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4,404 results for “digitization”
A droplet digital polymerase chain reaction assay to detect rare helminth parasites infecting natural host populations (Vancouver Island 2023, University of Wisconsin Madison Laboratory colony 2024)
Helminth infections represent a significant challenge to human, livestock, and wildlife health, yet they remain relatively under-studied, especially in terms of their ecological impacts. Better understanding of how these parasites spread in wildlife populations could improve our ability to predict and manage disease transmission across various species. Traditional detection methods, such as visually identifying parasites in environmental samples or infected hosts, often fall short, especially during the early stages of infection when parasite loads are minimal. In this study, we introduce a highly sensitive and precise droplet digital PCR (ddPCR) assay that quantifies helminth DNA in aquatic habitats, focusing on the 18S rRNA gene as a marker. These data utilize the model host-parasite system between the tapeworm Schistocephalus solidus, and its cyclopoid copepod host, Acanthocyclops robustus. The molecular assays are built around creating an infection standard in the lab, where copepods were singly infected with a single tapeworm parasite. We extracted DNA from 100 infected adults and used this as a standard to translate gene copy numbers from the ddPCR reactions to actual animal values. After creating a known lab standard, we then use the generated probes and primers to detect (and quantify!) infection burdens in field samples, which include both water filter samples (eDNA) and zooplankton tows from several lakes around Vancouver Island, B.C. The data presented here include well-specific data from ddPCR runs (amplitude of individual level oil droplets in the reaction) as well as each ddPCR analysis in its entirety. In order to prove the specificity of probes and probe-primers, we include here ddPCR runs of closely related helminth species, Schistocephalus cotti and Schistocephalus pungitii. We also consider the binding to another genera of copepod, the calanoid Eurytomora. All of the data wrangling, analysis, and data visualization are included as .Rmd files in th
Widespread Sampling Biases in Herbaria Revealed from Large-Scale Digitization 1656-2016
Non-random collecting practices may bias conclusions drawn from analyses of herbarium records. Recent efforts to fully digitize and mobilize regional floras offer a timely opportunity to assess commonalities and differences in herbarium sampling biases. We determined spatial, temporal, trait, phylogenetic, and collector biases in ~5 million herbarium records, representing three of the most complete digitized floras of the world: Australia (AU), South Africa (SA), and New England, USA (NE) We identified numerous shared and unique biases among these regions. Shared biases included specimens i) collected close to roads and herbaria; ii) collected more frequently during spring; iii) of threatened species collected less frequently; and iv) of close relatives collected in similar numbers. Regional differences included i) over-representation of graminoids in SA and AU and of annuals in AU; and ii) peak collection during the 1910s in NE, 1980s in SA, and 1990s in AU. Finally, in all regions, a disproportionately large percentage of specimens were collected by a few individuals. These mega-collectors, and their associated preferences and idiosyncrasies, may have shaped patterns of collection bias via ‘founder effects’. Studies using herbarium collections should account for sampling biases and future collecting efforts should avoid compounding these biases.
Historical Plat Maps of Dane County Digitized and Converted to GIS (1962-2005)
We constructed a time-series spatial dataset of parcel boundaries for the period 1962-2005, in roughly 4-year intervals, by digitizing historical plat maps for Dane County and combining them with the 2005 GIS digital parcel dataset. The resulting datasets enable the consistent tracking of subdivision and development for all parcels over a given time frame. The process involved 1) dissolving and merging the 2005 digital Dane County parcel dataset based on contiguity and name, 2) further merging 2005 parcels based on the hard copy 2005 Plat book, and then 3) the reverse chronological merging of parcels to reconstruct previous years, at 4-year intervals, based on historical plat books. Additional land use information such as 1) whether a structure was actually constructed (using the companion digitized aerial photo dataset), 2) cover crop, and 3) permeable surface area, can be added to these datasets at a later date.
Continental Europe Digital Terrain Model geomorphometry derivatives at 30 m, 100 m and 250 m
<p>Digital Terrain Model geomorphometry derivatives based on the DTM for Continental Europe using the <a href="https://epsg.io/3035">EPSG:3035</a> projection system. Processed using <a href="http://www.saga-gis.org/">SAGA GIS</a>, <a href="https://grass.osgeo.org/grass78/">GRASS 7 GIS</a> and <a href="https://gdal.org/programs/gdaldem.html">GDAL</a> at 3 standard spatial resolutions: 30-m, 100-m and 250-m. Derivatives include:</p> <ul> <li>devmean = deviation from mean value derived using <a href="http://www.saga-gis.org/saga_tool_doc/7.4.0/statistics_grid_1.html">SAGA GIS</a>,</li> <li>downlocal / down = downslope local and general curvature derived using <a href="http://www.saga-gis.org/saga_tool_doc/7.1.1/ta_morphometry_26.html">SAGA GIS</a>,</li> <li>hillshade = hillshading derived using using GDAL <a href="https://gdal.org/programs/gdaldem.html">gdaldem</a> functions,</li> <li>mnr = Module Melton Ruggedness Number derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.4/ta_hydrology_23.html">SAGA GIS</a>,</li> <li>northerness/easterness = derived using <a href="https://grass.osgeo.org/grass78/manuals/addons/r.northerness.easterness.html">GRASS 7 GIS</a>,</li> <li>openp / openn = openness positive negative derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.5/ta_lighting_5.html">SAGA GIS</a>,</li> <li>slope = slope in percent derived using GDAL <a href="https://gdal.org/programs/gdaldem.html">gdaldem</a> functions,</li> <li>topidx = a topographic index (wetness index) derived using <a href="https://grass.osgeo.org/grass76/manuals/r.topidx.html">GRASS 7 GIS</a>,</li> <li>tpi = Topographic Wetness Index derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.1.3/ta_hydrology_20.html">SAGA GIS</a>,</li> <li>vbf = Multiresolution Index of Valley Bottom Flatness derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.6/ta_morphometry_8.html">SAGA GIS</a>,</li> </ul> <p>Detailed processing steps can be found <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers"><strong>here</strong></a>. Read more about the processing steps <a href="https://opendatascience.eu/building-continental-europe-digital-terrain-model-30-m-resolution-using-machine-learning"><strong>here</strong></a>.</p> <p>Derivatives were chosen aiming to support soil and vegetation mapping projects. The slope.percent map at 30-m has been converted from 0-100% scale to 0-200% (Byte format) to help decrease the file size.</p>
UAV-based colour-infrared orthomosaics and digital elevation models of basalts and rock glaciers on Disko Island, West Greenland
<p><span>This data set contains multispectral surveys conducted with an unoccupied aerial vehicle over rock glaciers and steep mafic outcrops (intrusive and flood volcanics) near the coastline of Disko Island.</span></p> <ul> <li><span>Acquisition date: 07.08.2019 – 10.08.2019</span></li> <li><span>Location: Illukunnguaq, Disko Island, Greenland</span></li> <li><span>UAV: SenseFly eBee Plus</span></li> <li><span>Flight altitude above ground level: >100m</span></li> <li><span>Image Overlap forward/side: various</span></li> <li><span>Camera: Parrot Sequoia multispectral</span></li> <li><span>EPSG: 32622</span></li> <li><span>Center coordinates: 69.885277°N, -52.577724°E</span></li> <li><span>Flight mode: automatic flight plan</span></li> </ul> <p><span>Data products: </span></p> <ul> <li><span>Orthomosaic colour-infrared, 10-16 cm pixel resolution</span></li> <li><span>Colour-infrared spectral bands: 790nm, 660nm, 550nm</span></li> <li><span>DEM, 20-30cm pixel resolution</span></li> <li><span>Data coverage: approx. 5500 x 2500 m</span></li> <li><span>Elevation profile: 20-680m </span></li> <li><span>Processing in Agisoft Metashape</span></li> </ul> <p><span>Additional data supplement for article:<br>Barnes, E. (2020). Assessment of Drone-Borne Multispectral Mapping in the Exploration of Magmatic Ni-Cu Sulphides–an Example from Disko Island, West Greenland. <br><em>URN: urn:nbn:se:uu:diva-418858</em></span></p> <p>MULSEDRO field campaign was conducted under scientific survey licence (VU-00158-2019) within mineral exploration licence MEL 2018-16 by Blue Jay Mining PLC. This research has been supported by the project MULSEDRO, funded by HZDR-HIF & EITRawMaterials (project ID 16193) and the European Union.</p>
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>
13/1 Sferamundi di Grecia. Prima parte - Progetto Mambrino Digital Library
<p>Dataset of the digital scholarly edition of the Italian book of chivalry <em>13/1 Sferamundi di Grecia. Prima parte</em>.</p> <p>It contains:</p> <ul> <li>transcription and commentary XML-TEI files (source.xml and commentary.xml)</li> <li>the eBook (in multiple formats)</li> <li>plain text file for computational anaysis</li> </ul> <p>The edition is part of the Progetto Mambrino Digital Library and has been developed within the PRIN 2017 Mapping Chivalry (Prot. 2017JA5XAR), in the context of the Project of Excellence "Inclusive Humanities" (2023-2027) of the Department of Foreign Languages and Literatures of the University of Verona.</p>
DESIRA - inventory of digital tools for agriculture, forestry, and rural areas
<p>Inventory of digital tools for agriculture, forestry, and rural areas collected by the DESIRA consortium.</p>
Digital image correlation measurement of linear elastic steel specimen
<p>The dataset comprises the axial and lateral displacements on the surface of a plate with a hole subjected to tensile load. The displacement data are measured by digital image correlation and the material is assumed to behave linear elastic. The material under investigation is a common low-carbon steel alloy of type S235. The displacement data are used for calibration of a linear elastic constitutive model using parametric physics-informed neural networks and finite elements. For that purpose, the dataset comprises both the raw experimental displacement data and displacement data interpolated onto a regular grid using linear interpolation, where the interpolation routine is provided as well.</p>
13/2 Sferamundi di Grecia. Seconda parte - Progetto Mambrino Digital Library
<p>Dataset of the digital scholarly edition of the Italian book of chivalry <em>13/2 Sferamundi di Grecia. Seconda parte</em>.</p> <p>It contains:</p> <ul> <li>transcription and commentary XML-TEI files (source.xml and commentary.xml)</li> <li>the eBook (in multiple formats)</li> <li>plain text file for computational anaysis</li> </ul> <p>The edition is part of the Progetto Mambrino Digital Library and has been developed within the PRIN 2017 Mapping Chivalry (Prot. 2017JA5XAR), in the context of the Project of Excellence "Inclusive Humanities" (2023-2027) of the Department of Foreign Languages and Literatures of the University of Verona.</p>
13/6 Sferamundi di Grecia. Sesta parte - Progetto Mambrino Digital Library
<p>Dataset of the digital scholarly edition of the Italian book of chivalry <em>13/6 Sferamundi di Grecia. Sesta parte</em>.</p> <p>It contains:</p> <ul> <li>transcription and commentary XML-TEI files (source.xml and commentary.xml)</li> <li>the eBook (in multiple formats)</li> <li>plain text file for computational anaysis</li> </ul> <p>The edition is part of the Progetto Mambrino Digital Library and has been developed within the PRIN 2017 Mapping Chivalry (Prot. 2017JA5XAR), in the context of the Project of Excellence "Inclusive Humanities" (2023-2027) of the Department of Foreign Languages and Literatures of the University of Verona.</p>
13/5 Sferamundi di Grecia. Quinta parte - Progetto Mambrino Digital Library
<p>Dataset of the digital scholarly edition of the Italian book of chivalry <em>13/5 Sferamundi di Grecia. Quinta parte</em>.</p> <p>It contains:</p> <ul> <li>transcription and commentary XML-TEI files (source.xml and commentary.xml)</li> <li>the eBook (in multiple formats)</li> <li>plain text file for computational anaysis</li> </ul> <p>The edition is part of the Progetto Mambrino Digital Library and has been developed within the PRIN 2017 Mapping Chivalry (Prot. 2017JA5XAR), in the context of the Project of Excellence "Inclusive Humanities" (2023-2027) of the Department of Foreign Languages and Literatures of the University of Verona.</p>
13/4 Sferamundi di Grecia. Quarta parte - Progetto Mambrino Digital Library
<p>Dataset of the digital scholarly edition of the Italian book of chivalry <em>13/4 Sferamundi di Grecia. Quarta parte</em>.</p> <p>It contains:</p> <ul> <li>transcription and commentary XML-TEI files (source.xml and commentary.xml)</li> <li>the eBook (in multiple formats)</li> <li>plain text file for computational anaysis</li> </ul> <p>The edition is part of the Progetto Mambrino Digital Library and has been developed within the PRIN 2017 Mapping Chivalry (Prot. 2017JA5XAR), in the context of the Project of Excellence "Inclusive Humanities" (2023-2027) of the Department of Foreign Languages and Literatures of the University of Verona.</p>
13/3 Sferamundi di Grecia. Terza parte - Progetto Mambrino Digital Library
<p>Dataset of the digital scholarly edition of the Italian book of chivalry <em>13/3 Sferamundi di Grecia. Terza parte</em>.</p> <p>It contains:</p> <ul> <li>transcription and commentary XML-TEI files (source.xml and commentary.xml)</li> <li>the eBook (in multiple formats)</li> <li>plain text file for computational anaysis</li> </ul> <p>The edition is part of the Progetto Mambrino Digital Library and has been developed within the PRIN 2017 Mapping Chivalry (Prot. 2017JA5XAR), in the context of the Project of Excellence "Inclusive Humanities" (2023-2027) of the Department of Foreign Languages and Literatures of the University of Verona.</p>
Market Power / Import demand elasticity faced by an exporter at 6-digit HS level from Solleder (2020)
<p><strong>Description</strong></p> <p>This dataset contains the market power of exporters at the country level for more than 4000 6-digit HS codes (HS 1992 / H0) from Solleder (2020). Market power is proxied by the inverse of the import demand elasticity faced by the exporting country. Elasticities are estimated following the method developed by Kee et al. (2008). For more information, please refer to Solleder (2020).</p> <p>The <em>dta </em>file can be opened with STATA 14 or above. The <em>csv</em> file is a comma-separated value file. The separator is ',', and the first row is variable names. The content is the same in both files. Variables are:</p> <ul> <li><em>exporter</em>: ISO 3166 3-character country codes, string; </li> <li><em>commoditycode</em>: product 6-digit HS codes in HS revision 1992 (H0), string;</li> <li><em>epsilon</em>: import demand elasticity faced by the exporter, numeric;</li> <li><em>epsilon_se</em>: standard error of <em>epsilon</em>, numeric;</li> <li><em>marketpower</em>: market power, inverse of the absolute value of the import demand elasticity faced by the exporter, numeric.</li> </ul> <p> </p> <p><strong>Reference</strong></p> <div> <div>Kee H.L., A. Nicita, M. Olarreaga 2008 'Import demand elasticities and trade distortions' Rev. Econ. Stat., 90 (4), pp. 666-682</div> <div> </div> <div>Solleder J.M. 2020 'Market power and export taxes' European Economic Review, Volume 125, 103425, ISSN 0014-2921, <a href="https://doi.org/10.1016/j.euroecorev.2020.103425">https://doi.org/10.1016/j.euroecorev.2020.103425</a>.</div> </div> <p> </p>
Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain – Dataset
<p><strong>Dataset of <a href="https://doi.org/10.1109/jstars.2022.3188922">Hugonnet et al. (2022), Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain</a>.</strong></p> <p>The data is composed of:</p> <ul> <li><strong>For the Mont-Blanc case study: </strong>the Pléiades reference DEM, the SPOT-6 DEM, the Pléiades–SPOT-6 elevation difference, and the forest mask generated from the ESA CCI landcover (delainey polygonization);</li> <li><strong>For the Northern Patagonian Icefield case study: </strong>the ASTER reference DEM, the SPOT-5 DEM, the ASTER–SPOT-5 elevation difference, and the quality of stereo-correlation of the ASTER DEM from MicMac.</li> </ul> <p>The filenames correspond to those used in the <strong>associated GitHub repository</strong>: <a href="https://github.com/rhugonnet/dem_error_study">https://github.com/rhugonnet/dem_error_study</a>. The shapefiles used for masking glaciers are available directly from the <strong>Randolph Glacier Inventory 6.0</strong> at <a href="https://www.glims.org/RGI/">https://www.glims.org/RGI/</a>.</p> <p>The date of the DEMs is in their original format: <strong>year-month-day for all but ASTER</strong> that has the original naming of <a href="https://lpdaac.usgs.gov/products/ast_l1av003/">AST L1A products</a>. <strong>Units are meters</strong> for the DEMs and elevation differences, <strong>and percentages</strong> for the quality of stereo-correlation.</p>
Dynamic X-ray CT of Synthetic magma for Digital Volume Correlation analysis
<p>Dataset of synthetic magma subjected to compression, useful for Digital Volume Correlation analysis, ref [1,2]. The data has been acquired at the Diamond Light Source synchrotron, with a bespoke thermo-mechanical rig (“P2R”) on the I12 beamline, ref [3,4,5]. Dataset 0 has no applied compression, while dataset 1 has applied compression.</p> <p>The data was saved with numpy 1.21 with <a href="https://numpy.org/doc/1.21/reference/generated/numpy.lib.format.html#format-version-1-0">NumPy format version 1.0</a> as dataset_0.npy and dataset_1.npy, and NumPy can be used to read it back in. Both data files have a header specifying how the data is stored, and following the header comes the array data.</p> <p>In particular the header length is 128 bytes, and the data consists of a 3 dimensional matrix of size (1520, 1257, 1260) stored in unsigned integer 8 bit, Fortran order. The screenshot named import_imagej.png shows how to import the data in with <a href="https://imagej.nih.gov/ij/">ImageJ</a>.</p> <p> </p> <p>A <a href="https://github.com/Kitware/MetaIO">METAImage</a> header describing the data in text form for each dataset is also provided, i.e. dataset_0.mhd and dataset_1.mhd,</p>
Written and spoken digits database for multimodal learning
<p><strong>Database description:</strong></p> <p>The written and spoken digits database is not a new database but a constructed database from existing ones, in order to provide a ready-to-use database for multimodal fusion [1].</p> <p>The written digits database is the original MNIST handwritten digits database [2] with no additional processing. It consists of 70000 images (60000 for training and 10000 for test) of 28 x 28 = 784 dimensions.</p> <p>The spoken digits database was extracted from Google Speech Commands [3], an audio dataset of spoken words that was proposed to train and evaluate keyword spotting systems. It consists of 105829 utterances of 35 words, amongst which 38908 utterances of the ten digits (34801 for training and 4107 for test). A pre-processing was done via the extraction of the Mel Frequency Cepstral Coefficients (MFCC) with a framing window size of 50 ms and frame shift size of 25 ms. Since the speech samples are approximately 1 s long, we end up with 39 time slots. For each one, we extract 12 MFCC coefficients with an additional energy coefficient. Thus, we have a final vector of 39 x 13 = 507 dimensions. Standardization and normalization were applied on the MFCC features.</p> <p>To construct the multimodal digits dataset, we associated written and spoken digits of the same class respecting the initial partitioning in [2] and [3] for the training and test subsets. Since we have less samples for the spoken digits, we duplicated some random samples to match the number of written digits and have a multimodal digits database of 70000 samples (60000 for training and 10000 for test).</p> <p>The dataset is provided in six files as described below. Therefore, if a shuffle is performed on the training or test subsets, it must be performed in unison with the same order for the written digits, spoken digits and labels.</p> <p> </p> <p><strong>Files:</strong></p> <ul> <li>data_wr_train.npy: 60000 samples of 784-dimentional written digits for training;</li> <li>data_sp_train.npy: 60000 samples of 507-dimentional spoken digits for training;</li> <li>labels_train.npy: 60000 labels for the training subset;</li> <li>data_wr_test.npy: 10000 samples of 784-dimentional written digits for test;</li> <li>data_sp_test.npy: 10000 samples of 507-dimentional spoken digits for test;</li> <li>labels_test.npy: 10000 labels for the test subset.</li> </ul> <p> </p> <p><strong>References:</strong></p> <ol> <li>Khacef, L. et al. (2020), "Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning".</li> <li>LeCun, Y. & Cortes, C. (1998), “MNIST handwritten digit database”.</li> <li>Warden, P. (2018), “Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition”.</li> </ol>
From 'digital nomadism' to 'rooted digitalism'
<p>Codebook along DDI standard, txt file.</p><p>This article explores the interplay between digital work and mobility through a look at the career trajectories, remote work practices and im/mobilities of professionals in the information technology (IT) sector. We draw upon a qualitative study conducted</p><p>with IT professionals who work remotely for Swiss or Swiss-based international companies. IT professionals have been pioneers in practising virtual work long before the outbreak of the COVID-19 crisis and have long engaged in various forms of mobility, including tourism and labour migration. A focus on their remote work and im/mobility practices can shed light on the possibilities and challenges of the virtualization of work, especially in the context of the pandemic. We discuss how geographical immobility, combined with digital technology, becomes important in building a career and a personal life, staying 'rooted' and reconstituting the boundaries between work and non-work.</p>
Data on the Digital Economy and Society Index (DESI), the ASEAN Digital Integration Index (ADII), and the Digital Intelligence Index (DII)
<p>This dataset contains the quantitative measurement of the Digital Economy and Society Index (DESI), the ASEAN Digital Integration Index (ADII), and the Digital Intelligence Index (DII) in 2019.</p>
ScienceDex guides
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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.