The video “How Natural Language Processing is Changing Research” provides a more extended look at WordSeer’s usefulness for analyzing slave narratives, but its purpose is also to underscore how such a tool can benefit humanities scholars. In this video the discussion veers toward presenting reading as a chore from which humanities scholars seek relief. On that note, a student in Dr. Michael Ullyot’s undergraduate ENG 203 course, “Hamlet in the Humanities Lab” at the University of Calgary offers some pertinent comments. In her penultimate blog post for the course, Stephanie Vandework devotes a section to “The Pros and Cons of Exploratory Analysis” and examines more closely the claims in the WordSeer Shakespeare demo, finding some to suffer from overgeneralization. (For a view of the course from the instructor’s perspective, see Dr. Ullyot’s presentation, Teaching Hamlet in the Humanities Lab, for the Renaissance Society of America conference this past March 2012.)

NEH DH Lightning Round on Wordseer.

What we need are collections in the 5,000 – 500,000 volume range, cleaned up to at least (say) 95% recall and 99% precision. Precision is more important than recall, because false negatives drop out of many kinds of analysis — as long as they’re randomly distributed (i.e. you can’t just ignore the f/s problem in the 18c). Collections of that kind are going to generate insights that we can’t glimpse as individual readers. They’ll be especially valuable once we enrich the metadata with information about (for instance) genre, gender, and nationality. I’m not confident that we can crowdsource OCR correction (it’s an awful lot of work), but I am confident that we could crowdsource some light enrichment of metadata.

by using multiple databases and keyword variants, the historian may gain confidence in a particular chronological intervention. Large databases, the result of scanned microfilm collections or mass digitization initiatives across multiple libraries, provide enough texts to bridge generation and genre, incorporating authors from a variety of backgrounds. Sheer number of texts is important here: ECCO indexes 200,000 works from eighteenth- and nineteenth-century Britain with 33 million pages of text; Google Books Search has 42 million books from all periods. If the historian’s goal is to show a shift in common word usage, the size of a database is more important than its genre specificity; in the case examined in the present article, for instance, Google Book Search and ECCO were superior to the available poetry databases. Iterative visitation of multiple databases provided another potential source of richness for extracting meaning from these tools.

Text analytics are often used in the social sciences as a way of unobtrusively observing people and their interactions. Humanists tend to approach the supporting algorithms with skepticism, and with good reason. This post is about the difficulties of using words or counts as a proxy for some secondary or deeper meaning.

Funders need to broaden expectations of sustainability beyond access and infrastructure, to include also how a project situates itself within the larger scholarly discourse. In other words, projects need a social contract to the broader scholarly community, not just funder. Funders must prioritize and encourage public critiques as a way of establishing scholarly value, rather than through grant selection alone. A project without accountability, without connectedness, without critique, simply fills another plot in the DH project graveyard.

The amount of literary material available on-line keeps growing rapidly. Not only are there machine-readable texts in libraries, collections and e-book stores, but there is also more and more “live” literature – e-zines, blogs, self-published e-books and so on. There is a need for tools to help users navigate, visualize and appreciate high volume of available literature.

Literary texts are quite different from technical and formal documents, which have been the focus of NLP research thus far. Most forms of statistical language processing rely on lexical information in one way or another. In literature, the primary mode is narrative rather than exposition. Stories may be cognitively easier to read than certain expository genres, such as scientific documents, but it is a challenging form of discourse for NLP tools and methods. For instance, literary prose lacks overt lexical clues and structural markers typically leveraged in the processing of more structured genres. Also, even conventional literary texts exhibit far less unity of time, space and topic than most formal discourse. Learning to handle these challenges in literary data may help move past heavy reliance on surface clues in general.

Literature also differs from other genres because of the needs of its typical audience. For instance, reading, searching or browsing literature online is a different task than searching for the latest news on a particular topic. Search criteria would be rather abstract: not a keyword, but a literary style, similarity to another work, point of view and so on. When looking for a summary or a digest, a reader may prefer to know or visualize a text's broad characteristics than facts which summarize the plot.

We invite papers that touch upon these areas, but also welcome other ideas which promote the processing of literary narrative or related forms of discourse.

The following guidelines are designed to help departments and faculty members implement effective evaluation procedures for hiring, reappointment, tenure, and promotion. They apply to scholars working with digital media as their subject matter and to those who use digital methods or whose work takes digital form.

Interested in teaching or learning TEI? The slides, lecture notes, and other materials here were developed by the WWP for the workshops we teach, but they can also be a starting point for self-guided study.

We don’t live in the binary producer/consumer world anymore. Even if we did, there is simply too much data to deal with. Its stewards simply do not have all necessary expertise or resources to organize it most effectively and flexibly. Without doubt, this involves plenty of technical challenges (standards, interfaces, infrastructure). But these are trivial in comparison to the real challenge: shifting community expectations that erroneous metadata can and should be edited by researchers themselves. And while we’re at it, we might broaden our view of metadata to include not only the usual fields (author, date, etc), but additional description as well (abstracts, section headings, keywords, etc) that makes the texts more findable.

The DH Jobs Archive is a repository of jobs listed as relating to digital humanities.

Listings are increasingly featured on community sites such as DHNow or circulating on Twitter. However, many of these listings appear only for a brief time before being taken offline. Listservs including Humanist act as the best existing archival source for listings within the community, however jobs are not easily searched, inconsistently mentioned, and lack structure making programmatic analysis difficult. This project is an effort to consolidate and archive those job listings, hopefully enabling future publications and computational analysis of job listings within the community.

A topic like this one is hard to interpret. But for a literary scholar, that’s a plus. I want this technique to point me toward something I don’t yet understand, and I almost never find that the results are too ambiguous to be useful. The problematic topics are the intuitive ones — the ones that are clearly about war, or seafaring, or trade. I can’t do much with those.

Reformats and renames etexts downloaded from Project Gutenberg.

An article on Raymond Roussel appeared on page 62 of the 18 December 1910 edition of the Cleveland Plain Dealer, entitled “Yes He Really Likes to Work”:

EMI and The Echo Nest have partnered to bring developers a unique opportunity to work with content from some of the biggest artists in the world. This content is organized into various sandboxes. Some sandboxes provide access to thousands of audio tracks from multiple artists. Other sandboxes focus on a single artist, providing broader content types like audio, video, artwork, photos and more.

The intent is to provide the developer community with the resources to create new and innovative applications and services. If you have an interesting concept, then EMI will partner with you to help bring that idea to market.

Digital texts and digital libraries offer us new opportunities for searching and accessing literary material. But more interesting and exciting than the mere searching of digital texts is the ability to leverage computation in order to process and analyze textual data, to provide new methods for reading, analyzing, and understanding literature.

This course provides an introduction to the field of humanities computing with a special emphasis on literary text-analysis. Students learn about the preparation and processing of digital texts while exploring literary methods which help us explain and interpret literary texts, genres, and movements. The course includes units dealing with "stylometry" (computer based stylistic analysis), authorship attribution, gender detection, text encoding, and the visualization of literary information using such open source tools as R and Gephi.

Throughout the course we consider the theoretical issues associated with employing quantitative methodologies in a traditionally qualitative discipline; we read and discuss landmark essays in the field; and we end with an informed discussion of how digital libraries and computation are taking literary scholarship "beyond the book." Students will develop basic coding skills in an environment in which understanding literature is the only prerequisite. No programming experience is required; students will develop fluency in XML and R through exercises and work on a collaborative text-analysis project.

The following materials have been suggested by participants in the workshop, and have been organized into rough groupings for ease of navigation. This is not intended as a comprehensive list of readings on data modeling in the humanities, but (at the moment) reflects the textual emphasis of the workshop.

Chris Manning's tutorial at Digital Humanities 2011 at Stanford.

We too often insist on a single, correct understanding of an event, or of the past. Instead, a richer history would included a heterogeneity of interpretations, the diversity of practices, the contestations, and the processes and negotiations by which people have dealt with such differences–turbulence. Digital media presents us with an opportunity to use tools that facilitate more complex, not complicated, narratives and stories of the past and how they continue to operate in our present. By bringing out such variability, we can show more of the operations of history, the stories embedded in primary data and the negotiations and decisions that lead to the structures, ideas, and social forms of our narratives.

Politics and political con ict often occur in the written and spoken word. Scholars have long recognized this, but the massive costs of analyzing even moderately sized collections of texts have hindered their use in political science research. Here lies the promise of automated text analysis: it substantially reduces the costs of analyzing large collections of text. We provide a guide to this exciting new area of research and show how, in many instances, the methods have already obtained part of their promise. But there are pitfalls to using automated methods: they are no substitute for careful thought and close reading and require extensive and problem specic validation. We survey a wide range of new methods, provide guidance on how to validate the output of the models, and clarify misconceptions and errors in the literature. To conclude, we argue that for automated text methods to become a standard tool for political scientists, methodologists must contribute new methods and new methods of validation.

As input into the development, design, and improvement of the HathiTrust Research Center (HTRC), recipients of Google’s Digital Humanities Grants were interviewed to identify issues encountered during their projects. This project was guided by the following goals:
- Increase empirical understanding of how to identify materials for use by scholars.
- Increase empirical understanding of how to provide better access to materials for use by scholars.
- Identify meaningful characteristics of content that affect identification, retrieval, and other parameters.
- Identify data preprocessing and transformation issues encountered by scholars.
- Provide input to inform the architecture of the HTRC related to representation of collections, faceted browsing, identifiers, etc.

Papyri.info is dedicated to the study of ancient papyrological documents.  It offers links to papyrological resources, a customized search engine (called the Papyrological Navigator) capable of retrieving information from multiple related collections, and an editing application, the Papyrological Editor, which contributors can use to suggest emendations to PN texts.

The Papyrological Navigator aggregates and displays information from the Advanced Papyrological Information System (APIS), the Duke Databank of Documentary Papyri (DDbDP), the Heidelberger Gesamtverzeichnis der griechischen Papyrusurkunden Ägyptens (HGV), and Bibliographie Papyrologique (BP), as well as links to Trismegistos.

Computational processes generate lists: lists of numbers, lists of words, lists of coordinates, lists of properties. We transform these lists into more exalted forms -- visualizations, maps, information systems, software tools -- but the list remains the fundamental data structure of computing, from which most other structures are derived. Whenever we treat the world as data, we are nearly always creating lists. But what sort of *texts* are these, and can we consider them the same way that we consider other texts within the humanities? In this paper, I offer some meditations on the nature of lists, and suggest that it is the paucity of information they provide -- and the ways in which that paucity licenses narrative and explanation -- that allows us to imagine computational representations as texts that can play a fruitful role in the wider context of humanistic inquiry.

Austin et al. point out that the statistical methods that are at the heart of data mining are not able to distinguish real from spurious associations. Data mining employs the automated examination of enormous bodies of data. Its usefulness is thought to be proportional to the size of the data set that it collates; however, as the data set becomes larger and as the number of attributes that serve as potential relata increases, the number of potential relationships increases exponentially. Importantly, the number of spurious associations also increases. With enough data, no significance test will be stringent enough to provide assurance against the kind of results found in Austin et al. What is needed, according to Austin et al. is a “pre-specified plausible hypothesis.” For statistical analysis to be useful, the researcher must begin with a hypothesis, preferably a plausible one, if the research is to be valuable.

What exactly is a pre-specified plausible hypothesis and how can we generate it if data mining can’t do that for us? The question was posed some sixty years ago by the philosopher Nelson Goodman using different terms: Goodman believed that a critical question for epistemology was to distinguish between “projectible and non-projectible hypotheses.” One can more or less replace “pre-specified plausible hypothesis” with Goodman’s term “projectible hypothesis.” According to Goodman, when we seek to understand what hypothesis is (or is not) projectible, we do not come to the problem “empty-headed but with some stock of knowledge” which we use to determine what is (or is not) projectible. Projectible hypotheses will be those which do not conflict with other hypotheses that have been supported in the past. They will commonly use the same terminology of previously supported hypotheses. The terminology appearing in the hypotheses will have become “entrenched” in the language. This goes a long distance toward explaining why we don’t find the link between one’s astrological sign and medical conditions plausible. Twenty-first century Western medicine is not accustomed to linking astrological signs to ailments and so must find any hypothesis that does so implausible.

If Goodman is correct, then data mining is of little use without an historical understanding of the field of science to which the data pertains.

...

Here, we have another argument for allocating library resources to pay for librarians with deep subject expertise. As e-science develops, vendors will make more and more data sets available, regardless of their actual worth to researchers. To effectively choose the data sets that are of value, librarians must have a thorough understanding of the research needs of their patrons. To do this, they must have a deep understanding of the field. Unfortunately, with the excitement swirling around e-science, the mere access to large data sets threatens to become the be-all and end-all in collection management. If we aren’t careful, we may find ourselves with mountains of data from which everything and nothing can be concluded.

Humanities students often do not realize (or even imagine) that 1) they are capable of learning to write useful and practical computer programs within the course of a semester even if they have no prior background in programming; 2) the ability to write one’s own programs can be valuable for scholars in the humanities, especially because commercial software often does not address research needs in the humanities; and 3) practical computer programming, no less than reading, writing, and arithmetic, is a useful skill that is within the reach of any educated person regardless of academic specialization.

This course will introduce students to the role that computational methods can play in primary research and scholarship in the humanities, using as a technological framework eXtensible Markup Language (XML) and related technologies.

Tapas is the TEI Archival Publishing and Access Service for scholars and other creators of TEI data who need a place to publish their materials in different forms and ensure it remains accessible over time. Tapas is also for anyone interested in reading and exploring TEI data, and communicating with those that share that interest.

Washington DC--The Association of Research Libraries (ARL) has published Digital Humanities, SPEC Kit 326, which provides a snapshot of research library experiences with digital scholarship centers or services that support the humanities (e.g., history, art, music, film, literature, philosophy, religion, etc.) and the benefits and challenges of hosting them. The survey asked ARL libraries about the organization of these services, how they are staffed and funded, what services they offer and to whom, what technical infrastructure is provided, whether the library manages or archives the digital resources produced, and how services are assessed, among other questions.

This survey revealed that library-based support for the digital humanities is offered predominantly on an ad hoc basis. However, as demand for services supporting the digital humanities has grown, libraries have begun to re-evaluate their provisional service and staffing models. Many respondents expressed a desire to implement practices, policies, and procedures that would allow them to cope with increases in demand for services.

This SPEC Kit includes documentation from respondents that describes the mission or purpose of digital humanities centers, the services offered, policies and procedures, examples of digital projects, fellowship and grant opportunities, promotional materials, and repositories for digital projects.

Diction 6.0 uses dictionaries (word-lists) to search a text for these qualities:

· Certainty - Language indicating resoluteness, inflexibility, and completeness and a tendency to speak ex cathedra.

· Activity - Language featuring movement, change, the implementation of ideas and the avoidance of inertia.

· Optimism - Language endorsing some person, group, concept or event, or highlighting their positive entailments.

· Realism - Language describing tangible, immediate, recognizable matters that affect people's everyday lives.

· Commonality - Language highlighting the agreed-upon values of a group and rejecting idiosyncratic modes of engagement.

The purpose of this cookbook is to document and discuss the use of RDF in digital humanities. Its focus is specific applications as found in the real world, though a few general principles are suggested. It assumes that you’re vaguely comfortable with RDF and RDFa.

Positive law, inscribed in legal texts, entails an authority not inherent in literary texts, generating legal consequences that can have real effects on a person’s life and liberty. The interpretation of legal texts, necessarily a normative undertaking, resists the mechanical application of rules, though still requiring a measure of predictability, coherence with other relevant legal norms and compliance with constitutional safeguards. The present proliferation of legal texts on the internet (codes, statutes, judgments, treaties, doctrinal treatises) renders the selection of relevant texts and cases next to impossible. We may expect that systems to mine these texts to find arguments that support one’s case, as well as expert systems that support the decision-making process of courts, will end up doing much of the work.

This raises the question of the difference between human interpretation and computational pattern-recognition and the issue of whether this difference makes a difference for the meaning of law. Possibly, data mining will produce patterns that disclose habits of the minds of judges and legislators that would have otherwise gone unnoticed (reinforcing the argument of the ‘legal realists’ at the beginning of the 20th century). Also, after the data analysis it will still be up to the judge to decide how to interpret the results or up to the prosecution which patterns to engage in the construction of evidence (requiring a hermeneutics of computational patterns instead of texts). My focus in this paper regards the fact that the mining process necessarily disambiguates the legal texts in order to transform them into a machine-readable data set, while the algorithms used for the analysis embody a strategy that will co-determine the outcome of the patterns. There seems a major due process concern here to the extent that these patterns are invisible for the naked human eye and will not be contestable in a court of law, due to their hidden complexity and computational nature.

This position paper aims to explain what is at stake in the computational turn with regard to legal texts. This prepares for the question I want to put forward to those involved in distant reading and not-reading of texts: could a visualization of computational patterns constitute a new way of un-hiding the complexity involved, opening the results of computational ‘knowledge’ to citizens’ scrutiny?

The piece had two aims, namely to advocate for the addition of computational methods to our critical repertoire and to give a sample of recent computational work of the sort I find useful. I mention these goals up front because I think some of Rosen’s criticisms follow from the failure (mine, to be sure) to specify exactly what my essay was and was not doing and arguing. So to be clear: it was an argument for methodological expansion, especially for those of us working with contemporary sources, and a high-level synopsis of the results of that expansion.

Wilkens neglects other equally pressing problems with the computational practices he advocates—limitations that reveal themselves in the very analysis he proffers as a sample of the kind of scholarship such practices might enable. Two problems with Wilkens’s method strike me as most urgent. First and most glaringly, he inadvertently demonstrates how easily data may be misinterpreted to serve conclusions that are sought by the analyst. And second, though he and others doing similar work purport to offer analysis of neutral data sets—say, all the fiction published in a given year—by working with existing bibliographies they perpetuate the selection criteria that governed the initial compilation. Doing so artificially reifies bodies of texts that might in fact be far more heterogeneous and unruly.

A short illustration of the underlying problem of literary and cultural abundance, a quick tour of several techniques that we might use to expand our analytical repertoire so as to deal with that problem more effectively, and, finally, a consideration of the substantial challenges these methods face in the short-to-medium term.

This course gives you an opportunity to combine the hands-on production of multimodal scholarly communications with critical approaches to literature, new media, and digital culture. Here, by “multimodal,” I mean a material communication that demands more than one form of perception (e.g., distant reading, casual listening, scanning, or close watching) through more than one medium (e.g., audio, electronic text, image, video, or a database). With this definition in mind, throughout the term we will ask how creating knowledge through algorithms, networked environments, graphical expressions, and dynamic texts influences the theory and practice of literary studies. In so doing, we will intertwine three primary threads in digital literary studies (DLS): (1) the legacies of electronic literature (where DLS implies studying literature that is “digital-born”), (2) computational approaches to literary criticism (where DLS implies using digital technologies to interpret literature and/or compose scholarly communications), and (3) critical frameworks for computational culture (where DLS implies examining the recursive relationships between digital technologies and cultural assumptions, practices, and formations).

Great, clear example of text mining using Wordseer.

This special section of Profession addresses the evaluation of digital scholarship in the humanities, an issue that has been discussed both within the digital humanities community and at symposia on the future of scholarly publishing, in university departments’ and deans’ offices, at professional conferences, and in scholarly journals as well as mainstream media.

I feel it's important for users and watchers in the humanities, which is going gang-busters for GIS technology, to understand the differences between Pleiades and a ESRI geodatabase, an OGC-style feature/map service, or a conventional digital gazetteer. I don't think it's useful to try to precisely define "Un-GIS", but here are a few qualities that I think distinguish Pleiades from a typical geographic information system or spatial data infrastructure.

This is the REST service of CollateX. To call it, you can post witness data as specified below and get the collation result back in a number of formats.

Microservices developed by a European-funded development collective whose aim it is to promote the interoperability of the tools and methodology we use in the field of digital scholarly editing and research.

Micro services are small cloud deployed web services supporting specific tasks in (any) digital work flow pertaining to larger scholarly tasks. They are the small reusable web published building blocks of digital scholarly tools. They provide the basic solution to interoperability and sustainability for digital scholarly tools Interedition is striving for.

Collection of DH syllabi from Dan Cohen's syllabus dump.

ACLS Digital Innovation Fellowships are intended to support an academic year dedicated to work on a major scholarly project that takes a digital form. Projects may:

Address a consequential scholarly question through new research methods, new ways of representing the knowledge produced by research, or both;

Create new digital research resources;

Increase the scholarly utility of existing digital resources by developing new means of aggregating, navigating, searching, or analyzing those resources;

Propose to analyze and reflect upon the new forms of knowledge creation and representation made possible by the digital transformation of scholarship.

Whatever else it might be then, the digital humanities today is about a scholarship
(and a pedagogy) that is publicly visible in ways to which we are generally unaccustomed, a scholarship and pedagogy that are bound up with infrastructure in ways that
are deeper and more explicit than we are generally accustomed to, a scholarship and
pedagogy that are collaborative and depend on networks of people and that live an
active 24/7 life online.

The Hip-Hop Word Count is a searchable ethnographic database built from the lyrics of over 40,000 Hip-Hop songs from 1979 to present day.

The Hip-Hop Word Count describes the technical details of most of your favorite hip-hop songs. This data can then be used to not only figure out interesting stats about the songs themselves, but also describe the culture behind the music.

Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities.

The LaTeCH workshop series aims to provide a forum for researchers who are working on developing novel information technology for improved information access to data from the Humanities, Social Sciences, and Cultural Heritage.

Recent developments in the Humanities, Social Sciences, and Cultural Heritage draw an increasing interest from researchers in NLP in developing methods for data cleaning, semantic annotation, intelligent querying, linking, discovery and visualisation of interesting trends. Language technology has an important role to play in these processes, even for collections which are primarily non-textual, since text is the pervasive medium used for metadata. These fairly novel domains of application entail new challenges to NLP research, such as noisy text (e.g., due to OCR problems), non-standard, or archaic language varieties (e.g., historic language, dialects, mixed use of languages, ellipsis, transcription errors), the necessity to link data of diverse formats (e.g., text, database, video, speech) and languages, and the lack of available resources, such as dictionaries. Furthermore, often neither annotated domain data is available, nor the required funds to manually create it, thus forcing researchers to investigate (semi-) automatic resource development and domain adaptation approaches involving the least possible manual effort.

Japanese academic communities related to digital humanities.

These NEH grants support national or regional (multistate) training programs for scholars and advanced graduate students to broaden and extend their knowledge of digital humanities. Through these programs, NEH seeks to increase the number of humanities scholars using digital technology in their research and to broadly disseminate knowledge about advanced technology tools and methodologies relevant to the humanities. The projects may be a single opportunity or offered multiple times to different audiences. Institutes may be as short as a few days and held at multiple locations or as long as six weeks at a single site. The duration of a program should allow for full and thorough treatment of the topic.

In Meg Timney’s Digital Editions course at DHSI 2011, one of the concepts that was new to me was the development of user personas in planning a web-based project.  For many of us, the users that we want to ideally attract need to be tempered by the users who actually may use the site.  We need to design for multiple audiences, and we always need to be thinking (in my mind, anyway) about our most basic, least informed reader.  We need to be sure the site is user- friendly.  It is going to be a pedagogical tool, and as a result, we need it to be intuitive enough and accessible enough that even the “Bobbie Le Blaw”‘s of the world will be connected to our work.  By working through this process, I was forced to think more about user needs and competencies.

Temporal information is one of the essential components in many domains, especially those related to history. Up until the twentieth century, the Chinese used a lunisolar calendar with the title of an Emperor and a reign period to express temporal information. When describing a historical event in Chinese history, it is inadequate to use existing time ontologies as presented in the traditional Chinese way of thinking to capture and encode time. To date, no attention in the field has been given to modelling ancient Chinese time. In this paper, we identify the problems encountered when modelling Chinese time resulting from the distinctive nature of a non-western time scale. We design a new model of temporal information with combined approaches, which are more appropriate for Chinese dynasties, emperors, and reign periods, and apply the OWL-Time ontology onto the ancient Chinese lunisolar calendar. This approach can also be applied to other ancient time-keeping methods in non-western time scales.

Wolfram|Alpha is a powerful tool for finding information about the universe at large, but sometimes we are interested in a much smaller universe: our families. Genealogical research is an increasingly popular hobby, and one which Wolfram|Alpha can make easier using features across several of its subject areas.

During the late 19th and early 20th centuries, thousands of African Americans were lynched in the southern region of the United States. Previous research on lynching has been based largely on existing inventories of the victims of this form of extralegal punishment. While extremely valuable, these inventories suffer from important limitations in how much they can tell us about those victims, about the local conditions that affected the risk of an individual becoming the victim of a lynch mob, or about how the relationship between individual characteristics and the likelihood of being lynched may have varied across time and place. We are creating a new source of data that will introduce the individual victim into research on lynching, and provide information about the social relations and environment within which each victim was embedded. Our procedure merges information from an inventory of nearly 2,800 individuals who were lynched between 1882 and 1930 (Beck and Tolnay 2005 – See Table 1) with: 1) the original enumerators’ manuscripts for the Census immediately preceding the lynching (e.g., the 1880 Census for an 1885 lynching); and 2) geocode information on the county of lynching and county of residence for identified victims. The main contribution of this work is twofold: we develop new spatially-oriented methods of matching individuals to their census records; and, we create an historical data source that will allow researchers studying lynching, hate crimes, and ethnic conflict to link information for individuals and households to characteristics of the local context within which the victim resided or was lynched.

The goal is to accumulate a database of lynchings that took place at any date within the present borders of the United States.  We hope to make the lynching database analogous to the comprehensive list of legal executions compiled by M. Watt Espy of the Capital Punishment Research Project, Headland, Alabama.  We will collect information for individual lynching incidents on an ongoing basis.  

The notion of “primitives” as the “finite list of self-understood terms” from which, without recourse to further definitions or explanations, axiomatic logic may proceed, has (as you probably know) run into some difficulty in philosophy and mathematics, especially in the 20th century, but it’s not my purpose here to sort that out—I’m using the term “primitives” in a self-consciously analogical way, to refer to some basic functions common to scholarly activity across disciplines, over time, and independent of theoretical orientation.  These “self-understood” functions form the basis for higher-level scholarly projects, arguments, statements, interpretations—in terms of our original, mathematical/philosophical analogy, axioms.

Creative and compelling theoretical formulations of the archive have emerged from a host of disciplines in the last decade. Derrida and Foucault, as well as many other humanists and social scientists, have initiated a broadly interdisciplinary conversation about the nature of the archive. This literature suggests a confluence of interests among scholars, archivists, and librarians that is fueled by a shared preoccupation with the function and fate of the historical and scholarly record. The following essay provides an exploration and overview of this archival discourse.

This web site is designed to be a starting place, an entryway for scholars interested in beginning to explore the possibilities for digital tools, programs, and methods to empower and enhance their scholarship in the humanities. These essays and links are only a brief glimpse into the vast field of potential in the digital humanities, but we hope that they point outward to the field’s many possibilities.

RoSE is a research-oriented social environment for tracking and integrating relations between authors and documents in a combined “social-document graph.”

It allows users to learn about an author or idea from the evolving relationships between people-and-documents, people-and-people, and documents-and-documents.

Welcome to ENG 798.001/ENG 583.001, Introduction to Digital Humanities. This course will introduce you to the digital humanities (DH), a multi-disciplinary, international field comprising academic researchers, librarians, archivists, museum professionals, technologists, artists, designers, and professionals working in both the government and private sectors.

I’d like to propose a working definition of computational journalism as the application of computer science to the problems of public information, knowledge, and belief, by practitioners who see their mission as outside of both commerce and government. This includes the journalistic mainstay of “reporting” — because information not published is information not known — but my definition is intentionally much broader than that. To succeed, this young discipline will need to draw heavily from social science, computer science, public communications, cognitive psychology and other fields, as well as the traditional values and practices of the journalism profession.

An informal monthly gathering to discuss issues related to the digital humanities.

Lev Manovich's Spring 2011 Digital Humanities course at UCSD.

A brief selection of DH-related syllabi.

Alex's digital humanities / digital scholarship syllabi.

Every year, the Day of Digital Humanities application form asks participants for their own definition of Digital Humanities.

The tool we’ve built is code-named Woodchipper. It allows the user to search and select text from participating collections and display them as a visualization which shows relationships among texts.

This is another equally well trod genre: the "What is the digital humanities?" (a paltry 5 google results) or "What are the digital humanities?" (127,000 results).

List of tools for doing research into the "natively digital".

Finding trending topics using Google Books n-grams data and Apache Hive on Elastic MapReduce

The Hip-Hop Word Count is a searchable ethnographic database built from the lyrics of over 40,000 Hip-Hop songs from 1979 to present day.

...digital media and huge databases have enormous potential for supporting, preserving, and making available for study the kinds of underground knowledges and cultural productions outside the sphere of mainstream print that you're concerned about. This is the insurgent potential of the Internet and digital media--they can bypass established methods of fixation and legitimation of cultural products. But in academia these are subjects of interest to humanists--and sociologists and anthropologists. By contrast, when true disciplinary outsiders like Jean-Baptiste Michel and his team enter the arena of cultural history and cultural studies from the side of science and engineering, they must be looking to legitimate themselves by proving that their approach "works" for subjects that they imagine will be widely recognized as significant.

...the lesson that I would then focus on with my students is that what they are looking at in such a graph is nothing more or less than the frequency with which a word is used in book (and only books) published over the centuries. While such frequencies do reflect something, it is not clear from one graph just what that something is. So instead of an answer, a graph like this one is a doorway that leads to a room filled with questions, each of which must be answered by the historian before he or she knows something worth knowing.

There's a certain Words For Snowism in the online Google Books Ngrams tool, the suggestion that the more frequently a word is used, the more important it is in a collective unconscious of which the Google Books data set serves as a convenient index. This importance is not the same thing as significance, in the sense of significant digits or statistical significance; it's not the difference that makes a difference, but rather a psychologized importance--attachment, cathexis. Which is really kind of garbage.

By kind permission of James Clifford, this is the text of a talk he delivered at “The University We Are For,” a conference organized by David Theo Goldberg and Wendy Brown at UC Berkeley (11/5/10).

I fear that the digital humanities is becoming dominated by purely technical concerns of implementation, I suspect in no small measure under pressure to assist the other disciplines survive their crises of confidence and funding — as well as to avoid the severe challenges computing presents to all disciplines. One sign of this industrialization is the spread of technological orthodoxy under the banner of technical standards.