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ShareScore release 0.9.0
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108 results for “Digital humanities”
Spatially resolved whole transcriptome profiling in human and mouse tissue using Digital Spatial Profiling [mouse organ array]
GEO Series GSE187013. Mus musculus. 85 samples. Type: Other.
Digital Human-Based Brief Interventions for Harmful Alcohol Use: The PAHOLA Project
ClinicalTrials.gov study NCT05906979. IPD Sharing: NO. Countries: 0. Publications: 0.
Digital RNA-Seq for transcriptome analysis in splenic human CD4-positive T cells of mock-infected humanized mice and GFP-positive and GFP-negative CD4-positive cells of HIV1-GFP-infected humanized mic
GEO Series GSE137644. Homo sapiens. 37 samples. Type: Expression profiling by high throughput sequencing.
Digital RNA Sequencing of Human Epidermal Keratinocytes carrying HPV 16E7
GEO Series GSE145224. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Digital Humanities Multi-Task Image Classification Dataset (DHMTIC)
<p>This dataset was assembled to train a neural network to undertake a multi-task, multi-class classification challenge, as well as to facilitate parameterized image searches for research queries in the humanities. This is significant for several reasons. To illustrate, large quantities of textual and visual data can be processed more efficiently through specially trained neural networks. Additionally, parameterized image searches permit a detailed examination and analysis of visual data, enabling tasks such as identifying image copies or tracking the evolution and reuse of image motifs. The images hail from diverse research fields, including art and architecture, design, and life sciences. Each image has been classified for two tasks: media type classification and content type classification. The content type, which refers to the subject depicted in the image, is categorized into 14 classes, whereas the media type, denoting whether the image is a graphic, photograph, or drawing, is divided into 3 classes. The class distribution is skewed, with a single class housing a large number of entries and the remaining classes having fewer. For example, in the content type, the "object" class contains the bulk of the data, whereas classes like "musical notation" and "map" form the tail, each with fewer than 100 examples in the training and validation subsets. Similarly, in the media type, the "graphic" subset contains the majority of samples, with the "photography" and "drawing" subsets containing considerably fewer.</p>
Digital Gene Expression data of human serum sensitive and resistant group 2 Trypanosoma brucei gambiense
GEO Series GSE23650. Trypanosoma brucei brucei; Trypanosoma brucei gambiense. 11 samples. Type: Expression profiling by high throughput sequencing.
Digital Humanities, or: The Broken Record of Everything
<p>A video essay by Tessa Gengnagel (University of Cologne), made for the Colloquium "Phenomenology of the Digital Humanities" at the FU Berlin, winter semester 2021/22, and the DH Colloquium of the University of Cologne, summer semester 2022.</p> <p>ABSTRACT:</p> <p>There are several definitions of the Digital Humanities that shift the focal point of their activities (towards humanities computing or computational humanities; public humanities; new media studies; or the digitality of the humanities in general). Most of them are premised, in one way or another, on the digitization of the ‘record’ of human history and culture. This source material which has served as the systematically indexed base of analysis and study in the humanities since their modern-day manifestation in the 19th century may be referred to as ‘artefacts’, ‘documents’, ‘monuments’ or similar and is a crucial component in research. It is not, however, and never has been, the sole subject of the humanities in and of itself.</p> <p>The video essay examines several aspects that arise in the context of digitizing said material, such as the documentary paradigm of the digital humanities, the complexity of description, the loss of information, and the challenge of virtual recreations of a time and place in the past. The essay proposes that 'the record' (or rather, the notion of 'the record') is broken because not everything was recorded, not everything that was recorded has survived, and not everything that was recorded and has survived has been digitized or may ever been digitized; to name but a few qualifications.</p> <p>Some questions for further investigation would be: How can the digital humanities enter into other areas of humanistic knowledge production? How can the digital reproduction of knowledges and extant assumptions about said material account for the silences, contradictions and factual inaccuracies in their “facsimile narratives” (Fafinski 2021)? And how will the digital humanities, as an academic discipline, navigate demands for more immersive simulative experiences which seek to extrapolate an imagined cultural memory from ‘the record’?</p>
dTwin4SkullShapes: a Digital Twin Dataset of Human Skull Shapes for Skull Missing Vs. Existing Part Learning and Prediction
<p>This dataset include the total of 757 human skulls reconstructed from computed tomography (CT) image sets. The CT image sets were constructed from the three main databases: (1) The Cancer Imaging Archive (DOI: 10.7937/TCIA.HMQ8-J677), (2) The New Mexico Decedent CT Images (DOI: 10.1055/s-0041-1730999), and (3) The MGU500+ (DOI: 10.1016/j.dib.2021.107524). The selected skull image sets have normal and full skull structures. We employed the voxelization technique to reconstruct skull meshes from CT image slices of each subject. The skull meshes were stored in *.off files. For each skull mesh, we estimated the skull shape by estimating the alpha shape of the skull meshes and stored in *.off files. The feature points on the skulls were also manually picked and stored in *.csv files with the form of 16x3 matrices, in which 16 is the number of feature points. A template skull shape was also deformed to the skull shapes of all subjects and stored in *.off files. The deformed skull shapes have unified skull feature points throughout all subjects. Moreover, we also added the source codes of the project. The details of the dataset were presented in the ReadMe.txt file. More details of how to use the source code project and the dataset were presented in the dTwin4SkullShapeTutorials.pdf file.</p>
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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.