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Classification of web-based Digital Humanities projects leveraging information visualisation techniques
<h2>Description</h2> <p>This dataset contains a list of 186 Digital Humanities projects leveraging information visualisation methods. Each project has been classified according to visualisation and interaction techniques, narrativity and narrative solutions, domain, methods for the representation of uncertainty and interpretation, and the employment of critical and custom approaches to visually represent humanities data.</p> <p> </p> <h2>Classification schema: categories and columns</h2> <p>The <code>project_id</code> column contains unique internal identifiers assigned to each project. Meanwhile, the <code>last_access</code> column records the most recent date (in DD/MM/YYYY format) on which each project was reviewed based on the web address specified in the <code>url</code> column.<br>The remaining columns can be grouped into descriptive categories aimed at characterising projects according to different aspects:</p> <p> </p> <p><strong>Narrativity.</strong> It reports the presence of information visualisation techniques employed within narrative structures. Here, the term narrative encompasses both author-driven linear data stories and more user-directed experiences where the narrative sequence is determined by user exploration [1]. We define 2 columns to identify projects using visualisation techniques in narrative, or non-narrative sections. Both conditions can be true for projects employing visualisations in both contexts. Columns:</p> <ul> <li> <p><code>non_narrative</code> (boolean)</p> </li> <li> <p><code>narrative</code> (boolean)</p> </li> </ul> <p> </p> <p><strong>Domain.</strong> The humanities domain to which the project is related. We rely on [2] and the chapters of the first part of [3] to abstract a set of general domains. Column:</p> <ul> <li> <p><code>domain</code> (categorical):</p> </li> <ul> <li> <p>History and archaeology</p> </li> <li> <p>Art and art history</p> </li> <li> <p>Language and literature</p> </li> <li> <p>Music and musicology</p> </li> <li> <p>Multimedia and performing arts</p> </li> <li> <p>Philosophy and religion</p> </li> <li> <p>Other: both extra-list domains and cases of collections without a unique or specific thematic focus.</p> </li> </ul> </ul> <p> </p> <p><strong>Visualisation of uncertainty and interpretation.</strong> Buiding upon the frameworks proposed by [4] and [5], a set of categories was identified, highlighting a distinction between precise and impressional communication of uncertainty. Precise methods explicitly represent quantifiable uncertainty such as missing, unknown, or uncertain data, precisely locating and categorising it using visual variables and positioning. Two sub-categories are interactive distinction, when uncertain data is not visually distinguishable from the rest of the data but can be dynamically isolated or included/excluded categorically through interaction techniques (usually filters); and visual distinction, when uncertainty visually “emerges” from the representation by means of dedicated glyphs and spatial or visual cues and variables. On the other hand, impressional methods communicate the constructed and situated nature of data [6], exposing the interpretative layer of the visualisation and indicating more abstract and unquantifiable uncertainty using graphical aids or interpretative metrics. Two sub-categories are: ambiguation, when the use of graphical expedients—like permeable glyph boundaries or broken lines—visually convey the ambiguity of a phenomenon; and interpretative metrics, when expressive, non-scientific, or non-punctual metrics are used to build a visualisation. Column:</p> <ul> <li> <p><code>uncertainty_interpretation</code> (categorical):</p> </li> <ul> <li> <p>Interactive distinction</p> </li> <li> <p>Visual distinction</p> </li> <li> <p>Ambiguation</p> </li> <li> <p>Interpretative metrics</p> </li> </ul> </ul> <p> </p> <p><strong>Critical adaptation.</strong> We identify projects in which, with regards to at least a visualisation, the following criteria are fulfilled: 1) avoid repurposing of prepackaged, generic-use, or ready-made solutions; 2) being tailored and unique to reflect the peculiarities of the phenomena at hand; 3) avoid simplifications to embrace and depict complexity, promoting time-consuming visualisation-based inquiry. Column:</p> <ul> <li> <p><code>critical_adaptation</code> (boolean)</p> </li> </ul> <p> </p> <p><strong>Non-temporal visualisation techniques.</strong> We adopt and partially adapt the terminology and definitions from [7]. A column is defined for each type of visualisation and accounts for its presence within a project, also including stacked layouts and more complex variations. Columns and inclusion criteria:</p> <ul> <li> <p><code>plot</code> (boolean): visual representations that map data points onto a two-dimensional coordinate system.</p> </li> <li> <p><code>cluster_or_set</code> (boolean): sets or cluster-based visualisations used to unveil possible inter-object similarities.</p> </li> <li> <p><code>map</code> (boolean): geographical maps used to show spatial insights. While we do not specify the variants of maps (e.g., pin maps, dot density maps, flow maps, etc.), we make an exception for maps where each data point is represented by another visualisation (e.g., a map where each data point is a pie chart) by accounting for the presence of both in their respective columns.</p> </li> <li> <p><code>network</code> (boolean): visual representations highlighting relational aspects through nodes connected by links or edges.</p> </li> <li> <p><code>hierarchical_diagram</code> (boolean): tree-like structures such as tree diagrams, radial trees, but also dendrograms. They differ from networks for their strictly hierarchical structure and absence of closed connection loops.</p> </li> <li> <p><code>treemap</code> (boolean): still hierarchical, but highlighting quantities expressed by means of area size. It also includes circle packing variants.</p> </li> <li> <p><code>word_cloud</code> (boolean): clouds of words, where each instance’s size is proportional to its frequency in a related context</p> </li> <li> <p><code>bars</code> (boolean): includes bar charts, histograms, and variants. It coincides with “bar charts” in [7] but with a more generic term to refer to all bar-based visualisations.</p> </li> <li> <p><code>line_chart</code> (boolean): the display of information as sequential data points connected by straight-line segments.</p> </li> <li> <p><code>area_chart</code> (boolean): similar to a line chart but with a filled area below the segments. It also includes density plots.</p> </li> <li> <p><code>pie_chart</code> (boolean): circular graphs divided into slices which can also use multi-level solutions.</p> </li> <li> <p><code>plot_3d</code> (boolean): plots that use a third dimension to encode an additional variable.</p> </li> <li> <p><code>proportional_area</code> (boolean): representations used to compare values through area size. Typically, using circle- or square-like shapes.</p> </li> <li> <p><code>other</code> (boolean): it includes all other types of non-temporal visualisations that do not fall into the aforementioned categories.</p> </li> </ul> <p> </p> <p><strong>Temporal visualisations and encodings.</strong> In addition to non-temporal visualisations, a group of techniques to encode temporality is considered in order to enable comparisons with [7]. Columns:</p> <ul> <li> <p><code>timeline</code> (boolean): the display of a list of data points or spans in chronological order. They include timelines working either with a scale or simply displaying events in sequence. As in [7], we also include structured solutions resembling Gantt chart layouts.</p> </li> </ul> <ul> <li> <p><code>temporal_dimension</code> (boolean): to report when time is mapped to any dimension of a visualisation, with the exclusion of timelines. We use the term “dimension” and not “axis” as in [7] as more appropriate for radial layouts or more complex representational choices.</p> </li> <li> <p><code>animation</code> (boolean): temporality is perceived through an animation changing the visualisation according to time flow.</p> </li> <li> <p><code>visual_variable</code> (boolean): another visual encoding strategy is used to represent any temporality-related variable (e.g., colour).</p> </li> </ul> <p> </p> <p><strong>Interaction techniques.</strong> A set of categories to assess affordable interaction techniques based on the concept of user intent [8] and user-allowed data actions [9]. The following categories roughly match the “processing”, “mapping”, and “presentation” actions from [9] and the manipulative subset of methods of the “how” an interaction is performed in the conception of [10]. Only interactions that affect the visual representation or the aspect of data points, symbols, and glyphs are taken into consideration. Columns:</p> <ul> <li> <p><code>basic_selection</code> (boolean): the demarcation of an element either for the duration of the interaction or more permanently until the occurrence of another selection.</p> </li> <li> <p><code>advanced_selection</code> (boolean): the demarcation involves both the selected element and connected elements within the visualisation or leads to brush and link effects across views. Basic selection is tacitly implied.</p> </li> <li> <p><code>navigation</code> (boolean): interactions that allow moving, zooming, panning, rotating, and scrolling the view but only when applied to the visualisation and not to the web page. It also includes “drill” interactions (to navigate through different levels or portions of data detail, often generating a new view that replaces or accompanies the original) and “expand” interactions generating new perspectives on data by expanding and collapsing nodes.</p> </li> <li> <p><code>arrangement</code> (boolean): methods to organise visualisation elements (symbols, glyphs, etc.) or multi-visualisation layouts spatially through drag and drop or according to a criterion via more automatic triggers.</p> </li> <li> <p><code>change</code> (boolean): visual encoding alterations involving different aspects of visualisation as a whole: the same content is presented with another visualisation technique; the change involves symbols or glyphs aspect (colour, size, shape, etc.); the visualisation type is unaltered, but the layout variant changes (e.g., to stacked layouts); or other changes like axes inversion and scale modifications. The presence of all the visualisation techniques involved in a change is reported.</p> </li> <li> <p><code>visualisation_filter</code> (boolean): filters to exclude or include visualisation elements with respect to defined criteria, without reloading or generating a new visualisation. Unlike options triggering the fetch of new data to alter the visualisation content, filters seamlessly operate on existing visual elements.</p> </li> <li> <p><code>collection_filter</code> (boolean): the interaction with visualised elements acts as a filter for a related collection or list of items (e.g., clicking a region on a map filters a list of items according to spatial metadata).</p> </li> <li> <p><code>aggregation</code> (boolean): changes to the granularity of visual elements according to a variable. It produces either visual data summarisations or segregations.</p> </li> <li> <p><code>btfw_interaction</code> (boolean): to identify the use of “breaking the fourth wall interactions” as defined [11]. It applies only to narratives.</p> </li> </ul> <p> </p> <p><strong>Narrative flow factors.</strong> Other categories aim to identify patterns in the design of narrative solutions. It is worth noticing that a project with multiple and diverse narratives can potentially report multiple design choices for the same column. Part of the factors and definitions from [12] are here re-used and adapted.</p> <p><em>Story layout </em>columns define the layout, or genre, of the narrative format:</p> <ul> <li> <p><code>document_layout</code> (boolean)</p> </li> <li> <p><code>slideshow_layout</code> (boolean)</p> </li> <li> <p><code>hybrid_layout</code> (boolean): mixing document and slideshow layouts.</p> </li> <li> <p><code>other_layout</code> (boolean): more complex solutions.</p> </li> </ul> <p><em>Role of visualisation</em> columns describe the role visualisations detain with respect to the entire story, in particular, with reference to the textual part of the narratives:</p> <ul> <li><code>equal_role</code> (boolean): visualisations and text play an equal role in the narrative.</li> <li><code>figure_role</code> (boolean): visualisations are supporting elements compared to the role of text.</li> <li><code>annotated_role</code> (boolean): visualisations are the drivers of the narrative.</li> </ul> <p><em>Story progression</em> columns categorise the shape of possible story paths:</p> <ul> <li> <p><code>linear_progression</code> (categorical): strongly author-driven or user-directed narrative. Possible values specify the potential to skip certain parts while not having a fully explorative experience:</p> </li> <ul> <li> <p>Skip</p> </li> <li> <p>No-skip</p> </li> </ul> <li> <p><code>user_directed</code> (bool): users can select a path among multiple alternatives and compose narrative pieces, providing a broder degree of interaction and exploration possibilities [1]. If a linear path can be suggested, here it remains merely one option among many others. Differently from a linear-skip approach, it has a low level of guidance oriented towards linear navigation.</p> </li> </ul> <p><em>Navigation input </em>columns define the ways users can move through the narrative:</p> <ul> <li> <p><code>button_input</code> (boolean)</p> </li> <li> <p><code>scroll_input</code> (boolean)</p> </li> <li> <p><code>slider_input</code> (boolean)</p> </li> </ul> <p><em>Navigation progress </em>columns describe methods through which the reader perceives its placement within the narrative:</p> <ul> <li> <p><code>text_progression</code> (boolean): text or numbers act as signifiers for user position.</p> </li> <li> <p><code>dots_progression</code> (boolean)</p> </li> <li> <p><code>visualisation_progression</code> (boolean): the visualisation used in the narrative, or a visualised progress widget acts as a signifier for user position.</p> </li> </ul> <p><em>Level of control </em>columns describe how much control a reader has over the text, visualisations, and animated transitions. Control could be discrete (D) when it triggers the motion, continuous (C) when it can act throughout all the keyframes, or hybrid (H) if it supports aspects of both. When animation is absent, control can be not available (NA). In particular, while visualisation control is related to the visualisation as a whole (e.g., the entire scatter plot moving up or down the page), the animated transition is related to more specific, data-relevant motion.<br>Columns:</p> <ul> <li> <p><code>text_control</code> (categorical):</p> </li> <ul> <li> <p>D</p> </li> <li> <p>C</p> </li> <li> <p>H</p> </li> </ul> <li> <p><code>visualisation_control</code> (categorical):</p> </li> <ul> <li> <p>D</p> </li> <li> <p>C</p> </li> <li> <p>H</p> </li> </ul> <li> <p><code>animation_control</code> (categorical):</p> </li> <ul> <li> <p>D</p> </li> <li> <p>C</p> </li> <li> <p>H</p> </li> <li> <p>NA</p> </li> </ul> </ul> <p> </p> <h2>References</h2> <p>[1] E. Segel and J. Heer, “Narrative Visualization: Telling Stories with Data,” IEEE Trans. Visual. Comput. Graphics, vol. 16, no. 6, pp. 1139–1148, 2010, doi: 10.1109/TVCG.2010.179.</p> <p>[2] M. Terras, J. Nyhan, and E. Vanhoutte, Defining Digital Humanities: A Reader. Routledge, 2016.</p> <p>[3] S. Schreibman, R. G. Siemens, and J. Unsworth, Eds., A companion to digital humanities. in Blackwell companions to literature and culture, no. 26. Malden, MA: Blackwell Pub, 2004.</p> <p>[4] C. Kinkeldey, A. M. MacEachren, and J. Schiewe, “How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies,” The Cartographic Journal, vol. 51, no. 4, pp. 372–386, 2014, doi: 10.1179/1743277414Y.0000000099.</p> <p>[5] G. Panagiotidou, H. Lamqaddam, J. Poblome, K. Brosens, K. Verbert, and A. Vande Moere, “Communicating Uncertainty in Digital Humanities Visualization Research,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 635–645, Jan. 2023, doi: 10.1109/TVCG.2022.3209436.</p> <p>[6] J. Drucker, “Humanities Approaches to Graphical Display,” Digital Humanities Quarterly, vol. 5, no. 1, 2011, Accessed: Sep. 17, 2024. [Online]. Available: <a href="https://www.digitalhumanities.org/dhq/vol/5/1/000091/000091.html">https://www.digitalhumanities.org/dhq/vol/5/1/000091/000091.html</a></p> <p>[7] F. Windhager et al., “Visualization of Cultural Heritage Collection Data: State of the Art and Future Challenges,” IEEE Trans. Visual. Comput. Graphics, vol. 25, no. 6, pp. 2311–2330, Jun. 2019, doi: 10.1109/TVCG.2018.2830759.</p> <p>[8] J. S. Yi, Y. A. Kang, J. Stasko, and J. A. Jacko, “Toward a Deeper Understanding of the Role of Interaction in Information Visualization,” IEEE Trans. Visual. Comput. Graphics, vol. 13, no. 6, pp. 1224–1231, 2007, doi: 10.1109/TVCG.2007.70515.</p> <p>[9] E. Dimara and C. Perin, “What is Interaction for Data Visualization?,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 119–129, Jan. 2020, doi: 10.1109/TVCG.2019.2934283.</p> <p>[10] M. Brehmer and T. Munzner, “A Multi-Level Typology of Abstract Visualization Tasks,” IEEE Trans. Visual. Comput. Graphics, vol. 19, no. 12, pp. 2376–2385, 2013, doi: 10.1109/TVCG.2013.124.</p> <p>[11] Y. Shi, T. Gao, X. Jiao, and N. Cao, “Breaking the Fourth Wall of Data Stories Through Interaction,” IEEE Trans. Visual. Comput. Graphics, pp. 1–11, 2022, doi: 10.1109/TVCG.2022.3209409.</p> <p>[12] S. McKenna, N. Henry Riche, B. Lee, J. Boy, and M. Meyer, “Visual Narrative Flow: Exploring Factors Shaping Data Visualization Story Reading Experiences,” Computer Graphics Forum, vol. 36, no. 3, pp. 377–387, 2017, doi: 10.1111/cgf.13195.</p> <p> </p> <h2>Fundings</h2> <p>Project funded by the European Union – NextGenerationEU under the National Recovery and Resilience Plan (NRRP), Investment I.4.1 - Borse PNRR Patrimonio Culturale.</p>
Dataset for presentation Le Digital Humanities nei corsi di studio e di dottorato: questioni formative, disciplinari, istituzionali
<p>This is the dataset upon which the slides of my talk "Le Digital Humanities nei corsi di studio e di dottorato: questioni formative, disciplinari, istituzionali" was based.</p> <ul> <li><strong><em>Singoli insegnamenti di informatica umanistica in Italia (responses).csv </em></strong>is a CSV file including the 'raw' replies of the survey mentioned in the slides. The column "User display name" represents the user filling the survey. It is always "Anonymous user", except when I replyied myself: in this case it is "ilbuonme"</li> <li><strong><em>insegnamenti.ods</em></strong> is an ODS spreadsheet including data and graphs on individual Digital Humanities classes in Italian universities. This spreadsheet derives from the CSV file, but includes additional information deriving from my personal research.</li> <li><em><strong>cds.ods</strong></em> is an ODS spreadsheet including data and graphs on BA and MA programs in the Digital Humanities in Italy. It is not related to the CSV file.</li> </ul>
Dataset do DH2020 [The Lusophone Digital Humanities and What they (we) are doing from the South: textual corpus analysis and FAIR principles to tackle Hegemony]
<p>Planilha de dados recuperados do Google Scholar utilizado na análise e apresentação da pesquisa empírica intitulada - <strong>The Lusophone Digital Humanities and What they (we) are doing from the South: textual corpus analysis and FAIR principles to tackle Hegemony </strong>- no evento <strong>DH2020 Ottawa</strong>: <a href="https://hcommons.org/deposits/item/hc:32051/">https://hcommons.org/deposits/item/hc:32051/</a></p>
Abstracts from the Digital Humanities Conference 2005-2018
<p>Plain-text versions of the <a href="http://adho.org/conference">Digital Humanities Conference</a>s' books of abstracts (2005-2018).<br> </p>
10 Women of Digital Humanities: An Analysis of Linked Open Data
<p>This is a spreadsheet of linked open data, including tweets of the 10 female scholars randomly chosen for this project. This is an experimental study, and was prepared for my own learning. Presentation prepared for a Masters seminar in Information Science at uOttawa in Winter 2020. </p> <p>Linked Open Data in the Humanities was taught by Prof. Constance Crompton.</p> <p>10 Women of Digital Humanities: An Analysis of Linked Open Data</p> <p>tags: dh, digital humanities, feminist dh, computational analysis, Voyant, word clouds, vizualization, digital identifiers, open scholarship</p>
Data Lakes for Digital Humanities
<p>Traditional data in Digital Humanities projects bear various formats (structured, semi-structured, textual) and need substantial transformations (encoding and tagging, stemming, lemmatization, etc.) to be managed and analyzed. To fully master this process, we propose the use of data lakes as a solution to data siloing and big data variety problems. We describe data lake projects we currently run in close collaboration with researchers in humanities and social sciences and discuss the lessons learned running these projects.</p>
List of Digital Humanities journals
<p>The dataset contains a list of Digital Humanities journals. </p> <p>It contains: an arbitrary identified, electronic and print ISSN (when it exists), the title, the URL, a column describing the level of digital humanities publications that it stores. </p> <p>The DH level field may have four values:</p> <ol> <li><strong>Exclusively</strong> (the journal is completely devoted as publishing Digital Humanities articles)</li> <li><strong>Significantly </strong>(a significant set of publications deal with Digital Humanities)</li> <li><strong>Marginally</strong> (only a small set of publications are Digital Humanities)</li> <li><strong>Mega-journal </strong></li> </ol> <p> </p> <p> </p> <p> </p>
Closure of the 9th European Summer University in Digital Humanities "Culture & Technology"
<p>Closure of the 9th European Summer University in Digital Humanities "Culture & Technology"</p>
Opening of the 9th European Summer University in Digital Humanities "Culture & Technology"
<p>Opening of the 9th European Summer University in Digital Humanities "Culture & Technology"</p>
A Digital Humanities Approach to Textual Emendations with a Special Focus on Ἰουδαίαν in Acts 2:9
<p>The original documents of almost all ancient writings have been lost, and the writings of the New Testament form no exception. Therefore, before any interpretation of a New Testament text, a researcher first must face the challenge of establishing its original wording by critically evaluating the differences in the existing manuscripts. The discipline of textual criticism provides criteria for systematic evaluation of such texts. Besides identified differences, there are texts where the different manuscripts do correspond, but where the content of the text puzzles the researcher. In these cases, some researchers assume a corruption of the text, which could have been caused by a variety of intentional and unintentional errors, and therefore emend the text by conjecture.<br> Any conjecture starts with an observation on the text, in which a critic is guided by some preunderstanding that leads to the detection of an oddity. After the detection of the textual problem, the critic needs to suggest an alternative that (1) fits the grammatical function of the disputed reading, (2) makes sense in the internal logic of the text, and (3) solves the assumed difficulties. Therefore, the credibility of a conjecture is restricted by grammar, semantics, and its historical, cultural, and geographical suitedness. Finally, the critic must also explain how the attested reading or readings could have originated from the proposed conjecture. Usually, a very early corruption during the transcription process is assumed, which could have been caused by palaeographic or phonetic confusion of letters.<br> This research proposes a method to estimate the probability of palaeographic confusion to explain the origination of conjectural emendations. Therefore, it introduces the confusion distance, a quantitative metric that indicates the relative proximity in orthography of alternative readings. This metric is based on the Levenshtein edit distance but is here expanded to account for the probability of confusion of a particular combination of (adjacent) letters and functionality has been added to evaluate three additional operations to mimic more sophisticated character confusion.<br> The resulting distances between the conjectured emendations and the manuscript readings are subsequently translated to a two-dimensional non-geographical space utilizing Multi-Dimensional Scaling and analyzed spatially to evaluate the probability of the originality of variant readings or textual emendations.<br> The remainder of the presentation will apply this method to the case of Ἰουδαίαν in Acts 2:9. Therefore, it will first present the issues which have challenged exegetes over time. Next, it will provide a short diachronic overview of the suggestions to overcome these challenges and finally it will approach the issue by testing whether reverse engineering might provide a suitable alternative to Ἰουδαίαν.<br> After presenting the case study its outcomes will be evaluated to assess the method’s viability to aid New Testament interpretation.</p> <p><br> van Altena, Vincent, Jan Krans, Henk Bakker, Balász Dukai, and Jantien Stoter. “Spatial Analysis of New Testament Textual Emendations Utilizing Confusion Distances.” OT 5.1 (2019): 44–65.<br> van Altena, Vincent, Jan Krans, Henk Bakker, and Jantien Stoter. “Ἰουδαίαν in Acts 2:9: A Diachronic Overview of Its Conjectured Emendations.” OT 6.1 (2020): 306–318.<br> van Altena, Vincent, Jan Krans, Henk Bakker, and Jantien Stoter. “Ἰουδαίαν in Acts 2:9: Reverse Engineering Textual Emendations.” OT 6.1 (2020): 378–391.</p>
CIS OCR Workshop v1.0: OCR and postcorrection of early printings for digital humanities
<p>The 2-day CIS OCR Workshop on "OCR and postcorrection of early printings for digital humanities" originally held at LMU, Munich 14/15 September 2015 (see http://www.cis.lmu.de/ocrworkshop).</p> <p>Release date: 2016-02-25</p> <p><br /> CIS OCR Workshop by Uwe Springmann, Florian Fink is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.</p>
Dataset from "Merging Digital Humanities and Discourse Analysis in the Study of COVID-19 Vaccine Distribution in Norwegian Newspapers" (Sverdljuk et al. 2022)
<p>Contains URNs (identifiers) for the newspapers used in the corpus study "Merging Digital Humanities and Discourse Analysis in the Study of COVID-19 Vaccine Distribution in Norwegian Newspapers".</p> <p>For each subcorpus there is an Excel file containing references to the objects used, together with basic metadata.</p> <p>The corpus definitions can be used in various webapps of the DH-LAB at the National Library of Norway, e.g.:</p> <p><a href="https://beta.nb.no/dhlab/concordances/">https://beta.nb.no/dhlab/concordances/</a></p> <p><a href="https://beta.nb.no/dhlab/collocations/">https://beta.nb.no/dhlab/collocations/</a></p> <p>See more at <a href="https://www.nb.no/dh-lab/">https://www.nb.no/dh-lab/</a></p>
Digital Phase Contrast on Primary Dermal Human Fibroblasts cells
<p><strong>Name</strong>: Digital Phase Contrast on Primary Dermal Human Fibroblasts cells </p> <p><strong>Data type</strong>: Paired microscopy images (Digital Phase Contrast, <em>square rooted</em>) and corresponding labels/masks used for cellpose training (the corresponding Brightfield images are also present), organized as recommended by <a href="https://cellpose.readthedocs.io/en/latest/train.html">cellpose documentation</a>.</p> <p><strong>Microscopy data type</strong>: Light microscopy (Digital Phase Contrast and Brighfield )</p> <p><strong>Manual annotations</strong>: Labels/masks obtained via manual segmentation. For each region, all cells were annotated manually. Uncertain objects (Dust, fused cells) were left unannotated, so that the cellpose model (10.5281/zenodo.6023317) may mimic the same user bias during prediction. This was particularly necessary due to the accumulation of floating debris in the center of the well.</p> <p><strong>Microscope</strong>: Perkin Elmer Operetta microscope with a 10x 0.35 NA objective</p> <p><strong>Cell type</strong>: Primary Dermal Human Fibroblasts cells</p> <p><strong>File format</strong>: .tif (16-bit for DPC and 16-bit for the masks)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 634 nm)</p> <p>NOTE : This dataset was used to train cellpose model ( 10.5281/zenodo.6023317 )</p> <p> </p>
"Wissen schaffen (lassen!?)". Workflows mit Generativer KI in den Digital Humanities
<p>Die rasante Entwicklung von generativen KI-Technologien stellt eine bedeutende Veränderung für die Forschungspraxis nicht nur in den Digital Humanities dar. Dieser Vortrag untersucht den Einsatz von GPT-4-Tier LLM (Gemini Advanced und Claude 3) sowie deren Möglichkeiten und Grenzen in verschiedenen Forschungsprojekten der Digital Humanities. Der Fokus liegt dabei auf Workflows wie der Datenerfassung, Transkription, Übersetzung, Datenmodellierung, Datengenerierung oder -analyse sowie der Visualisierung geisteswissenschaftlicher Daten. Anhand ausgewählter Fallstudien wird die Integration von generativer KI in diese Prozesse dargestellt, wobei sowohl die Automatisierung von Standardaufgaben als auch die Unterstützung komplexerer, analytischer und anspruchsvoller Tätigkeiten wie Datenmodellierung thematisiert werden. Haben generative KI-Modelle, wenn sie im Einklang mit menschlicher Expertise und komplementären Systemen eingesetzt werden, das Potenzial, die Effizienz und Tiefe (digitaler) geisteswissenschaftlicher Forschung zu steigern? Die Studie betont auch die Notwendigkeit, die Grenzen und Herausforderungen, wie die Abhängigkeit von großen Technologieunternehmen beim Einsatz von generativer KI in den Digital Humanities kritisch zu hinterfragen.</p>
Abstracts from the Digital Humanities conference in Kraków in 2016 (DH2016)
<p>Abstracts from the Digital Humanities conference in Kraków in 2016 (DH2016).</p>
Abstracts and Metadata from the Digital Humanities Conference 2015 in Sydney (DH2015)
<p>Initial release of abstracts and metadata.</p>
arabic-digital-humanities/root-extraction-validation-data: 0.1.0
<p>Dataset for evaluating root extraction</p> <p>This dataset contains data to evaluate the roots extracted by Arabic stemmers and morphological analyzers.</p> <p>The dataset was created in the context of the <a href="https://www.esciencecenter.nl/project/bridging-the-gap">Bridging the Gap project</a>.</p> <p>The <code>txt</code> directory contains the text files for which roots were extracted, and <code>gs</code> contains csv files with columns <code>word</code> and <code>root</code>, containing the words from the text and the manually corrected roots. If multiple roots apply, they have been separated using a backslash (<code>\</code>). If the word is a letter, the root is <code>#</code>.</p> <p>The text files have been analyzed using <a href="http://arabic.emi.ac.ma/safar/">SAFAR</a>, using <a href="https://github.com/arabic-digital-humanities/research-scripts">our software</a>. The directories <code>khoja</code>, <code>isri</code>, and <code>alkhalil</code> contain the output xml files.</p> <p>License</p> <p> </p> <p>The data in this repository is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <ul> <li>The text fragments have been taken from the <a href="https://alraqmiyyat.github.io/OpenITI/">OpenITI project</a>.</li> <li>The stopword list was created by Maksim Abdul Latif.</li> </ul>
Opening of the 10th European Summer University in Digital Humanities "Culture & Technology"
<p>Welcoming by the Vice-Rector for Research and Young Scholars of the University of Leipzig, Prof. Dr. Erich Schröger.</p> <p>Handing over of the certificates to scholarship, fellowship and bursary winners by Elisabeth Burr et al.</p> <p>Introduction into the programme and philosophy of the European Summer University in Digital Humanities "Culture & Technology" by Elisabeth Burr</p>
Digital Humanities - A diverse field
<p>During the first Project Presentation Session on <strong>Wednesday 24.07.2019</strong> 14:15 - 15:45 the following 3 projects were presented:</p> <ul> <li><strong>Emma Elizabeth Goodwin</strong> (University of Oxford, Great Britain): "A social machine for transcribing the Song of the First Crusade"</li> <li><strong>Lars Langer</strong> (Leipzig University, Germany): "Quantifying Nature's Contribution to Communication (NCC)"</li> <li><strong>Patience Haggin</strong> (Independent, United States of America): "<em>Zeno's Conscience</em>: Three English Translations of Italo Svevo's <em>Coscienza di Zeno</em>?"</li> </ul>
Tweet IDs for the #brexit tweet dataset collected for the Helsinki Digital Humanities Hackathon 2019
<p>This dataset contains lists of tweet ids for the tweets used as material by the "Brexit in Transna­tional So­cial Me­dia" group in the <a href="http://heldig.fi/dhh19/">Helsinki Digital Humanities Hackathon 2019</a>.</p> <p>Due to restrictions in Twitter's terms of service, the full tweet dataset cannot be made public. However, Twitter allows the publication of tweet ids, from which the dataset can be reconstituted, <em>with the exception of deleted tweets</em>.</p> <p>The dataset was gathered as follows: Between 2019-01-22T09:19Z and 2019-04-15T14:42Z, a <a href="https://github.com/DocNow/twarc">Twarc</a> version 1.6.1 script was called hourly to retrieve and archive tweets from Twitter matching the #brexit hashtag using the Twitter search API. All 5,547,585 tweet IDs returned by this run are listed in the file <code>original_ids.txt.gz</code>.</p> <p>However, upon further inspection, problems were identified in the archiving. For an unidentified reason, gathering did not occur between 2019-02-13T06:17Z and 2019-02-26T09:24Z. In addition, the Twarc script had been run without the <code>--extended</code> argument, so the tweet data contained only truncated contents for many tweets.</p> <p>Due to this, a decision was taken to rehydrate a new dataset of tweets falling between 2019-02-26T09:24Z and 2019-04-15T14:42Z. Of the original 4,197,059 tweets gathered for this time period (listed in <code>continuous_ids.txt</code>), 3,941,653 could be rehydrated (i.e., they had not been deleted in the interim). The ids of tweets in this dataset are listed in the file <code>continuous_rehydrated_ids.txt</code>.</p> <p>Finally, out of these rehydrated tweets, a subset was derived that filtered out all tweets that were pure retweets. This subset consisted of 1,104,514 tweets, whose ids are listed in the file <code>continuous_rehydrated_no_retweets_ids.txt</code>.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.