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=f6335fccfdfbacd6f6698c92e18e2f5159c14023900430c1430e222d03f1c5c1" alt="Smart Data Conference 2017 tracks, including combined co-tracks (marked by arrows). Source: Compiled according to the program at http://smartdata2017.dataversity.net/." class="mw-100"/><figcaption class="fw-500">Smart Data Conference 2017 tracks, including combined co-tracks (marked by arrows). Source: Compiled according to the program at http://smartdata2017.dataversity.net/.</figcaption></figure><figure><h4 class="mb-4">Figure 3</h4><img src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_003.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220707T020852Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=1d9333df1cdc846bbee6e8ad300f730e857304809b9c49a9b467e72db32e1600" alt="Domains/areas of interests, resources, and technologies expressed in the project descriptions of Digging into Data Challenge Round 1, 2, and 3, 2009–2013. Source: Compiled based on the project descriptions retrieved from the website at https://dev.diggingintodata.org/awards." class="mw-100"/><figcaption class="fw-500">Domains/areas of interests, resources, and technologies expressed in the project descriptions of Digging into Data Challenge Round 1, 2, and 3, 2009–2013. Source: Compiled based on the project descriptions retrieved from the website at https://dev.diggingintodata.org/awards.</figcaption></figure><figure><h4 class="mb-4">Figure 4</h4><img src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_004.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220707T020852Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=0843848a35d1f8dab06df7114e25d55dfad19e59c917f6380f8206db023b761a" alt="The Data-Information-Knowledge-Wisdom (DIKW) pyramid." class="mw-100"/><figcaption class="fw-500">The Data-Information-Knowledge-Wisdom (DIKW) pyramid.</figcaption></figure><figure><h4 class="mb-4">Figure 5</h4><img src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_005.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220707T020852Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=b217eeaa0713dcb7ecff91119821dd7e4382dc460e7f5d0c26a8e4eec0ad221f" alt="The unknown-unknowns." class="mw-100"/><figcaption class="fw-500">The unknown-unknowns.</figcaption></figure><figure><h4 class="mb-4">Figure 6</h4><img src="https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_006.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220707T020852Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=45e4bfac0885d877fda76df06b5427befcb68192ae831eb63b01de08f456a798" alt="Examples of the data resources provided by libraries, archives, and museums (LAMs)." class="mw-100"/><figcaption class="fw-500">Examples of the data resources provided by libraries, archives, and museums (LAMs).</figcaption></figure></div></div></div><div id="reference" class="SeriesTab_card__26XnC SeriesTab_tab-pane__3pc7y card tab-pane" role="tabpanel" aria-labelledby="tab-5"><div class="SeriesTab_card-header__1DTAS card-header d-md-none pl-0" role="tab" id="heading-5"><h4 class="mb-0"><a data-toggle="collapse" href="#collapse-5" data-parent="#content" aria-expanded="false" aria-controls="collapse-5" style="padding:24px 0">Referencje<svg aria-hidden="true" focusable="false" data-prefix="fas" data-icon="chevron-down" class="svg-inline--fa fa-chevron-down fa-w-14 " role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><path fill="currentColor" d="M207.029 381.476L12.686 187.132c-9.373-9.373-9.373-24.569 0-33.941l22.667-22.667c9.357-9.357 24.522-9.375 33.901-.04L224 284.505l154.745-154.021c9.379-9.335 24.544-9.317 33.901.04l22.667 22.667c9.373 9.373 9.373 24.569 0 33.941L240.971 381.476c-9.373 9.372-24.569 9.372-33.942 0z"></path></svg></a></h4></div><div id="collapse-5" class="SeriesTab_seriesTabCollapse__2csiF collapse" role="tabpanel" aria-labelledby="heading-5" data-parent="#content"><div class="SeriesTab_series-tab-body__1tZ1H SeriesTab_card-body__31JEh card-body"><p class="Article_refData__1fofs"><span class="Article_d-block__2MPqH"><ref id="j_jdis-2017-0001_ref_001_w2aab2b8c32b1b7b1ab2ab1Aa"><mixed-citation>Ackoff, R.L. 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The main areas of interest are science of science, evidence-based policymaking, research evaluation, computational social science, and scientometrics/bibliometrics/altmetrics/ informetrics. Emphasis is given to research that focuses on data, analytics, and knowledge discovery, and supports decision making and science policy. This includes modeling, innovation, data security, media and communications, and social development. Topics may include studies of metadata or full content data, text or non-textural data, structured or non-structural data, domain-specific or cross-domain data, and dynamic or interactive data. \u003c/P\u003e \u003cP\u003eSpecific topic areas may include (but are not limited to): \u003c/P\u003e \u003cUL\u003e \u003cP\u003e\u003c/P\u003e \u003cLI\u003eKnowledge organization \u003c/LI\u003e \u003cLI\u003eKnowledge discovery and data mining \u003c/LI\u003e \u003cLI\u003eKnowledge integration and fusion \u003c/LI\u003e \u003cLI\u003eSemantic Web \u003c/LI\u003e \u003cLI\u003eScience of science \u003c/LI\u003e \u003cLI\u003eBibliometrics and scientometrics \u003c/LI\u003e \u003cLI\u003eAnalytic and diagnostic informetrics \u003c/LI\u003e \u003cLI\u003eCompetitive intelligence \u003c/LI\u003e \u003cLI\u003ePredictive analysis \u003c/LI\u003e \u003cLI\u003eSocial network analysis and metrics \u003c/LI\u003e \u003cLI\u003eSemantic and interactively analytic retrieval \u003c/LI\u003e \u003cLI\u003eEvidence-based policy analysis \u003c/LI\u003e \u003cLI\u003eIntelligent knowledge production \u003c/LI\u003e \u003cLI\u003eKnowledge-driven workflow management and decision-making \u003c/LI\u003e \u003cLI\u003eKnowledge-driven collaboration and its management \u003c/LI\u003e \u003cLI\u003eDomain knowledge infrastructure with knowledge fusion and analytics \u003c/LI\u003e \u003cLI\u003eTraining for data \u0026amp; information scientists \u003c/LI\u003e \u003cLI\u003eDevelopment of data and information services \u003c/LI\u003e \u003cP\u003e\u003c/P\u003e\u003c/UL\u003e \u003cP\u003e\u003c/P\u003e \u003cP\u003eJDIS publishes theoretical and empirical work. Systematic reviews are welcome and applied research in development of advanced methods, services, and best practices is also an important part. But simple application of established informetrics on a specific research field or country is out of the scope.\u003cBR\u003e\u003cBR\u003eWelcome to submit your papers to JDIS. \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eWhy subscribe and read\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003eJDIS is the first and only English journal from China in Library and Information Science and related fields. With an aim to disseminate the cutting-edge research in these fields, it is devoted to the study and application of the theories, methods, techniques, services, and infrastructural facilities using big data to support knowledge discovery for decision and policy making. The basic emphasis is big data-based, analytics centered, knowledge discovery driven, and decision making supporting. JDIS has gathered a big body of high profile experts across the world who contribute their research to the journal. The international authors account for around 62% in its first publication year (2016). \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eWhy submit\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003eJDIS is the first and only English journal from China in Library and Information Science and related fields. It owns a number of world front-line scholars as editorial board members or reviewers. The turnaround time on average for a manuscript from submission to final decision is less than two and a half months. \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eArchiving\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003eSciendo archives the contents of this journal in \u003cA href=\"https://www.portico.org/\"\u003ePortico\u003c/A\u003e- digital long-term preservation service of scholarly books, journals and collections. \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003ePlagiarism Policy\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003eThe editorial board is participating in a growing community of \u003cA href=\"https://www.crossref.org/services/similarity-check/\"\u003eSimilarity Check System's\u003c/A\u003e users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. \u003c/P\u003e"},{"type":"editorial","language":"English","textformat":null,"content":"\u003cP\u003e\u003cSTRONG\u003eCo-Editors-in-Chief\u003c/STRONG\u003e\u003cBR\u003eRonald Rousseau\u003cBR\u003eUniversity of Leuven, and University of Antwerp, Belgium \u003c/P\u003e \u003cP\u003eLiying Yang\u003cBR\u003eNational Science Library, the Chinese Academy of Sciences, China \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eVice-editors\u003c/STRONG\u003e\u003cBR\u003eZhesi Shen\u003cBR\u003eNational Science Library, the Chinese Academy of Sciences, China \u003c/P\u003e \u003cP\u003eJohan Bollen \u003cBR\u003eIndiana University, USA \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eManaging Editor\u003c/STRONG\u003e\u003cBR\u003ePing Meng\u003cBR\u003eNational Science Library, the Chinese Academy of Sciences, China \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eEditorial Boards\u003c/STRONG\u003e\u003cBR\u003eJudit Bar-Ilan (1958-2019)\u003cBR\u003eBar-Ilan University, Israel \u003c/P\u003e \u003cP\u003eChristine L. Borgman\u003cBR\u003eUniversity of California, Los Angeles, USA \u003c/P\u003e \u003cP\u003eKevin Boyack\u003cBR\u003eSciTech Strategies Inc., USA \u003c/P\u003e \u003cP\u003eCong Cao\u003cBR\u003eUniversity of Nottingham Ningbo, China \u003c/P\u003e \u003cP\u003eDar-Zen Chen\u003cBR\u003eNational Taiwan University, Taiwan, China \u003c/P\u003e \u003cP\u003eCinzia Daraio\u003cBR\u003eSapienza University of Rome, Italy \u003c/P\u003e \u003cP\u003eNees-Jan van Eck\u003cBR\u003eLeiden University, the Netherlands \u003c/P\u003e \u003cP\u003eTove Faber Frandsen,\u003cBR\u003eUniversity of Southern Denmark, Denmark \u003c/P\u003e \u003cP\u003eJane Greenberg,\u003cBR\u003eDrexel University, USA \u003c/P\u003e \u003cP\u003eRobin Haunschild,\u003cBR\u003eMax Plank Institute for Solid State Research, Germany \u003c/P\u003e \u003cP\u003eXiaojun Hu,\u003cBR\u003eZhejiang University, China \u003c/P\u003e \u003cP\u003eTao Jia\u003cBR\u003eSouthwest University, China \u003c/P\u003e \u003cP\u003eGuangjian Li\u003cBR\u003ePeking University, China \u003c/P\u003e \u003cP\u003eYuelin Li\u003cBR\u003eNankai University, China \u003c/P\u003e \u003cP\u003eWei Liu\u003cBR\u003eLibrary of Shanghai, China \u003c/P\u003e \u003cP\u003eWei Lu\u003cBR\u003eWuhan University, China \u003c/P\u003e \u003cP\u003eXiaobin Lu\u003cBR\u003eRenmin University, China \u003c/P\u003e \u003cP\u003eHamid R. Jamali,\u003cBR\u003eCharles Sturt University, Australia \u003c/P\u003e \u003cP\u003eAlberto Martin-Martin,\u003cBR\u003eUniversity of Granada, Spain \u003c/P\u003e \u003cP\u003eEustache Megnigbeto,\u003cBR\u003eBureau of Studies and Research in Information Science, Benin \u003c/P\u003e \u003cP\u003eOlga Moskaleva,\u003cBR\u003eSaint-Petersburt State University, Russia \u003c/P\u003e \u003cP\u003eEd Noyons\u003cBR\u003eUniversity of Leiden, the Netherlands \u003c/P\u003e \u003cP\u003eJosé Miguel Baptista Nunes,\u003cBR\u003eSun Yat-Sen University, China \u003c/P\u003e \u003cP\u003eHan Woo Park,\u003cBR\u003eYeung Nam University, South Korea \u003c/P\u003e \u003cP\u003eQing Qian,\u003cBR\u003eInstitute of Medical Information/Medical Library, CAMS, China \u003c/P\u003e \u003cP\u003eJian Qin,\u003cBR\u003eSyracuse University, USA \u003c/P\u003e \u003cP\u003eElias Sanz-Casado,\u003cBR\u003eUniversity Carlos III de Madrid, Spain \u003c/P\u003e \u003cP\u003eZhesi Shen,\u003cBR\u003eNational Science Library, the Chinese Academy of Scienes, China \u003c/P\u003e \u003cP\u003eGunnar Sivertsen,\u003cBR\u003eNordic Institute for Studies in Innovation, Research and Education, Norway \u003c/P\u003e \u003cP\u003eNeil Smalheiser,\u003cBR\u003eUniversity of Illinois at Chicago, USA \u003c/P\u003e \u003cP\u003eXinning Su,\u003cBR\u003eNanjing University, China \u003c/P\u003e \u003cP\u003eSugimoto Shigeo,\u003cBR\u003eUniversity of Tsukuba, Japan \u003c/P\u003e \u003cP\u003eTan Sun,\u003cBR\u003eAgriculture Information Institute, CAAS, China \u003c/P\u003e \u003cP\u003eLi Tang,\u003cBR\u003eFudan University, China \u003c/P\u003e \u003cP\u003eMike Thelwall,\u003cBR\u003eUniversity of Wolverhampton, UK \u003c/P\u003e \u003cP\u003eLili Wang,\u003cBR\u003eMaastricht University, the Netherlands \u003c/P\u003e \u003cP\u003eYuefen Wang,\u003cBR\u003eTianjin Normal University, China \u003c/P\u003e \u003cP\u003eFang Wang,\u003cBR\u003eNankai University, China \u003c/P\u003e \u003cP\u003eJevin West,\u003cBR\u003eWashington University, USA \u003c/P\u003e \u003cP\u003eDietmar Wolfram,\u003cBR\u003eUniversity of Wisconsin-Milwaukee, USA \u003c/P\u003e \u003cP\u003eDan Wu,\u003cBR\u003eWuhan University, China \u003c/P\u003e \u003cP\u003eJinshan Wu,\u003cBR\u003eBeijing Normal University, China \u003c/P\u003e \u003cP\u003eYishan Wu,\u003cBR\u003eChinese Academy of Science and Technology for Development, China \u003c/P\u003e \u003cP\u003eErjia Yan,\u003cBR\u003eDrexel University, USA \u003c/P\u003e \u003cP\u003eGuoliang Yang,\u003cBR\u003eInstitute of Science and Development, Chinese Academy of Sciences, China \u003c/P\u003e \u003cP\u003eYing Ye (Fred Y. Ye),\u003cBR\u003eNanjing University, China \u003c/P\u003e \u003cP\u003eMarcia Lei Zeng,\u003cBR\u003eKent State University, USA \u003c/P\u003e \u003cP\u003eLin Zhang,\u003cBR\u003eWuhan University, China \u003c/P\u003e \u003cP\u003eZhixiong Zhang,\u003cBR\u003eNational Science Library, CAS, China \u003c/P\u003e \u003cP\u003eDangzhi Zhao,\u003cBR\u003eUniversity of Alberta, Canada \u003c/P\u003e \u003cP\u003eYuxiang Zhao,\u003cBR\u003eRenmin University, China \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eContact:\u003cBR\u003eEditorial Office\u003cBR\u003e\u003c/STRONG\u003eNational Science Library, Chinese Academy of Sciences\u003cBR\u003e33 Beisihuan Xilu, Haidian, Beijing 100190, P.R. 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The mechanism follows the guidelines from the Committee on Publication Ethics (COPE), which can be accessed at \u003cA href=\"https://publicationethics.org/retraction-guidelines\"\u003ehttps://publicationethics.org/retraction-guidelines\u003c/A\u003e \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eInstructions for Authors\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eArticle Types\u003c/STRONG\u003e \u003c/P\u003e \u003cUL\u003e \u003cP\u003e\u003c/P\u003e \u003cLI\u003eResearch Articles: original research work or a comprehensive and in-depth analysis of a topic (around 5,000 to 8,000 words) \u003c/LI\u003e \u003cLI\u003eReview Articles: manuscripts that provide a novel synthesis of a research area (around 5,000 to 10,000 words) \u003c/LI\u003e \u003cLI\u003eResearch notes: short data-based discussions of research findings of interest to the wider community. \u003c/LI\u003e \u003cLI\u003ePerspectives: forward-looking viewpoints that advocate important future directions in the field (around 2,000 to 3,000 words) \u003c/LI\u003e \u003cLI\u003eCommentaries: call attention to published articles, books, or reports (around 1,000 to 2,000 words) \u003c/LI\u003e \u003cLI\u003eLetters to the Editor: comments on previously published articles in JDIS (around 100 to 2,000 words) \u003c/LI\u003e \u003cLI\u003eOpinions: pieces to present ideas, discuss recent books, propose arguments, or initiate debates (around 1,000 to 3,000 words) \u003c/LI\u003e \u003cLI\u003eEditorial: editors or guest editors may occasionally provide background information on an issue or an event \u003c/LI\u003e \u003cP\u003e\u003c/P\u003e\u003c/UL\u003e\u003cSTRONG\u003e\u003c/STRONG\u003e \u003cP\u003e\u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eSubmission guidelines\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003eJDIS does not require specific formats for first submissions. 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In addition, we suggest line numbering as well. \u003c/LI\u003e \u003cLI\u003eFigures and tables should be placed close to where they are first referenced. \u003c/LI\u003e \u003cLI\u003eReferences should adhere to \u003cA href=\"https://apastyle.apa.org/style-grammar-guidelines/references/examples/journal-article-references\"\u003eAPA\u003c/A\u003e (American Psychological Association) style \u003c/LI\u003e \u003cLI\u003eAcknowledgments, author contribution statements, competing interest statements, funding information, data availability statements, and appendices should be included at the end of the manuscript. \u003c/LI\u003e \u003cP\u003e\u003c/P\u003e\u003c/UL\u003e \u003cP\u003e\u003cSTRONG\u003eOpen Access Statement\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003eThe journal is an Open Access journal that allows a free unlimited access to all its contents without any restrictions upon publication to all users. \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003e\u003cA href=\"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/JDIS/Open_Access_License.pdf\"\u003eOpen Access License\u003c/A\u003e\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eCopyright policy\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003eAll contributions published in \u003cEM\u003eJDIS\u003c/EM\u003e will be under a Creative Commons Attributions license, with the default as CC-BY. \u003c/P\u003e \u003cP\u003e\u003cSTRONG\u003eAPC Policy\u003c/STRONG\u003e \u003c/P\u003e \u003cP\u003eTo publish in JDIS, authors are not required to pay an Article Processing Charge. \u003c/P\u003e"}]}],"metrics":{"metric":[{"name":"Cite Score","value":1.7},{"name":"SCImago Journal Rank","value":0.565},{"name":"Source Normalized Impact per Paper","value":0.622}]},"pricing":null,"publicationFrequency":{"frequency":"4","period":"YEAR"},"permissions":null,"contributors":"","serial":null,"publishMonth":"2","publishYear":"2017","tableCount":null,"figureCount":null,"refCount":null,"keywords":[],"figures":null,"tables":null,"planPubDates":[],"epubLink":null,"pdfLink":null,"coverImage":"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/cover-image.jpg","coverImageOriginal":"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/cover-image-original.jpg","pdfFiles":[],"parentObjectId":"600935d7f1433668c24d021f","isParentConference":false,"relatedTitles":null,"forAuthors":null,"nextPackageId":"600652d5e797941b18f30e37","prevPackageId":null,"parentName":"Volume 2 (2017): Issue 1 (February 2017)","grandParentId":"6005ae9ce797941b18f23840","grandParentName":"Journal of Data and Information Science","isGrandParentConference":false,"publisherName":"De Gruyter Open","publisherLocation":null,"nextMap":{"id":{"timestamp":1611027157,"date":"2021-01-19T03:32:37.000+00:00"},"doi":"10.1515/jdis-2017-0002"},"prevMap":{"doi":null},"counter":0,"apaString":"Zeng,M.(2017).\u003carticle-title\u003eSmart Data for Digital Humanities\u003c/article-title\u003e. 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Human Systems Management, 7(1), 59–70.\u003c/mixed-citation\u003e\u003celement-citation publication-type=\"journal\" publication-format=\"print\"\u003e\u003cname\u003e\u003csurname\u003eZeleny\u003c/surname\u003e\u003cgiven-names\u003eM.\u003c/given-names\u003e\u003c/name\u003e\u003cyear\u003e1987\u003c/year\u003e\u003carticle-title\u003eManagement support systems: Towards integrated knowledge management\u003c/article-title\u003e\u003csource\u003eHuman Systems Management\u003c/source\u003e\u003cvolume\u003e7\u003c/volume\u003e\u003cissue\u003e1\u003c/issue\u003e\u003cfpage\u003e59\u003c/fpage\u003e\u003clpage\u003e70\u003c/lpage\u003e\u003cdgdoi:pub-id xmlns:dgdoi=\"http://degruyter.com/resources/doi-from-crossref\" pub-id-type=\"doi\"\u003e10.3233/HSM-1987-7108\u003c/dgdoi:pub-id\u003e\u003c/element-citation\u003e\u003c/ref\u003e"}],"pdfUrl":"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/10.1515_jdis-2017-0001.pdf?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026X-Amz-Date=20220707T020852Z\u0026X-Amz-SignedHeaders=host\u0026X-Amz-Expires=18000\u0026X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026X-Amz-Signature=928d1a894dea40c5dd6af06810ec3b082761c17c88c1938d40a86011e3674a32","authorNotes":null,"publishMonth":"02","publishYear":"2017","receivedDate":"2017-01-13T00:00:00.000+00:00","acceptedDate":"2017-01-16T00:00:00.000+00:00","ePubDate":"2017-02-18T00:00:00.000+00:00","ePubDateText":"18 February 2017","pPubDate":null,"pPubDateText":null,"issueDate":"2017-02-18T00:00:00.000+00:00","coverDate":"2017-02-01T00:00:00.000+00:00","tableCount":null,"figureCount":null,"refCount":null,"articleCategories":"{\"subj-group\":{\"subject\":\"Perspective\"}}","titleGroup":"{\"article-title\":\"Smart Data for Digital Humanities\"}","fundingGroup":null,"abstractContent":[{"title":"Abstract","language":"English","content":"\u003cabstract\u003e\u003ctitle style='display:none'\u003eAbstract\u003c/title\u003e\u003cp\u003eThe emergence of “Big Data” has been a dramatic development in recent years. Alongside it, a lesser-known but equally important set of concepts and practices has also come into being—“Smart Data.” This paper shares the author’s understanding of \u003citalic\u003ewhat\u003c/italic\u003e, \u003citalic\u003ewhy\u003c/italic\u003e, \u003citalic\u003ehow\u003c/italic\u003e, \u003citalic\u003ewho\u003c/italic\u003e, \u003citalic\u003ewhere\u003c/italic\u003e, and \u003citalic\u003ewhich data\u003c/italic\u003e in relation to Smart Data and digital humanities. It concludes that, challenges and opportunities co-exist, but it is certain that Smart Data, the ability to achieve big insights from trusted, contextualized, relevant, cognitive, predictive, and consumable data at any scale, will continue to have extraordinary value in digital humanities.\u003c/p\u003e\u003cp\u003eThe emergence of “Big Data” has been a dramatic development in recent years. Alongside it, a lesser-known but equally important set of concepts and practices has also come into being—“Smart Data.”\u003c/p\u003e\u003c/abstract\u003e"}],"figures":[{"label":null,"caption":null,"imageLink":"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_007.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026X-Amz-Date=20220707T020852Z\u0026X-Amz-SignedHeaders=host\u0026X-Amz-Expires=18000\u0026X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026X-Amz-Signature=01194de9e8f77d8954ceeec864384ebf0088ceb36f1d7e6f52bb6422f0ddf4e6"},{"label":"Figure 1","caption":"Big Data and Smart Data.","imageLink":"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_001.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026X-Amz-Date=20220707T020852Z\u0026X-Amz-SignedHeaders=host\u0026X-Amz-Expires=18000\u0026X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026X-Amz-Signature=839eb146587a702bdd3365a1cb8d7aa017f4780e4aa6da692f07fecd95f3a08c"},{"label":"Figure 2","caption":"Smart Data Conference 2017 tracks, including combined co-tracks (marked by arrows). Source: Compiled according to the program at http://smartdata2017.dataversity.net/.","imageLink":"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_002.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026X-Amz-Date=20220707T020852Z\u0026X-Amz-SignedHeaders=host\u0026X-Amz-Expires=18000\u0026X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026X-Amz-Signature=f6335fccfdfbacd6f6698c92e18e2f5159c14023900430c1430e222d03f1c5c1"},{"label":"Figure 3","caption":"Domains/areas of interests, resources, and technologies expressed in the project descriptions of Digging into Data Challenge Round 1, 2, and 3, 2009–2013. Source: Compiled based on the project descriptions retrieved from the website at https://dev.diggingintodata.org/awards.","imageLink":"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_003.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026X-Amz-Date=20220707T020852Z\u0026X-Amz-SignedHeaders=host\u0026X-Amz-Expires=18000\u0026X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026X-Amz-Signature=1d9333df1cdc846bbee6e8ad300f730e857304809b9c49a9b467e72db32e1600"},{"label":"Figure 4","caption":"The Data-Information-Knowledge-Wisdom (DIKW) pyramid.","imageLink":"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_004.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026X-Amz-Date=20220707T020852Z\u0026X-Amz-SignedHeaders=host\u0026X-Amz-Expires=18000\u0026X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026X-Amz-Signature=0843848a35d1f8dab06df7114e25d55dfad19e59c917f6380f8206db023b761a"},{"label":"Figure 5","caption":"The unknown-unknowns.","imageLink":"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_005.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026X-Amz-Date=20220707T020852Z\u0026X-Amz-SignedHeaders=host\u0026X-Amz-Expires=18000\u0026X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026X-Amz-Signature=b217eeaa0713dcb7ecff91119821dd7e4382dc460e7f5d0c26a8e4eec0ad221f"},{"label":"Figure 6","caption":"Examples of the data resources provided by libraries, archives, and museums (LAMs).","imageLink":"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_006.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026X-Amz-Date=20220707T020852Z\u0026X-Amz-SignedHeaders=host\u0026X-Amz-Expires=18000\u0026X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026X-Amz-Signature=45e4bfac0885d877fda76df06b5427befcb68192ae831eb63b01de08f456a798"}],"tableContent":{},"tables":null,"articleContent":"\n\u003cdiv\u003e\u003cp\u003eMarcia Lei Zeng is Professor of Library and Information Science at Kent State University. She holds a Ph.D. from the School of Information Sciences at the University of Pittsburgh and an M.A. from Wuhan University in China. Her major research interests include knowledge organization systems (KOS), Linked Data, metadata and markup languages, smart data and big data, database quality control, semantic technologies, and digital humanities. Her scholarly publications consist of more than 90 papers and five books, as well as over 200 national and international conference presentations and invited lectures. Her research projects have received funding from the US National Science Foundation (NSF), Institute of Museum and Library Services (IMLS), OCLC Online Computer Library Center, Fulbright, and other foundations. Dr. Zeng has chaired or served on committees, working groups, and executive boards for the International Federation of Library Associations and Institutions (IFLA), Special Libraries Association (SLA), Association for Information Science and Technology (ASIS\u0026amp;T), the US National Information Standards Organization (NISO), the International Organization for Standardization (ISO), Dublin Core Metadata Initiative (DCMI), International Society for Knowledge Organization (ISKO), and the World Wide Web Consortium (W3C).\u003c/p\u003e\u003cp\u003e\u003cfigure id=\"j_jdis-2017-0001_fig_007_w2aab2b8c32b1b7b1ab1b1aAa\" position=\"float\" fig-type=\"figure\"\u003e\u003cimg xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"graphic/j_jdis-2017-0001_fig_007.jpg\" src=\"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_007.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026amp;X-Amz-Date=20220707T020852Z\u0026amp;X-Amz-SignedHeaders=host\u0026amp;X-Amz-Expires=18000\u0026amp;X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026amp;X-Amz-Signature=01194de9e8f77d8954ceeec864384ebf0088ceb36f1d7e6f52bb6422f0ddf4e6\" class=\"mw-100\"\u003e\u003c/img\u003e\u003c/figure\u003e\u003c/p\u003e\u003csec id=\"j_jdis-2017-0001_s_001_w2aab2b8c32b1b7b1ab1b2Aa\"\u003e\u003ctitle/\u003e\u003csec id=\"j_jdis-2017-0001_s_001_s_001_w2aab2b8c32b1b7b1ab1b2b1Aa\"\u003e\u003cdiv\u003eWHAT is Smart Data?\u003c/div\u003e\u003cp\u003eBig data has been characterized by multiple “V”s, with the number of “V”’s still increasing. Volume, Velocity, and Variety have been joined by Variability and Veracity (refer to \u003ca ref-type=\"fig\" href=\"#j_jdis-2017-0001_fig_001_w2aab2b8c32b1b7b1ab1b2b1b2aAa\"\u003eFigure 1\u003c/a\u003e). Big Data can bring big Value, if used appropriately, because it is now possible to find the hidden patterns, the unexpected correlations, and the surprising connections within large datasets through effective processing (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_008_w2aab2b8c32b1b7b1ab2ab8Aa\"\u003eGardner, 2012\u003c/a\u003e). The realization of the last “V”, Value, is dependent on “Smart Data,” the “ability to achieve big insights from trusted, contextualized, relevant, cognitive, predictive, and consumable data at any scale, great or small” (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_013_w2aab2b8c32b1b7b1ab2ac13Aa\"\u003eKobielus, 2016\u003c/a\u003e, p. 8). Simply speaking, Smart Data makes sense out of Big Data. It provides value from harnessing the challenges posed by Volume, Velocity, Variety and Veracity of Big Data, in-turn providing actionable information and improving decision making (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_020_w2aab2b8c32b1b7b1ab2ac20Aa\"\u003eSheth, 2014\u003c/a\u003e). Smart Data “is the way in which different data sources (including Big Data) are brought together, correlated, analyzed, etc., to be able to feed decision-making and action processes” (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_011_w2aab2b8c32b1b7b1ab2ac11Aa\"\u003eIafrate, 2015\u003c/a\u003e, p. 13) (\u003ca ref-type=\"fig\" href=\"#j_jdis-2017-0001_fig_001_w2aab2b8c32b1b7b1ab1b2b1b2aAa\"\u003eFigure 1\u003c/a\u003e).\u003c/p\u003e\u003cp\u003e\u003cfigure id=\"j_jdis-2017-0001_fig_001_w2aab2b8c32b1b7b1ab1b2b1b2aAa\" position=\"float\" fig-type=\"figure\"\u003e\u003ch2\u003eFigure 1\u003c/h2\u003e\u003cfigCaption\u003e\u003cp\u003eBig Data and Smart Data.\u003c/p\u003e\u003c/figCaption\u003e\u003cimg xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"graphic/j_jdis-2017-0001_fig_001.jpg\" src=\"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_001.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026amp;X-Amz-Date=20220707T020852Z\u0026amp;X-Amz-SignedHeaders=host\u0026amp;X-Amz-Expires=18000\u0026amp;X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026amp;X-Amz-Signature=839eb146587a702bdd3365a1cb8d7aa017f4780e4aa6da692f07fecd95f3a08c\" class=\"mw-100\"\u003e\u003c/img\u003e\u003c/figure\u003e\u003c/p\u003e\u003c/sec\u003e\u003csec id=\"j_jdis-2017-0001_s_001_s_002_w2aab2b8c32b1b7b1ab1b2b2Aa\"\u003e\u003cdiv\u003eWHY Smart Data?\u003c/div\u003e\u003cp\u003eData in the 21\u003csup\u003est\u003c/sup\u003e century, like oil in the 18\u003csup\u003eth\u003c/sup\u003e century, is an untapped asset that holds immense value for those who can learn to extract and use it. “Data is the new oil” (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_010_w2aab2b8c32b1b7b1ab2ac10Aa\"\u003eHumby, 2006\u003c/a\u003e) has become a defining phrase used by many in recent years as the evidence became more and more convincing. “However, in its raw form, data is just like crude oil; it needs to be refined and processed in order to generate real value. Data has to be cleaned, transformed, and analyzed to unlock its hidden potential” (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_021_w2aab2b8c32b1b7b1ab2ac21Aa\"\u003eTiECON East, 2014\u003c/a\u003e). According to a 2012 report on the “digital universe”—a measure of all the digital data created, replicated, and consumed in a single year—“even with a generous estimate, the amount of information in the digital universe that is ‘tagged’ accounts for only about 3% of the digital universe in 2012, and that which is analyzed is half a percent of the digital universe” (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_009_w2aab2b8c32b1b7b1ab2ab9Aa\"\u003eGantz \u0026amp; Reinsel, 2012\u003c/a\u003e, p. 3). Extracting Value from Big Data characterized by the other “V”s presents both great challenges and inestimable opportunities. Only after it has been tamed through organization and integration processes is such data turned into Smart Data that reflects the research priorities of a particular discipline or field. These tamed results, as Smart Data inquiries, can then be used to provide comprehensive analyses and generate new products and services (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_008_w2aab2b8c32b1b7b1ab2ab8Aa\"\u003eGardner, 2012\u003c/a\u003e; \u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_015_w2aab2b8c32b1b7b1ab2ac15Aa\"\u003eMukerjee, 2014\u003c/a\u003e; \u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_019_w2aab2b8c32b1b7b1ab2ac19Aa\"\u003eSchöch, 2013\u003c/a\u003e; \u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_021_w2aab2b8c32b1b7b1ab2ac21Aa\"\u003eTiECON East, 2014\u003c/a\u003e).\u003c/p\u003e\u003c/sec\u003e\u003csec id=\"j_jdis-2017-0001_s_001_s_003_w2aab2b8c32b1b7b1ab1b2b3Aa\"\u003e\u003cdiv\u003eHOW to transform Big Data into Smart Data?\u003c/div\u003e\u003cp\u003eA look at the topics presented at Smart Data conferences since 2015 may provide a good overview of the technologies involved in Smart Data strategies of achieving big insights from trusted, contextualized, relevant, cognitive, predictive, and consumable data at any scale. These include: cognitive computing, deep learning, machine learning, artificial intelligence, predictive analytics, graph databases, machine intelligence, voice processing, semantic technologies, autonomous vehicles, Big Data, data science, Internet of Things (IoT), text analysis, Resource Description Framework (RDF), knowledge graphs, contextual computing, Linked Data, deep reasoning, ontologies, JSON-LD\u003cfn id=\"j_jdis-2017-0001_fn_001_w2aab2b8c32b1b7b1ab1b2b3b1b1Aa\" symbol=\"①\"\u003e\u003cp\u003eJSON-LD is a lightweight Linked Data format\u003c/p\u003e\u003c/fn\u003e, common sense, natural language processing (NLP), and semantic search (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_006_w2aab2b8c32b1b7b1ab2ab6Aa\"\u003eDATAVERSITY, 2017\u003c/a\u003e). These topics are closely interrelated and overlapping. For example, deep learning shows the great potential in natural language processing; cognitive computing uses machine learning to find deep patterns (including those not obviously statistical) within complex, unstructured, and streaming data. Some of the topics have moved beyond the original territory conveyed by these labels for years. For example, “artificial intelligence” is a field that has changed dramatically in the 21\u003csup\u003est\u003c/sup\u003e century. Meanwhile, the topics of the Smart Data conferences reflect the varied applications of the W3C standards for the Semantic Web, including—but not limited to—RDF, Linked Data, ontologies, graph databases, semantic search, and other semantic technologies (\u003ca ref-type=\"fig\" href=\"#j_jdis-2017-0001_fig_002_w2aab2b8c32b1b7b1ab1b2b3b2aAa\"\u003eFigure 2\u003c/a\u003e).\u003c/p\u003e\u003cp\u003e\u003cfigure id=\"j_jdis-2017-0001_fig_002_w2aab2b8c32b1b7b1ab1b2b3b2aAa\" position=\"float\" fig-type=\"figure\"\u003e\u003ch2\u003eFigure 2\u003c/h2\u003e\u003cfigCaption\u003e\u003cp\u003eSmart Data Conference 2017 tracks, including combined co-tracks (marked by arrows). Source: Compiled according to the program at \u003cext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"http://smartdata2017.dataversity.net/\"\u003ehttp://smartdata2017.dataversity.net/\u003c/ext-link\u003e.\u003c/p\u003e\u003c/figCaption\u003e\u003cimg xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"graphic/j_jdis-2017-0001_fig_002.jpg\" src=\"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_002.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026amp;X-Amz-Date=20220707T020852Z\u0026amp;X-Amz-SignedHeaders=host\u0026amp;X-Amz-Expires=18000\u0026amp;X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026amp;X-Amz-Signature=f6335fccfdfbacd6f6698c92e18e2f5159c14023900430c1430e222d03f1c5c1\" class=\"mw-100\"\u003e\u003c/img\u003e\u003c/figure\u003e\u003c/p\u003e\u003c/sec\u003e\u003csec id=\"j_jdis-2017-0001_s_001_s_004_w2aab2b8c32b1b7b1ab1b2b4Aa\"\u003e\u003cdiv\u003eWHO is making/using Smart Data?\u003c/div\u003e\u003cp\u003eEfforts to tame Big Data using Smart Data strategies have been made by various experts including natural scientists, engineers, business executives and financial analysts, practitioners of medicine, and government agents. In humanities, the word “Smart Data” is not universally used, even though the approaches can be recognized in many research projects over the last six years. Since 2009, through the \u003citalic\u003eDigging into Data Challenge\u003c/italic\u003e program (\u003cext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://diggingintodata.org/\"\u003ehttps://diggingintodata.org/\u003c/ext-link\u003e), research funders from more than 10 countries have funded dozens of projects aimed at research questions in the humanities and/or social sciences. The sponsors in the USA include the National Endowment for the Humanities (NEH), the National Science Foundation (NSF), and the Institute of Museum and Library Services (IMLS). Based on the project descriptions of the last three rounds (the most recent, the 4\u003csup\u003eth\u003c/sup\u003e round has not announced final winners as of the date this paper was written), the resources include mainly unstructured data assets originating in ancient times, while structured datasets created in the digital age are also used. The domains and areas of interests are widely spread in the humanities and social sciences. Technologically, large-scale data analyses have been applied to research questions in the fields using the Smart Data approaches (refer to the above “How” section). Methodologically, the projects are interdisciplinary and strive to show how best to tap data in large scale and diverse formats in order to search for key insights while also ensuring access to such data by humanities and social science researchers through new technology-supported tools (\u003ca ref-type=\"fig\" href=\"#j_jdis-2017-0001_fig_003_w2aab2b8c32b1b7b1ab1b2b4b2aAa\"\u003eFigure 3\u003c/a\u003e).\u003c/p\u003e\u003cp\u003e\u003cfigure id=\"j_jdis-2017-0001_fig_003_w2aab2b8c32b1b7b1ab1b2b4b2aAa\" position=\"float\" fig-type=\"figure\"\u003e\u003ch2\u003eFigure 3\u003c/h2\u003e\u003cfigCaption\u003e\u003cp\u003eDomains/areas of interests, resources, and technologies expressed in the project descriptions of \u003citalic\u003eDigging into Data Challenge\u003c/italic\u003e Round 1, 2, and 3, 2009–2013. Source: Compiled based on the project descriptions retrieved from the website at \u003cext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://dev.diggingintodata.org/awards\"\u003ehttps://dev.diggingintodata.org/awards\u003c/ext-link\u003e.\u003c/p\u003e\u003c/figCaption\u003e\u003cimg xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"graphic/j_jdis-2017-0001_fig_003.jpg\" src=\"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_003.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026amp;X-Amz-Date=20220707T020852Z\u0026amp;X-Amz-SignedHeaders=host\u0026amp;X-Amz-Expires=18000\u0026amp;X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026amp;X-Amz-Signature=1d9333df1cdc846bbee6e8ad300f730e857304809b9c49a9b467e72db32e1600\" class=\"mw-100\"\u003e\u003c/img\u003e\u003c/figure\u003e\u003c/p\u003e\u003cp\u003eA newly launched nationwide contest encouraging the use of data from \u003citalic\u003eChronicling America’\u003c/italic\u003es digital repository of historic US newspapers, as well as the new Humanities Access Grant funded by the \u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_016_w2aab2b8c32b1b7b1ab2ac16Aa\"\u003eNEH (2016)\u003c/a\u003e, are further signs of initiatives taking place at the intersection between the humanities and digital technologies. While the multifaceted landscape of digital humanities is yet to be fully understood, the highly competitive Digital Humanities (DH) conferences could give us more clues. The self-tagged topics of submissions from DH 2013 to DH 2016 conferences reflected a multi-disciplinary nature: \u003citalic\u003etext analysis\u003c/italic\u003e was number one in submission count, followed by \u003citalic\u003ehistorical studies\u003c/italic\u003e, \u003citalic\u003edata mining/text mining\u003c/italic\u003e, \u003citalic\u003earchives \u0026amp; repositories\u003c/italic\u003e, \u003citalic\u003eliterary studies\u003c/italic\u003e, and \u003citalic\u003edata visualization\u003c/italic\u003e. The 2017 count, which separated topics from disciplines, shows that those top topics are joined by \u003citalic\u003einterdisciplinary collaboration\u003c/italic\u003e and \u003citalic\u003ecorpora and corpus activities\u003c/italic\u003e. The disciplines that have more than 100 submissions are: \u003citalic\u003ecomputer science\u003c/italic\u003e, \u003citalic\u003eliterary studies\u003c/italic\u003e, \u003citalic\u003elibrary and information science\u003c/italic\u003e, \u003citalic\u003ecultural studies\u003c/italic\u003e, and \u003citalic\u003ehistorical studies\u003c/italic\u003e. A notable finding is that submissions from \u003citalic\u003efilm and media studies\u003c/italic\u003e have greatly increased compared to previous years, as have other \u003citalic\u003enon-textual\u003c/italic\u003e disciplines. There has also been a steady increase of new authors entering the field and of co-authorship of submissions (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_023_w2aab2b8c32b1b7b1ab2ac23Aa\"\u003eWeingart, 2016\u003c/a\u003e; \u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_024_w2aab2b8c32b1b7b1ab2ac24Aa\"\u003e2017\u003c/a\u003e). Overall, a wide range of disciplines and approaches are seen in the humanities to reach “bigger smart data” or “smarter big data” (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_019_w2aab2b8c32b1b7b1ab2ac19Aa\"\u003eSchöch, 2013\u003c/a\u003e), as demonstrated by the outcomes presented at digital humanities conferences, the government funded research projects, and new initiatives and publications all over the world in the past six years.\u003c/p\u003e\u003c/sec\u003e\u003csec id=\"j_jdis-2017-0001_s_001_s_005_w2aab2b8c32b1b7b1ab1b2b5Aa\"\u003e\u003cdiv\u003eWHERE is the distinctive mark in Digital Humanities?\u003c/div\u003e\u003cp\u003eIt might be natural that, when thinking about digital humanities in the data-intensive research projects, people would look for distinctive marks toward the direction of technologies. However, as \u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_019_w2aab2b8c32b1b7b1ab2ac19Aa\"\u003eSchöch (2013)\u003c/a\u003e pointed out, the distinctive mark of Big Data in the humanities seemed to be a methodological shift rather than a primarily technological one. Further scrutiny of the methodological shift in humanities highlights the role of Big Data and Smart Data for every field of knowledge. In short, the relationship between Big Data and Smart Data can be characterized as “what it is” and “what it is for” (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_011_w2aab2b8c32b1b7b1ab2ac11Aa\"\u003eIafrate, 2015\u003c/a\u003e). This view of turning Big Data into Smart Data brings us back to the well-known Data-Information-Knowledge-Wisdom (DIKW) pyramid (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_025_w2aab2b8c32b1b7b1ab2ac25Aa\"\u003eZeleny, 1987\u003c/a\u003e; \u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_001_w2aab2b8c32b1b7b1ab2ab1Aa\"\u003eAckoff, 1988\u003c/a\u003e) which represents the most basic strategy for understanding a world that far exceeds our brains’ capacity by filtering, winnowing, and otherwise reducing it to something more meaningful (\u003ca ref-type=\"fig\" href=\"#j_jdis-2017-0001_fig_004_w2aab2b8c32b1b7b1ab1b2b5b2aAa\"\u003eFigure 4\u003c/a\u003e).\u003c/p\u003e\u003cp\u003e\u003cfigure id=\"j_jdis-2017-0001_fig_004_w2aab2b8c32b1b7b1ab1b2b5b2aAa\" position=\"float\" fig-type=\"figure\"\u003e\u003ch2\u003eFigure 4\u003c/h2\u003e\u003cfigCaption\u003e\u003cp\u003eThe Data-Information-Knowledge-Wisdom (DIKW) pyramid.\u003c/p\u003e\u003c/figCaption\u003e\u003cimg xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"graphic/j_jdis-2017-0001_fig_004.jpg\" src=\"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_004.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026amp;X-Amz-Date=20220707T020852Z\u0026amp;X-Amz-SignedHeaders=host\u0026amp;X-Amz-Expires=18000\u0026amp;X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026amp;X-Amz-Signature=0843848a35d1f8dab06df7114e25d55dfad19e59c917f6380f8206db023b761a\" class=\"mw-100\"\u003e\u003c/img\u003e\u003c/figure\u003e\u003c/p\u003e\u003cp\u003eNevertheless, the Smart Data approach is not simply a replication of the DIKW path because Smart Data is based on Big Data’s methodology, which assumes the ability to reveal the \u003citalic\u003eunknown-unknowns\u003c/italic\u003e (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_004_w2aab2b8c32b1b7b1ab2ab4Aa\"\u003eBorne, 2013\u003c/a\u003e) instead of taking the approach that one knows to do something in order to prove or disapprove the \u003citalic\u003eknown-unknowns\u003c/italic\u003e (\u003ca ref-type=\"fig\" href=\"#j_jdis-2017-0001_fig_005_w2aab2b8c32b1b7b1ab1b2b5b4aAa\"\u003eFigure 5\u003c/a\u003e). This is a fundamental advancement of Smart Data that distinguishes it from other approaches that follow the more traditional blueprint of hypothesizing, modeling, and testing (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_002_w2aab2b8c32b1b7b1ab2ab2Aa\"\u003eAnderson, 2008\u003c/a\u003e).\u003c/p\u003e\u003cp\u003e\u003cfigure id=\"j_jdis-2017-0001_fig_005_w2aab2b8c32b1b7b1ab1b2b5b4aAa\" position=\"float\" fig-type=\"figure\"\u003e\u003ch2\u003eFigure 5\u003c/h2\u003e\u003cfigCaption\u003e\u003cp\u003eThe unknown-unknowns.\u003c/p\u003e\u003c/figCaption\u003e\u003cimg xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"graphic/j_jdis-2017-0001_fig_005.jpg\" src=\"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_005.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026amp;X-Amz-Date=20220707T020852Z\u0026amp;X-Amz-SignedHeaders=host\u0026amp;X-Amz-Expires=18000\u0026amp;X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026amp;X-Amz-Signature=b217eeaa0713dcb7ecff91119821dd7e4382dc460e7f5d0c26a8e4eec0ad221f\" class=\"mw-100\"\u003e\u003c/img\u003e\u003c/figure\u003e\u003c/p\u003e\u003cp\u003eOne good example of revealing the unknown-unknowns through Smart Data is the research project “A network framework of cultural history” published in \u003citalic\u003eScience\u003c/italic\u003e and also on \u003citalic\u003eNature Video\u003c/italic\u003e (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_017_w2aab2b8c32b1b7b1ab2ac17Aa\"\u003eSchich et al., 2014a\u003c/a\u003e; \u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_018_w2aab2b8c32b1b7b1ab2ac18Aa\"\u003e2014b\u003c/a\u003e). A multidisciplinary research team provided a macroscopic perspective of the cultural history of Europe and North America across 3,000 years, using simple—but large—datasets of the birth and death locations of more than 150,000 notable individuals, which revealed previously undocumented human mobility patterns and cultural attraction dynamics. Incorporating this analyzed data, the 3,000 year, large-scale patterns in European and American cultural life are visualized and brought to life, enlightening the formation of intellectual and cultural centers, the rising and crumbling of empires, and other influential factors, all beyond the scope of specific events or narrow time intervals (Recommend watching the video at \u003cext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://www.youtube.com/watch?v=4gIhRkCcD4U\"\u003ehttps://www.youtube.com/watch?v=4gIhRkCcD4U\u003c/ext-link\u003e). The value of the knowledge is incredible and the big insights are achieved from trusted, contextualized, relevant, cognitive, predictive, and consumable data (the original sources are structured data from Freebase (now Wikidata), the General Artist Lexicon (AKL), and the Getty Union List of Artist Names (ULAN)) (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_017_w2aab2b8c32b1b7b1ab2ac17Aa\"\u003eSchich et al., 2014a\u003c/a\u003e). This example not only demonstrates the potential of the Smart Data approach in sociology, anthropology, and history in general but also indicates a significant methodological advancement in the humanities.\u003c/p\u003e\u003c/sec\u003e\u003csec id=\"j_jdis-2017-0001_s_001_s_006_w2aab2b8c32b1b7b1ab1b2b6Aa\"\u003e\u003cdiv\u003eWHICH DATA can be found in supporting research and scholarship in Digital Humanities?\u003c/div\u003e\u003cp\u003eWhen putting Big Data and Smart Data into the context of digital humanities, a key concept that needs to be agreed upon is the use of the term “data.” In the digital age, it is common for people to only think of data in terms of digitally available formats. The connection between digital data and data analytics is correct, but we need to fully understand that the terms “data” and “digital data” are not equivalent. The types of data are also not limited to quantitative data. The Reference Model for an Open Archival Information System (OAIS) defined data as a “reinterpretable representation of information in a formalized manner suitable for communication, interpretation, or processing” while offering examples of data as: a sequence of bits, a table of numbers, the characters on a page, the recording of sounds made by a person speaking, or a moon rock specimen. This definition of “data” was given within the context of “information,” which is “Any type of knowledge that can be exchanged. In an exchange, it is represented by data” (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_005_w2aab2b8c32b1b7b1ab2ab5Aa\"\u003eConsultative Committee for Space Data Systems, 2012\u003c/a\u003e, p. 1–10 and p. 1–12). After a comprehensive review of the definitions and terminology for “data” in her book titled \u003citalic\u003eBig data\u003c/italic\u003e, \u003citalic\u003elittle data\u003c/italic\u003e, \u003citalic\u003eno data: Scholarship in the networked world\u003c/italic\u003e, \u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_003_w2aab2b8c32b1b7b1ab2ab3Aa\"\u003eBorgman (2015)\u003c/a\u003e presented an overarching summary that “data are representations of observations, objects, or other entities used as evidence of phenomena for the purpose of research or scholarship” (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_003_w2aab2b8c32b1b7b1ab2ab3Aa\"\u003eBorgman, 2015\u003c/a\u003e, p. 28).\u003c/p\u003e\u003cp\u003eIn the data resources that are usually served through libraries, archives, and museums (LAMs) and other information institutions, the types of data, which are available in the largest quantity, have the diversity in type, nature, and quality, and are the most challenging to process, belong to \u003citalic\u003eunstructured data\u003c/italic\u003e found in documents and other information-bearing objects (textual or non-textual, digitized or non-digitized) in all kinds of formats (examples can be found in \u003ca ref-type=\"fig\" href=\"#j_jdis-2017-0001_fig_003_w2aab2b8c32b1b7b1ab1b2b4b2aAa\"\u003eFigure 3\u003c/a\u003e). These primary data resources are held in special collections, archives, oral history files, annual reports, provenance indexes, and inventories, to name just a few. The nature of such data is quite different from, for instance, that of the data used by the “digital universe” that “is made up of images and videos on mobile phones uploaded to YouTube, digital movies populating the pixels of our high-definition TVs, banking data swiped in an ATM, security footage at airports and major events such as the Olympic Games, subatomic collisions recorded by the Large Hadron Collider at CERN, transponders recording highway tolls, voice calls zipping through digital phone lines, and texting as a widespread means of communications” (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_009_w2aab2b8c32b1b7b1ab2ab9Aa\"\u003eGantz \u0026amp; Reinsel, 2012\u003c/a\u003e, p. 1). Such a “digital universe” may not be the major or only source for humanities researchers.\u003c/p\u003e\u003cp\u003eIn fact, one primary challenge in applying the Smart Data approach to digital humanities is the availability of data resources for those in need of historical data that one could not obtain through Web crawling or scraping. There is no doubt that Smart Data approaches that have been tested and implemented in business and industry can be applied to the digital humanities. Nevertheless, how to “datafy” the unstructured data (i.e. turn the heritage materials into not only machine-readable but also machine-processable resources, and reconstruct through digitization pipelines) before the researchers can make use of data analytics technologies? This fundamental question might explain why, for digital humanities, the Smart Data approach emphasizes the organization and integration processes to transform unstructured data to structured and semi-structured data (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_012_w2aab2b8c32b1b7b1ab2ac12Aa\"\u003eKaplan, 2015\u003c/a\u003e; \u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_014_w2aab2b8c32b1b7b1ab2ac14Aa\"\u003eMayer-Schönberger \u0026amp; Cukier 2013; Schöch, 2013\u003c/a\u003e).\u003c/p\u003e\u003cp\u003eIn addition to the \u003citalic\u003eunstructured data\u003c/italic\u003e discussed above, LAMs and other information institutions also provide tremendous opportunities for humanities researchers to dig nuggets of gold from \u003citalic\u003esemi-structured data\u003c/italic\u003e (examples include the intellectual works encoded following the Text Encoding Initiative (TEI) guidelines, archival finding aids, value-added or tagged resources that exist in all kinds of formats, and the unstructured portions of otherwise structured datasets) as well as \u003citalic\u003estructured data\u003c/italic\u003e (including bibliographies, indexing and abstracting databases, citation indexes, catalogs of all kinds, special collection portals, metadata repositories, curated research datasets, and name authorities) (\u003ca ref-type=\"fig\" href=\"#j_jdis-2017-0001_fig_006_w2aab2b8c32b1b7b1ab1b2b6b5aAa\"\u003eFigure 6\u003c/a\u003e).\u003c/p\u003e\u003cp\u003e\u003cfigure id=\"j_jdis-2017-0001_fig_006_w2aab2b8c32b1b7b1ab1b2b6b5aAa\" position=\"float\" fig-type=\"figure\"\u003e\u003ch2\u003eFigure 6\u003c/h2\u003e\u003cfigCaption\u003e\u003cp\u003eExamples of the data resources provided by libraries, archives, and museums (LAMs).\u003c/p\u003e\u003c/figCaption\u003e\u003cimg xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"graphic/j_jdis-2017-0001_fig_006.jpg\" src=\"https://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/600935d7f1433668c24d021f/j_jdis-2017-0001_fig_006.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256\u0026amp;X-Amz-Date=20220707T020852Z\u0026amp;X-Amz-SignedHeaders=host\u0026amp;X-Amz-Expires=18000\u0026amp;X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220707%2Feu-central-1%2Fs3%2Faws4_request\u0026amp;X-Amz-Signature=45e4bfac0885d877fda76df06b5427befcb68192ae831eb63b01de08f456a798\" class=\"mw-100\"\u003e\u003c/img\u003e\u003c/figure\u003e\u003c/p\u003e\u003cp\u003eThese datasets might be relatively small in volume and have limited heterogeneity in comparison with Big Data, but they are clean, explicit, trusted, and value-added, and their creation is governed mostly by human decisions. More promisingly, they are among the resources most likely to be freely accessible (non-proprietary and non-commercial). These make them treasures for all humanities researchers and beyond. In his speech titled “Contextual Computing with Knowledge Graphs and the Web of Entities” at Smart Data Online 2016, Richard Wallis, a well-known pioneer of the library community’s Linked Open Data movement, provided his vision of \u003citalic\u003econtextual computing\u003c/italic\u003e, in which he listed elements such as meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, task, and goal. The revolutionary work of WorldCat Linked Data and the WorldCat Entities experiment at OCLC are among the successful cases. By providing millions of entities of intellectual works, places, concepts, persons, organizations, events, and other types of tamed data together, the WorldCat Entities shows how the structured data provided by LAMs can enrich knowledge graphs and Linked Open Data datasets infinitely (\u003ca ref-type=\"bibr\" href=\"#j_jdis-2017-0001_ref_022_w2aab2b8c32b1b7b1ab2ac22Aa\"\u003eWallis, 2016\u003c/a\u003e).\u003c/p\u003e\u003cp\u003eIn the processes that transform unstructured data to structured and semi-structured data, the Smart Data strategy drives data service providers to aim at machine-\u003citalic\u003eunderstandable\u003c/italic\u003e, -\u003citalic\u003eprocessable\u003c/italic\u003e, and -\u003citalic\u003eactionable\u003c/italic\u003e (instead of merely machine-\u003citalic\u003ereadable\u003c/italic\u003e) data, to provide accurate data in the processes of interlinking, citing, transferring, rights-permission management, use and reuse, and to enable both one-to-many usages and high efficiency processing of data for digital humanities.\u003c/p\u003e\u003c/sec\u003e\u003c/sec\u003e\u003csec id=\"j_jdis-2017-0001_s_002_w2aab2b8c32b1b7b1ab1b3Aa\"\u003e\u003cdiv\u003eConclusion\u003c/div\u003e\u003cp\u003eToday, advanced technologies, under the umbrella of Big Data and Smart Data, allow researchers of the humanities to join the mainstream of the digital age with new abilities as never before: to access and reuse large volumes of diverse data; to unearth patterns and connections formerly hidden from view; to reconstruct the past; to discover the impact and value of qualitative and quantitative variables in both real and virtual environments; and to bring the knowledge of the complex intricacies of human society to light. Challenges and opportunities co-exist, but it is certain that Smart Data, the ability to achieve big insights from trusted, contextualized, relevant, cognitive, predictive, and consumable data at any scale, will continue to have extraordinary value in digital humanities.\u003c/p\u003e\u003c/sec\u003e\u003c/div\u003e","keywords":[],"recentIssues":{"10.2478/jdis-2022-0012":"\u003carticle-title\u003eBibliometrics Is Valuable Science. 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And — if they believe the book can sell well — they would like to receive a significant part of the sales revenues.","selfPublishingContent.second":"If you supply a ready-made publishable eBook file, we can host, distribute, sell and promote your book free of any charge. \u003c1\u003eYou will receive 70% of net revenues from the book sales.\u003c/1\u003e In addition, you have the option of choosing some of our paid services, including eBook formatting.","selfPublishingContent.third":"To see the complete list of publishing services and solutions that Sciendo offers to Self-publishing authors, as well as the relevant fees, \u003c1\u003eregister here\u003c/1\u003e","selfPublishingContent.fourth":"To learn more about these services, please contact Magdalena Cal, Customer Service Manager at \u003c1\u003emagdalena.cal@sciendo.com\u003c/1\u003e","selfPublishingContent.fifth":"You can also \u003c1\u003edownload the Self-Publishing brochure\u003c/1\u003e for more information.","fullPublishingContent.first":"Sciendo publishes books from universities, research institutes, academies of sciences, learned societies and other organizations. 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