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36,930 results for “human”
LAGOS-US HUMAN v2: Data module of human population(1990-2020), urbanization classification, and lake access in the conterminous U.S.
The LAGOS-US HUMAN v1 data package is an extension module of the LAGOS-US research platform that includes data characterizing human population (population count, race, ethnicity, socioeconomic information), urbanization, and lake access of 479,950 lakes larger than or equal to 1 ha in the conterminous U.S. (48 states plus the District of Columbia). This data module contains four data tables linked through the unique lake identifier for the LAGOS-US research platform, lagoslakeid. Human population characteristics (race, ethnicity, and socioeconomic factors) were derived from U.S. census data for 1990, 2000, 2010, and 2020. Lakes were classified as urban or not using two different classifications: one based on the ‘Developed’ land category in the National Land Cover Dataset; and another based on the 2020 Census Urban Areas category. Metrics for lake access were developed from national datasets on public boat launches, transportation, and public lands. LAGOS-US HUMAN v1 provides a link between lake data and human contexts, facilitating interdisciplinary research in limnology, urban ecology, environmental justice, and conservation. To facilitate such studies, users are encouraged to use the other three core data modules of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds); GEO (geospatial ecological context at multiple spatial and temporal scales); and LIMNO (in situ lake physical, chemical, and biological measurements through time) that are each found in their own data packages.
Land Conservation and Human Demographics by Census Tract in New England 2014-2018
This dataset summarizes land protection, conservation prioritization layer scores, and human demographics within New England communities, defined as census tracts. This dataset was created to identify disparities in land protection according to metrics of social marginalization and assess how incorporating environmental justice criteria into land conservation prioritization systems might change conservation priorities.
Human MEG recordings during sequential conflict task
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Four-Chamber Human Heart Model for the Simulation of Cardiac Electrophysiology and Cardiac Mechanics
<p><strong>Changes in version 1.1 compared to version 1.0:</strong></p> <ul> <li>Ventricular fiber orientation changed to 66° on the endocardial and −41° on the epicardial surface</li> <li>Electrophysiology mesh was resampled</li> <li>Updated material tags in EP mesh</li> <li>Further details can be found in the <a href="https://github.com/KIT-IBT/CardioMechanics/tree/main">CardioMechanics GitHub repository</a> new reference paper:</li> </ul> <blockquote> <p>Gerach, T.; Loewe, A. Differential effects of mechano-electric feedback mechanisms on whole-heart activation, repolarization, and tension. <em>The Journal of Physiology</em> <strong>2024. </strong>https://doi.org/10.1113/JP285022 </p> </blockquote> <p>This repository contains a four-chamber model of the human heart which is ready to use for simulations of cardiac electrophysiology and cardiac mechanics problems. When using this dataset, please also cite the accompanying paper</p> <blockquote> <p>Gerach, T.; Schuler, S.; Fröhlich, J.; Lindner, L.; Kovacheva, E.; Moss, R.; Wülfers, E.M.; Seemann, G.; Wieners, C.; Loewe, A. Electro-Mechanical Whole-Heart Digital Twins: A Fully Coupled Multi-Physics Approach. <em>Mathematics</em> <strong>2021</strong>, <em>9</em>, 1247. https://doi.org/10.3390/math9111247</p> </blockquote> <p>The cardiac anatomy was manually segmented from magnetic resonance imaging (MRI) data of a 33 year old male volunteer. The volunteer provided informed consent and the study was approved by the IRB of Heidelberg University Hospital (Fritz et al., 2014).<br>The MRI data were acquired using a 1.5 T MR tomography system and consist of a static whole heart image stack taken during diastasis as well as time-resolved images in several long and short axis slices. Based on the segmentation, we first labeled the atria and the ventricles. The geometry was extended by a representation of the mitral valve, the tricuspid valve, the aortic valve and the pulmonary valve. Additionally, we closed the endo- and epicardial surfaces of the atria and added truncated pulmonary veins, vena cavae as well as the ascending aorta and pulmonary artery. Furthermore, we added a concentric layer of tissue around the entire heart which phenomenologically represents the influence of the pericardium and the surrounding tissue.</p> <p>Two tetrahedral meshes were created using Gmsh (Geuzaine et al., 2009): (1) the mechanical reference domain (<strong>M.vtu</strong>) with 128,976 elements (on average 3.17 mm edge length) and (2) the electrophysiological reference domain (<strong>EP.vtu</strong>) as a subset of M with 50,058,295 elements (on average 0.4 mm edge length).<br>We used rule-based methods to assign the myofiber orientation on EP: Wachter et al. (2015) was used for the atria and Bayer et al. (2012) for the ventricles. The fiber angle in the ventricles was chosen as +60° and -60° on the endocardial and epicardial surface, respectively. The sheet angle was set to -65° on the endocardium and 25° on the epicardium. Github repositories to these fiber generation tools are given in the sidebar. All geometry files are given in millimeter (mm).</p> <ul> <li><strong>Data:</strong><br>We provide time resolved data evaluated from cine MRI data, which can be used for model calibration. <ul> <li>Wall thickening (<strong>17AHA_WT.txt</strong>) / fractional wall thickening (<strong>17AHA_fractionalWT.txt</strong>) in the 17 AHA segments of the left ventricle</li> <li>Atrioventricular plane displacement (<strong>AVPD.txt</strong>) and velocity (<strong>AVPV.txt</strong>) as well as the displacement of all tracked points used for AVPD calculation (<strong>AVPD_trackedPoints.txt</strong>)</li> <li>Left and right ventricular volume (<strong>Volume_LV_RV.txt</strong>). RV volume is only available for end-diastole and end systole.</li> </ul> </li> <li><strong>Surfaces:</strong><br>This directory contains *.stl files with triangulated surfaces on which boundary conditions can be applied. <ul> <li><strong>cavityXX.stl</strong>: blood volume of the LV, RV, LA, RA</li> <li><strong>epicard.stl</strong>: the whole epicardium</li> <li><strong>outerPeri.stl</strong> and <strong>outerTrunks.stl</strong>: surfaces for Dirichlet boundary conditions</li> <li><strong>master.stl </strong>and <strong>slave.stl</strong>: surfaces used for the frictionless contact problem described in Fritz et al. (2014)</li> </ul> </li> <li><strong>TetGen:</strong><br>Contains the geometry <strong>M.vtu</strong> in the TetGen file format. T4 mesh with 4 node tetrahedrons and 3 node triangles. <ul> <li>.bases: fiber, sheet, and normal orientation at quadrature points of all elements</li> <li>.node: vertex coordinates</li> <li>.ele: list of tetrahedra</li> <li>.sur: list of triangles</li> </ul> </li> <li><strong>EP.vtu:</strong><br>Contains the cell arrays Fiber and Material.</li> <li><strong>M.vtu:</strong><br>Contains the cell arrays Fiber, Sheet, Sheetnormal, Material, and Label.</li> <li><strong>LabelIDs.txt:</strong><br>List of Label and Material identification numbers and corresponding anatomical structures.</li> </ul> <p> </p>
EATRIS-Plus multi-omics data of a human reference cohort
<p>In this reference study, blood samples of 127 healthy individuals were analyzed with a wide range of -omics technologies, resulting in the most comprehensive -omics <br>profiling data set that is publicly available. The molecular measurements that are available here, can be used as reference values for any future (multi-)omics studyies. Along with phenotypic information (Sex, Age, BMI etc. and measured cell types levels) on the healthy subjects, the following data types are included:</p> <ul> <li>Targeted metabolomics (acylcarnitines, amino acids and very long chain fatty acids)</li> <li>Lipidomics (negative and positive ionization modes)</li> <li>Proteomics</li> <li>mRNA-seq</li> <li>miRNA-seq</li> <li>miRNA qRT-PCR</li> <li>Enzymation Methylation sequencing</li> </ul> <p>The pre-processed mult-omics data can be accessed here in the shape of a MultiAssayExperiment object (<a href="https://doi.org/10.1158/0008-5472.can-17-0344">Ramos et al. 2017</a>). Instructions on how to read the object into R can be found here: <a href="https://github.com/EATRIS/Read_MultiAssayExperiment">Read_MultiAssayExperiment</a>.</p> <p>A similar object for Python (MuData) including the same data will be added later. </p> <p> </p> <p>DATA AVAILABILITY STATEMENT:</p> <p>Full data related to the EATRIS-Plus multiomic cohort are available in the ClinData repository (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fclindata.imtm.cz%2F&data=05%7C02%7CCasper.deVisser%40radboudumc.nl%7C347853c763954a30b82208dc3ebe2571%7Cb208fe69471e48c48d87025e9b9a157f%7C0%7C0%7C638454233596610669%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=Jp7u%2BXblry9QNg4EQkJE4CKxMZZfMK9U84Ob7E6up90%3D&reserved=0">https://clindata.imtm.cz</a>) and include full phenotypic information, physical and laboratory examinations, multiomic data from white blood cells (whole genome sequencing, enzymatic methylation DNA sequencing, mRNA sequencing, miRNA sequencing) or plasma (miRNA qPCR profiling, proteomics, targeted metabolomics, untargeted lipidomics, Raman spectroscopy profiling). However, access is restricted due to legal, ethical, scientific and/or commercial reasons. Access to the data is subject to approval and a data sharing transfer agreement. For data access please contact <a href="mailto:data.access@imtm.upol.cz">data.access@imtm.cz</a>. </p>
Forest Change and Human Populations in New England 1600-2015
As part of retrospective studies of land-use across New England, information was compiled on forest cover and human population for the New England states. The states share a common history of deforestation for agriculture followed by farm abandonment and natural reforestation with the exception of northern Maine, which was never densely settled and largely remained forested. The extent to which these secondary forests differ in structure and function from permanently wooded areas or the forests of the pre-settlement period forms a major research emphasis of Harvard Forest studies.
The physiological effects of non-invasive brain stimulation fundamentally differ across the human cortex
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A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP1
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Social Processes Initiative in Neurobiology of the Schizophrenia(s) Traveling Human Phantoms
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Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation
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3D Mapping of Neurofibrillary Tangle Burden in the Human Medial Temporal Lobe
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Transcriptomic response of human cells to SARS-CoV-2, RSV and H1N1 (STAR + StringTie)
<p>These data represent results from:</p> <ol> <li>Processing reads from 20 experiments (part of GSE147507) by following a standard approach, which includes using STAR to align the reads to GRCh38 and StringTie to calculate the (raw) counts per experiment. These results depict the transcriptomic response of human cells to SARS-CoV-2, RSV and H1N1, and enrichment analyses based on genes differentially expressed in SARS-CoV-2 but not in RSV or H1N1. (Authors: V.A.-P., M.G.F. and A.G.)</li> <li>Aligning to SARS-CoV-2 and quantifying reads by using HISAT2 and StringTie. (Author: C.R.-A.)</li> </ol> <p>Disclaimer: These results were obtained during the virtual BioHackathon 2020. As such, they are subject to ongoing research and have thus NOT yet undergone any scientific peer-review. That is, none of the contents can be considered to be free of errors and must be taken with caution!</p>
Raw data for "Development and characterization of a non-human primate model of disseminated synucleinopathy"
<p><span>In this study, the performance and biodistribution of the retrogradely-spreading AAV9-SynA53T vector was evaluated in the NHP brain. Conducted intraparenchymal deliveries of viral suspensions in the left putamen gave rise to a disseminated synucleinopathy in a circuit-specific basis.</span></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>
RoHuCAD: Robots and Humans Collaborative Anomaly Detection
<h1>RoHuCAD: Robots and Humans Collaborative Anomaly Detection</h1> <p>RoHuCAD is a dataset of human-robot collaboration in a robotic workshop (check <code>workshop_layout.png</code>). Two robots (collaborative manipulator - cobot, autonomous mobile robot - AMR) assist three human operators in assembly of electronic devices.</p> <p>There are two 8-min long recordings in the dataset. They mostly follow the same scenario, with slightly different anomalies. The data is in ROS Noetic rosbag format.</p> <h2>Included data </h2> <ul> <li>RGBD camera data (color + depth) <ul> <li>3 cameras: <a href="https://www.intelrealsense.com/depth-camera-d435i/">Intel Realsense D435i</a></li> <li>color and depth data at 6 frames per second</li> <li>Intrinsic calibration data</li> <li>Extrinsic calibration data (positions and orientations)</li> </ul> </li> <li>Information about positions of robots <ul> <li>AMR: <a href="https://www.ez-wheel.com/en/development-kit-for-agv-and-amr">Ez-Wheel SWD® Starter Kit</a></li> <li>Cobot: <a href="https://www.universal-robots.com/products/ur10-robot/">Universal Robots UR10e</a></li> </ul> </li> </ul> <h2>Annotations</h2> <p>Annotations of specific anomalies are included (CSV file with columns: event_id, tstart, tend, event_type, person_id, camera_id)</p> <ul> <li>Gestures / poses <ul> <li>BENT</li> <li>T-POSE (hands horizontally to the sides)</li> <li>L+R-UP (both hands up)</li> <li>RH-UP (right hand up)</li> <li>LH-UP (left hand up)</li> <li>SQUAT</li> <li>HI-POSE (waving)</li> </ul> </li> <li>Unsafe behaviour <ul> <li>Human in robot working area</li> <li>Standing back to (moving) robot</li> <li>Looking at phone</li> <li>Human in the way of AMR</li> </ul> </li> <li>Normal activities <ul> <li>Assembling/Working</li> <li>Loading/unloading AMR</li> </ul> </li> </ul> <h2>ROS topics</h2> <ul> <li><code>/tf </code></li> <li><code>/tf_static</code></li> <li><code>/joint_states</code></li> <li>cam_ws2_box <ul> <li><code>/cam_ws2_box/color/camera_info</code></li> <li><code>/cam_ws2_box/color/image_raw/compressed</code></li> <li><code>/cam_ws2_box/depth_registered/camera_info</code></li> <li><code>/cam_ws2_box/depth_registered/image_rect_raw</code></li> </ul> </li> <li>cam_ta2_ws2 <ul> <li><code>/cam_ta2_ws2/color/camera_info</code></li> <li><code>/cam_ta2_ws2/color/image_raw/compressed</code></li> <li><code>/cam_ta2_ws2/depth_registered/camera_info</code></li> <li><code>/cam_ta2_ws2/depth_registered/image_rect_raw</code></li> </ul> </li> <li>cam_ta1_ws2 <ul> <li><code>/cam_ta1_ws2/color/camera_info</code></li> <li><code>/cam_ta1_ws2/color/image_raw/compressed</code></li> <li><code>/cam_ta1_ws2/aligned_depth_to_color/camera_info</code></li> <li><code>/cam_ta1_ws2/aligned_depth_to_color/image_raw</code></li> </ul> </li> </ul> <h2>Acknowledgement</h2> <p>The work leading to these results has received funding from the European Union’s Horizon Europe research and innovation programme within the ULTIMATE project under the Grant Agreement no 101070162.</p>
Full-length and split homologs of human proteins in the gut microbiome
<p>These files were generated as part of the manuscript "Human xenobiotic metabolism proteins have full-length and split homologs in the gut microbiome" (submitted).</p> <p>The .tar file contains .ipc files that are tables of full-length (full_humcover3.ipc) and split homologs (part_humcover3.ipc) of human proteins in the gut microbiome, organized by alignment coverage threshold. For example, the directory `HumanUPR_0.67_src_20000_70` contains results obtained at a 67% alignment coverage threshold for the bacterial protein, and 70% for the human protein. Note that our pipeline collapses full-length alignments to the same UHGP-90 protein family into a single entry per species, with the number of genomes reported in the column nGenomes. Split homologs are not collapsed because genomic context is used to define them, and this context may differ across individual genomes.</p> <p>These files are in Arrow <a href="https://arrow.apache.org/docs/python/ipc.html#ipc">IPC</a> format, which provides compression and fast I/O for large tables. We recommend reading them using <a href="https://pola.rs/">pola.rs</a> or the <a href="https://arrow.apache.org/docs/r/">R Arrow</a> package. In particular, because the full-length homolog table is large, you may wish to work with it without loading it into memory, which can be accomplished using <a href="https://docs.pola.rs/api/python/dev/reference/api/polars.scan_ipc.html">scan_ipc</a> in pola.rs or <a href="https://arrow.apache.org/docs/r/reference/open_dataset.html">open_dataset</a> in R Arrow.</p> <p>We also provide gzipped .csv format datasets of full-length (pgkb_FH_drugs.csv.gz) and split (pgkb_SH_drugs.csv.gz) homologs, at the default 67% alignment coverage threshold for bacterial and 70% for human proteins, organized by their <a href="https://www.pharmgkb.org/">PharmGKB</a> annotations. For each drug annotated in PharmGKB as being metabolized by a human protein with full-length or split homologs, we provide the human protein(s) responsible, its xenobiotic enzyme class, the bacterial protein homolog(s), length and percent identity of the alignment, and either the specific genome (g, split homologs only) or the number of genomes (nGenomes, full homologs only). Xenobiotic enzyme classes are defined as in Figure 4 of the manuscript, with the additional classes "nucl" (nucleobase-containing metabolic proteins not annotated to any other class), "redox" (oxidoreductases not annotated to any other class), and "other" (all remaining proteins).</p>
Differential gene expression data of commercial compounds used to assess the performance of human TeraTox assay
<p>The dataset supplements the publication `Optimization of the <em>TeraTox</em> assay for preclinical teratogenicity assessment`. </p> <ul> <li>2022-02-18-TeraTox-commercial-logFC.gct: log2FC matrix of genes by compounds (in concentration ranges)</li> <li>2022-02-18-TeraTox-commercial-pScore.gct: p-scores (log 10 transformed p-values with the sign of logFC) of genes by compounds</li> <li>2022-02-18-TeraTox-commercial-featureData.txt: feature annotation in TSV format</li> <li>2022-02-18-TeraTox-commercial-phenoData.txt: sample annotation in TSV format</li> <li>2021-06-10-gcGeneFactorAnno-withPositiveCoefs.tsv: gene membership of germ-layer factors, with germ-layer annotation and average expression in copies per million (cpm).</li> </ul> <p>Citation: Jaklin, Manuela, Jitao David Zhang, Nicole Schäfer, Nicole Clemann, Paul Barrow, Erich Küng, Lisa Sach-Peltason, Claudia McGinnis, Marcel Leist, and Stefan Kustermann. “Optimization of the TeraTox Assay for Preclinical Teratogenicity Assessment.” <em>Toxicological Sciences</em> 188, no. 1 (July 1, 2022): 17–33. <a href="https://doi.org/10.1093/toxsci/kfac046">https://doi.org/10.1093/toxsci/kfac046</a>.</p>
Human Kino-Dynamic Measurements Dataset for Factory-like Activities
<p>This dataset was created as a part of the study presented in IEEE Transactions on Human-Machine Systems with the title "An Online Multi-Index Approach to Human Ergonomics Assessment in the Workplace" by Marta Lorenzini, Wansoo Kim and Arash Ajoudani. This paper introduces an online approach to monitor kinematic and dynamic quantities on the workers, providing on the spot an estimate of the physical load required in their daily jobs. A set of ergonomic indexes is defined to account for multiple potential contributors to work-related musculoskeletal disorders (WMSDs), which remain one of the major occupational safety and health problems in the European Union nowadays. Thus, the continuous tracking of workers’ exposure to the factors that may contribute to their development is paramount. To evaluate the proposed framework, a throughout experimental analysis was conducted.</p> <p>Twelve healthy adult subjects were recruited in the experimental study to perform, in the laboratory settings, occupational activities that are commonly carried out by workers in the current industrial scenario. Three tasks were selected to encompass the most significant risk factors in the workplace: mechanical overloading of the body joints, variable and high-intensity interaction forces, and repetitive and monotonous movements. Accordingly, lifting/lowering of a heavy object, drilling, and painting with a lightweight tool were considered, respectively, in this study. While the subjects were carrying out such activities, the data regarding the whole-body motion and the forces exchanged with the environment (both ground reaction force (GRF) and interaction forces at the end-effector) were collected. In addition, ten surface electromyography (sEMG) sensors were placed on the body of each subject to measure muscle activity as a reference to the effective physical effort required for the tasks.</p> <p>The whole experimental procedure was carried out in accordance with the Declaration of Helsinki and the protocol was approved by the ethics committee azienda sanitaria locale (ASL) Genovese N.3 (Protocol IIT_HRII_ERGOLEAN 156/2020).</p>
Software and suspect database for: "A large scale multi-laboratory suspect screening of pesticide metabolites in human biomonitoring: From tentative annotations to verified occurrences"
<p>This upload contains the pesticide suspect list aggregated among the laboratories of work package 16 of the HBM4EU (https://www.hbm4eu.eu) project for a large-scale pesticide suspect screening and the resolving search templates for each pesticide. Additionally, we provide the used software version of MetAlign applied in this screening.</p>
Subjective human thresholds over computer generated images
<p>Realistic image computation mimics the natural process of acquiring pictures by simulating the physical interactions of light between all the objects, lights and cameras lying within a modelled 3D scene. This process is known as global illumination and was formalised by Kajiya with the following rendering Equation:<br> <span class="math-tex">\(\begin{equation} \label{eq:rendering_equation} L_o(x, \omega_o) = {L_e(x, \omega_o)} + \int_{\Omega}^{} {L_i(x, \omega_i)} \cdot f_r(x, \omega_i \rightarrow \omega_o) \cdot \cos \theta_i d\omega_i \end{equation}\)</span></p> <p>where:</p> <ul> <li> <span class="math-tex">\(L_o(x, \omega_o)\)</span> is the luminance traveling from point <span class="math-tex">\(x\)</span> in direction <span class="math-tex">\(\omega_o\)</span>;</li> <li><span class="math-tex">\(L_e(x, \omega_o)\)</span> is point <span class="math-tex">\(x\)</span> emitted luminance (it is null if point x does not lie on a ligth source surface);</li> <li>the integral represents the set of luminances <span class="math-tex">\(L_i\)</span>incident in <span class="math-tex">\(x \)</span> from the hemisphere of the directions <span class="math-tex">\(\Omega\)</span> and reflected in the direction <span class="math-tex">\(\omega_o\)</span>. The reflected luminances are weighted by the materials reflecting properties (bidirectionnal reflectance function <span class="math-tex">\(f_r(x, \omega_i \rightarrow \omega_o)\)</span>) and the cosinus of the incident angle.</li> </ul> <p>This equation cannot be analytically solved and Monte Carlo approaches are generally used to estimate the value of the pixels of the final image.</p> <p>This proposed dataset is composed of 80 points of view of photo realistics images with different level of samples (following the Monte Carlo approach) for each. Each image is 800 x 800 pixels in size. The most noisy image is of 20 samples and the reference one (the most converged image obtained) is of 10000 samples. The <a href="https://www.pbrt.org/index.html">pbrt</a> rendering engine (version 3) was used to generate these images.</p> <p>By exploiting these levels of samples obtained and therefore of noise perceptible in the images, average subjective human thresholds were collected. For this purpose, the images were divided into 16 areas of 200 x 200 pixels in size for each point of view.</p> <p>The proposed image database is composed of the following files:</p> <ul> <li><strong>human-thresholds.csv</strong> : the set of human subjective thresholds obtained on 40 points of view. A line is composed of the name of the point of view followed by all the thresholds obtained for each of the 16 zones;</li> <li><strong>SIN3D_dataset.tar.gz</strong> : is an archive containing all the images from 20 to 10000 samples in steps of 20 samples for each point of view (i.e. 500 images per point of view). Each folder in the archive corresponds to a point of view.</li> </ul> <p><em>This image database has been exploited in order to propose an objective model for noise detection in photo-realistic computer-generated images (article referenced to this image database).</em></p> <p><strong>Note:</strong> Some of the proposed scenes come from:</p> <ul> <li><a href="https://pbrt.org/scenes-v3">https://pbrt.org/scenes-v3</a></li> <li><a href="https://benedikt-bitterli.me/resources/">https://benedikt-bitterli.me/resources/</a></li> </ul> <p><strong>Funding:</strong> This research was funded by ANR support: project ANR-17-CE38-0009.</p> <p> </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.