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4,404 results for “digits”
Air Temperature for 4 LTTG sites, digitized from thermograph weather charts: Hourly Data; 1968-1989
Data is from weather stations. Pre- 5/89 data is all from thermographs of various makes (Bendix, Belfort, and Casella). Temperatures were digitized on a CalComp drawing board using software developed by Robert E. Schlentner (Forest Soils Lab, U. of Ak.). Program reads automatically at each hour's "distance" on the chart and allows for the specific curvature of the plot due to pen sweep. Post 5/89 data is from Omnidata easy logger data loggers replaced these thermographs in 1989. The data logger data is also available (see eloga.dfd, elogd.dfd and elogs.dfd).
McMurdo Dry Valleys Bathymetric Values From Contour Map Digitizing
As part of the Long Term Ecological Research in the McMurdo Dry Valleys of Antarctica, bathymetric data was collected for Lakes Hoare, Fryxell and Bonney. This table contains the values for depth, perimeter length, polygon area and total area per contour used for contour map digitizing.
Evaluation of the impact of imprinted polymer particles on morphology and motility of breast cancer cells by using digital holographic cytometry
<p>Supplemented Videos used in "Evaluation of the impact of imprinted polymer particles on morphology and motility of breast cancer cells by using digital holographic cytometry"</p>
Digitized Particulate Matter Size Distribution Profiles from Literature Sources for Improved Size Representation of PM Emissions in Atmospheric Chemical Transport Models
<p>Processing particulate matter (PM) emissions for use in a chemistry transport model (CTM) such as GEM-MACH (Global Environmental Multiscale Modelling Air-Quality and Chemistry) requires detailed information about particle size distribution and chemical speciation for different PM emissions source types. The current PM size distribution and speciation profile library used at Environment and Climate Change Canada (ECCC) for preparing model-ready emission files for GEM-MACH contains very detailed chemical speciation profiles for PM emissions from 91 source types but only has three generic PM size disaggregation profiles, one each for mobile, point, and area sources. These generic profiles are used to disaggregate bulk PM emissions to a 12-bin sectional size representation, where PM<sub>2.5</sub> emissions are split into size bins 1-8 and PM<sub>10‑2.5</sub> emissions are split into bins 9 and 10. Since there is wide variability in the particle size distribution depending on the source type, the inclusion of source-type-specific PM size disaggregation profiles should lead to better representation of PM particle size for emissions from different source types in the model.</p> <p>A presentation entitled “Expansion of a Size Distribution Profile Library for Particulate Matter (PM) Emissions Processing from Three to 32 Source Categories” was given recently at the Community Modeling and Analysis System (CMAS) conference in Chapel Hill, North Carolina in October 2019 (<a href="https://www.cmascenter.org/conference/2019/slides/1300_zhang_expansion_size_2019.pptx">https://www.cmascenter.org/conference//2019/slides/1300_zhang_expansion_size_2019.pptx</a>) . This presentation described work carried out at ECCC to improve the PM size disaggregation profile library used to generate model-ready emissions. In particular, the number of PM size disaggregation profiles in the library was increased from three generic profiles to 32 source-type-specific profiles. After the conference, four more profiles were added to the library for a total of 36 PM size disaggregation profiles. In order to carry out this study, over 100 PM size distribution profiles from various PM emissions sources were gathered from literature publications, analyzed, and transformed into size disaggregation profiles that correspond to the GEM-MACH 12-bin sectional configuration. The 36 PM size disaggregation profiles that were obtained were then combined with detailed PM chemical speciation data to compile a new PM size disaggregation and chemical speciation library for emissions processing using the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system.</p> <p>This Excel workbook provides the digitized particle size distribution data for PM emissions from 36 different source types that were used as input to calculate the PM size disaggregation profiles for the GEM-MACH 12-bin sectional configuration. The digitized particle size distribution profiles were obtained by digitizing images of size distribution plots obtained from the literature publications using graph digitizing software such as Engauge Digitizer (<a href="http://markummitchell.github.io/engauge-digitizer/">http://markummitchell.github.io/engauge-digitizer/</a>) and WebPlot Digitizer (<a href="https://directory.fsf.org/wiki/WebPlotDigitizer">https://directory.fsf.org/wiki/WebPlotDigitizer</a>). By manually defining the axes and selecting points along the curve by computer mouse, a comma-separated-values file was generated for each size distribution profile image. From there, a series of transformations were carried out as required, including particle diameter conversions from aerodynamic diameter to Stokes diameter, and conversion of number-weighted size distributions to volume-weighted size distributions, in order to obtain a harmonized set of profiles. This Excel workbook contains the raw digitized data for all literature size distributions included in the compilation of the new library, as well as the diameter and size distribution weighting conversions. There are 39 worksheets: the first is an introductory worksheet entitled “Spreadsheet_Info” while the next 36 worksheets are ordered alphabetically and correspond to each of the 36 emissions source types for which a PM size disaggregation profile was generated. The final two worksheets contain digitized particle penetration data for common PM control devices.</p> <p>These digitized profiles may be used and adapted for use with other emissions processing systems and other CTMs with a size-resolved representation for PM. More details are provided in the following publication:</p> <p>Elisa I. Boutzis, Junhua Zhang & Michael D. Moran (2020) Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles, <em>Journal of the Air & Waste Management Association</em>, 70:11, 1067-1100, DOI: <a href="https://doi.org/10.1080/10962247.2020.1743794">10.1080/10962247.2020.1743794</a></p>
Figures of the paper"A Conceptual Model of Chinese Oral Memory Based on Digital Humanities"
<p>Those are the figures used for the paper "A Conceptual Model of Chinese Oral Memory Based on Digital Humanities".</p>
soilmap_simple: a simplified and standardized derivative of the digital soil map of the Flemish Region
<p>The data source <code>soilmap_simple</code> is a simplified and standardized derived form of the '<a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a>' (the shapefile of which we named <code>soilmap</code>, for analytical workflows in R) published by 'Databank Ondergrond Vlaanderen’ (DOV). It is a GeoPackage that contains a <strong>spatial polygon layer</strong> ‘<code>soilmap_simple</code>’ in the Belgian Lambert 72 coordinate reference system (EPSG-code <a href="https://epsg.io/31370">31370</a>), plus a <strong>non-spatial table</strong> ‘<code>explanations</code>’ with the meaning of category codes that occur in the spatial layer. Further documentation about the digital soil map of the Flemish Region is available in Van Ranst & Sys (2000) and Dudal et al. (2005).</p> <p>This version of <code>soilmap_simple</code> was derived from version '<code>soilmap_2017-06-20</code>' (<a href="https://doi.org/10.5281/zenodo.3387008">Zenodo DOI</a>) as follows:</p> <ul> <li>all attribute variables received English names (purpose of standardization), starting with prefix <code>bsm_</code> (referring to the 'Belgian soil map');</li> <li>attribute variables were reordered;</li> <li>the values of the morphogenetic substrate, texture and drainage variables (<code>bsm_mo_substr</code>, <code>bsm_mo_tex</code> and <code>bsm_mo_drain</code> + their <code>_explan</code> counterparts) were filled for most features in the 'coastal plain' area. <ul> <li>To derive morphogenetic texture and drainage levels from the geomorphological soil types, a conversion table by Bruno De Vos & Carole Ampe was applied (for earlier work on this, see Ampe 2013).</li> <li>Substrate classes were copied over from <code>bsm_ge_substr</code> into <code>bsm_mo_substr</code> (<code>bsm_ge_substr</code> already followed the categories of <code>bsm_mo_substr</code>).</li> </ul> These steps coincide with the approach that had been taken to construct the <code>Unitype</code> variable in the <code>soilmap</code> data source;</li> <li>only a minimal number of variables were selected: those that are most useful for analytical work.</li> </ul> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/b3c6696/src/generate_soilmap_simple">'n2khab-preprocessing' at commit b3c6696</a> for the creation from the <code>soilmap</code> data source.</p> <p>A reading function to return <code>soilmap_simple</code> (this data source) or <code>soilmap</code> in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p><strong>The attributes</strong> of the spatial polygon layer <code>soilmap_simple</code> can have <code>mo_</code> in their name to refer to the <em>Belgian Morphogenetic System</em>:</p> <ul> <li><code>bsm_poly_id</code>: unique polygon ID (numeric)</li> <li><code>bsm_region</code>: name of the region</li> <li><code>bsm_converted</code>: boolean. Were morphogenetic texture and drainage variables (<code>bsm_mo_tex</code> and <code>bsm_mo_drain</code>) derived from a conversion table (see above)? Value <code>TRUE</code> is largely confined to the 'coastal plain' areas.</li> <li><code>bsm_mo_soilunitype</code>: code of the soil type (applying morphogenetic codes within the coastal plain areas when possible, just as for the following three variables)</li> <li><code>bsm_mo_substr</code>: code of the soil substrate</li> <li><code>bsm_mo_tex</code>: code of the soil texture category</li> <li><code>bsm_mo_drain</code>: code of the soil drainage category</li> <li><code>bsm_mo_prof</code>: code of the soil profile category</li> <li><code>bsm_mo_parentmat</code>: code of a variant regarding the parent material</li> <li><code>bsm_mo_profvar</code>: code of a variant regarding the soil profile</li> </ul> <p>The <strong>non-spatial table</strong> <code>explanations</code> has following variables:</p> <ul> <li><code>subject</code>: attribute name of the spatial layer: either <code>bsm_mo_substr</code>, <code>bsm_mo_tex</code>, <code>bsm_mo_drain</code>, <code>bsm_mo_prof</code>, <code>bsm_mo_parentmat</code> or <code>bsm_mo_profvar</code></li> <li><code>code</code>: category code that occurs as value for the corresponding attribute in the spatial layer</li> <li><code>name</code>: explanation of the value of <code>code</code></li> </ul>
Data for: Mechanisms of root-reinforcement in soils: an experimental methodology using four-dimensional X-ray computed tomography and digital volume correlation
<p>Collection of data files used in the paper titled: Mechanisms of root-reinforcement in soils: an experimental methodology using four-dimensional X-ray computed tomography and digital volume correlation.</p> <p>Additional dataset which covers the noise study CT scans and digital volume correlation noise studies can be found in DOI: <a href="http://www.doi.org/10.5281/zenodo.3352268">10.5281/zenodo.3361832</a></p> <p><strong>Data contains the following:</strong></p> <ul> <li>X-ray CT data for the interrupted direct shear tests of a soil sample containing a Willow plant. The first file is the specimen scanned unloaded, followed by seven incremental shear load steps to 20 mm shear displacement. Files are 8-bit unsigned and 1800x1800x1600px. Voxel resolution is 0.04642 mm. <ol> <li><strong>20180430_HUTCH_1839_DJB_willow_C_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_A_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_B_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_C_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_D_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_E_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_F_8-bit_1800x1800x1600.raw</strong></li> <li><strong>20180430_HUTCH_1839_DJB_willow_C_Load_G_8-bit_1800x1800x1600.raw</strong></li> </ol> </li> <li>Direct shear vs. displacement data is presented in an Excel spreadsheet: <ul> <li><strong>All_load_data_with_reducing_area.xlsx</strong></li> </ul> </li> <li>Normal and shear strain DVC data which has been averaged and plotted against specimen depth is presented in an Excel spreadsheet: <ul> <li><strong>Average_slice_vs_depth_data_all_samples_all_loads3.xlsx</strong></li> </ul> </li> <li>CT scan metadata giving information to the scan settings, voxel resolution, etc. is contained in a .zip file which consists of .xtekct and .XML files generated from the Nikon CT scanner. <ul> <li><strong>CT_Scan_Metadata.zip</strong></li> </ul> </li> <li>Drawings and Solidworks CAD files of the direct shear test rig are contained within the .zip file. There are a number of parts to the assembly. The file SSSB1003-5.SLDASM contains the complete assembly of the direct shear rig and will help identify the part names. The folder CAD_Drawings contains pdf documents of the part drawings. <ul> <li><strong>Direct_Shear_Drawings_Solidworks_Files.zip</strong></li> </ul> </li> <li>Tabulated DVC data taken at 32x32x32px subset size is contained inside a .zip file. These consist of tab separated .dat files from unloaded (data_1.dat) through incremental load steps Load A, Load B... Load G, (data_2.dat, data_3.dat... data_8.dat). The structure of the .dat file contains columns of data with each column number corresponding to the following: (1) x, (2) y (3) z, (4) vx, (5) vy, (6) vz, (7) exx, (8) eyy, (9) ezz, (10) exy, (11) exz, (12) eyz, (13) volumetric strain, (14) is valid. Columns 1-3 are subset positions in mm, 4-6 are displacements in mm, 7-9 are normal strains, 10-12 are shear strains, 13 is volumetric strain and 14 is a binary value indicating if the subset is valid. <ul> <li><strong>DVC_Load_Data_Willow_C.zip</strong></li> </ul> </li> <li>Tabulated DVC data applied to the load step CT data at 32, 48, 64, 96, 128 pixels is presented in the .zip files containing .dat tab separated files. The structure of the .dat files is described in the previous bullet point. These files were used to construct the noise study applied to incrementally loaded CT data by sampling regions away from the shear zone where the strain signals are close to zero. <ul> <li><strong>DVC_Noise_Study_Data_Load_Steps_Willow_C.zip</strong></li> </ul> </li> <li>Processed noise study data which compares the effects of DVC subset size is presented in the .xlsx file. This file contains both the controlled noise experiment data (stationary, magnification and rigid body motion) and noise study data applied to incrementally loaded data using sampled regions away from the shear zone where the strain signals are close to zero. <ul> <li><strong>Willow_C_Processed_Noise_study2.xlsx</strong></li> </ul> </li> <li>Tabulated data which compares the local x displacement vs depth profile from DVC and direct measurement of root position is contained in the following Excel spreadsheet file: <ul> <li><strong>X_Displacement_Root_and_DVC_Comparison_Willow C.xlsx</strong></li> </ul> </li> </ul>
Towards Green Cartography & Visualization: An automated, semantically-enriched method of generating energy-aware color schemes for digital maps and visualizations
<p>Towards Green Cartography & Visualization: An automated, semantically-enriched method of generating energy-aware color schemes for digital maps and visualizations</p>
SPECTRAL PROPERTIES OF QUASARS FROM SLOAN DIGITAL SKY SURVEY DATA RELEASE 14: THE CATALOG
<p>We present measurements of the spectral properties for a total of 526,265 quasars, out of which 63% have continuum S/N> 3 pixel<sup>−1</sup>, selected from the fourteenth data release of the Sloan Digital Sky Survey (SDSS-DR14) quasar catalog. We performed a careful and homogeneous analysis of the SDSS spectra of these sources, to estimate the continuum and line properties of several emission lines such as Hα, Hβ, Hγ, Mg ii, C iii], C iv and Lyα. From the derived emission line parameters, we estimated single-epoch virial black hole masses (M<sub>BH</sub>) for the sample using Hβ, Mg ii, and C iv emission lines. The sample covers a wide range in bolometric luminosity (log L<sub>bol;</sub> erg s<sup>−1</sup> ) between 44.4 and 47.3 and log M<sub>BH</sub> between 7.1 and 9.9 M. Using the ratio of L<sub>bol</sub> to the Eddington luminosity as a measure of the accretion rate, the logarithm of the accretion rate is found to be in the range between −2.06 and 0.43. We performed several correlation analyses between different emission line parameters and found them to match with that known earlier using smaller samples. We noticed that strong Fe ii sources with large Balmer line width, and highly accreting sources with large M<sub>BH</sub> are rare in our sample. We make available an extended and complete catalog that contains various spectral properties of 526,265 quasars derived in this work along with other properties culled from the SDSS-DR14 quasar catalog.</p> <p> </p> <p><strong>Here, you will find the entire spectral catalog in the FITS forma</strong>t.</p> <ul> <li>The main catalog (“ <strong>dr14q_spec_prop.fits</strong>”) is based on the spectral information from this study consisting of 274 columns, which are described in Table B4.</li> <li>An extended catalog (“ <strong>dr14q_spec_prop_ext.fits</strong>”) where all the columns of DR14Q (Paris et al. 2018) are appended after the main catalog (ie, after column # 274). The extended catalog has a total of 380 columns.</li> </ul> <p><strong>We also make available all the model components (in FITS format) for each quasar and the spectral decomposition plots (PDF format):</strong> <strong><a href="https://seafile.utu.fi/d/9866433c200743119e92/">HERE</a></strong></p> <ul> <li><em>Each model FITS file has the following 8 columns:</em></li> </ul> <blockquote> <ol> <li>wave : rest-frame wavelength</li> <li>flux : rest-frame spectral flux</li> <li>err : rest-frame spectral flux uncertainty</li> <li>model_host : decomposed host-Galaxy flux</li> <li>model_pl_bc : a sum of the model power law and Balmer continuum</li> <li>model_feii : a sum of the Fe II UV and Fe II optical</li> <li>model_line_bc : total broad line model</li> <li>model_line_nc : total narrow line model</li> </ol> </blockquote> <ul> <li><strong>A dedicated website has been created for Supplementary material:</strong> <strong><a href="http://www.utu.fi/sdssdr14">WEBSITE</a></strong></li> </ul>
La Digital Libray open data della Biblioteca del Comune di Mottola - Progetto "Terra delle Gravine"
<p><em>Terra delle Gravine tra sharing economy e turismo esperienziale</em><a href="#_ftn1">[1]</a> è un progetto finanziato dalla Regione Puglia a un consorzio di dodici comuni della provincia di Taranto, finalizzato alla formazione di studenti di scuola secondaria superiore nella descrizione di risorse digitali relative al patrimonio culturale in formato open data e alla creazione di una piattaforma, sempre in formato Open Data, di contenuti relativi ai beni culturali dei comuni coinvolti.</p> <p>Le attività sono state progettate e realizzate tra il 2017 e il 2018 dal Dipartimento di Studi Umanistici – DISUM dell’Università degli Studi di Bari e dalla spin off DABIMUS S.r.l. in collaborazione con l’Open Data Manager Pierfrancesco Paolicelli.</p> <p>I percorsi formativi e i tracciati in Open Data da utilizzare per indicizzare i beni considerati sono stati progettati in relazione alle diverse classi di beni culturali oggetto di interesse, che hanno incluso beni monumentali, beni storico artistici e demo-etnoantropologici e beni librari.</p> <p>Il DISUM, nella persona del prof. Nicola Barbuti, ha curato sia la progettazione e realizzazione del tracciato in open data utilizzato per indicizzare e pubblicare on line una selezione di volumi a stampa delle biblioteche comunali di Massafra, Mottola e Grottaglie, sia la formazione degli studenti delle scuole secondarie superiori dei tre comuni alla descrizione catalografica di risorse digitali librarie.</p> <p>I dataset sono stati strutturati conformemente alle regole definite dalla Regione Puglia nel suo catalogo <a href="http://www.dataset.puglia.it/">http://www.dataset.puglia.it/</a>, conformi all’ontologia dei luoghi della cultura e degli eventi culturali <em>Cultural ON</em> realizzata dal MiBACT nell’ambito del progetto nazionale <a href="http://dati.beniculturali.it">http://dati.beniculturali.it</a>, la piattaforma in cui il Ministero si propone di pubblicare il patrimonio culturale informativo secondo la logica dei Linked Open Data.</p> <p>Gli studenti degli istituti scolastici superiori dei tre comuni, impegnati in percorsi di Alternanza Scuola Lavoro, hanno prima seguito la formazione, quindi hanno indicizzato una selezione di volumi delle biblioteche comunali di riferimento utilizzando il tracciato in formato open data realizzato per il progetto<a href="#_ftn2">[2]</a>.</p> <p>Per facilitare la descrizione collaborativa e per la condivisione dei file con i tracciati è stata utilizzata la suite di Google Drive, di semplice utilizzo.</p> <p>Il monitoraggio degli stati di avanzamento di progetto e l’interazione con i team di studenti è stato realizzato attivando anche specifici gruppi tramite l’app Telegram, veloce, sicura, semplice e open.</p> <p>Le attività si sono svolte secondo le seguenti fasi:</p> <ul> <li>affiancamento degli studenti e formazione sui processi di indicizzazione secondo il formato di dati open CKAN;</li> <li>formazione sugli standard di descrizione digitale e indicizzazione di risorse librarie e sulla co-creazione assistita di dataset in open data;</li> <li>insegnamento sul riuso dei dati aperti per creare mappe, percorsi, infografiche, statistiche.</li> </ul> <p>Le tre digital library sono pubblicate sul sito dell’AgID <a href="http://dati.gov.it">http://dati.gov.it</a><a href="#_ftn3">[3]</a></p> <p> </p> <p><a href="#_ftnref1">[1]</a> https://terradellegravineprogettazioneperlacultura.wordpress.com/il-progetto/</p> <p><a href="#_ftnref2">[2]</a> http://www.terradellegravine.eu:3389/dataset</p> <p><a href="#_ftnref3">[3]</a> Massafra: <a href="https://dati.gov.it/dataset?id=69a7c931-b910-4acd-9f8f-47d0bd4f9dfb">https://dati.gov.it/dataset?id=69a7c931-b910-4acd-9f8f-47d0bd4f9dfb</a></p> <p>Grottaglie: <a href="https://dati.gov.it/dataset?id=644e8203-18f0-40a5-a46a-bd78cb2bd999">https://dati.gov.it/dataset?id=644e8203-18f0-40a5-a46a-bd78cb2bd999</a></p> <p>Mottola: <a href="https://dati.gov.it/dataset?id=5a99673e-2123-462e-a868-85b03c46c7fd">https://dati.gov.it/dataset?id=5a99673e-2123-462e-a868-85b03c46c7fd</a></p>
Image Dataset for 'Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning'
<p>Dataset used in the manuscript 'Digitally Deconstructing Leaves in 3D Using X-ray microcomputed Tomography and Machine Learning'. Please cite the paper presenting this dataset:</p> <p><strong>Citation:</strong> Théroux-Rancourt, G., M. R. Jenkins, C. R. Brodersen, A. McElrone, E. J. Forrestel, and J. M. Earles. 2020. Digitally deconstructing leaves in 3D using X-ray microcomputed tomography<strong> </strong>and machine learning. <em>Applications in Plant Sciences</em> 8(7): .</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>A 'Cabernet Sauvignon' grapevine (<em>Vitis vinifera</em> L.) leaf from a plant of the BOKU experimental vineyard in Tulln, Austria, was scanned using microCT at the Swiss Light Source. The original reconstructions of the scans are using the gridrec (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Gridrec_reconstruction_downsized.zip?versionId=28d98982-f69d-4eac-9dfa-efcc89c6823c">Gridrec_reconstruction_downsized.zip</a>) and the paganin, or phase-contrast, algortithm (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Phase_contrast_reconstruction_downsized.zip?versionId=bef3260d-2865-4c9b-b5e1-e692edefb691">Phase_contrast_reconstruction_downsized.zip</a>). To facilitate automated segmentation, the size of the image in the <em>x </em>and <em>y</em> dimensions have been halved, so that the size of the pixels is 0.325 µm in those dimensions, but 0.1625 µm in the <em>z</em> (slices) dimension.</p> <p>A binary image segmenting the leaf cells and the airspace for each gridrec and phase-contrast stacks are created, and both are combined together (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Binary_stack_for_local_thickness.zip?versionId=165e3938-b490-4e56-9c8e-a2084cb39d49">Binary_stack_for_local_thickness.zip</a>), a map of the local thickness is created (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Local_thickness_map.zip?versionId=ce0a7dc7-5e3f-44a4-8881-cf84b6efd87c">Local_thickness_map.zip</a>). This map gives information on the largest diameter of the pixels labeled as cells in the binary stack.</p> <p>Hand-labeled slices or ground truths were drawn on the following slices: 80, 140, 200, 260, 340, 400, 440, 540, 620, 740, 800, 860, 940, 1060, 1140, 1240, 1300, 1400, 1480, 1540, 1600, 1690, 1740, 1840 (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Hand_labelled_slices.tif?versionId=a21a13ac-fa47-4ef8-a903-ecc433787184">Hand_labelled_slices.tif</a>).</p> <p>Using the hand-labeled slices and the different images, a random-forest model was trained, which allowed to automatically segment the remaining slices of the stack (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Fullstack_Prediction_Example-6_training_slices-6_testing_slices.zip?versionId=02b69e65-da85-492e-9b72-9b2b3ccd085f">Fullstack_Prediction_Example-6_training_slices-6...</a>).</p> <p>The source code for the segmentation program is available <a href="https://github.com/plant-microct-tools/leaf-traits-microct/tree/master">here</a>, and the source code for the testing used in the paper is available <a href="https://github.com/plant-microct-tools/leaf-traits-microct/tree/nb-slices-eval">here</a>.</p>
The five digits of the giraffe metatarsal
<p>Evolution has shaped the limbs of hoofed animals in specific ways. In artiodactyls, it is the common assumption that the metatarsal is composed of the fusion of digits III and IV, while the other three digits have been lost or are highly reduced. However, evidence from the fossil record and internal morphology of the metatarsal challenges these assumptions. Further, only a few taxonomic groups have been analyzed. In giraffes, we discovered that all five digits are present in the adult metatarsal and are highly fused and modified rather than lost. We used high resolution µCT-scans of the metatarsals of two mid and late-Miocene giraffid fossils and the extant giraffe and okapi. In all the Giraffidae analyzed, we find a combination of four morphologies: (1) four articular facets, (2) four, and in most cases, five separate medullary cavities internally, (3) a clear, small digit I, and (4) in the two fossil taxa of unknown genus the presence of external elongated grooves where the fusions of II and V have taken place. <i>Giraffa</i> and <i>Okapia</i>, the extant Giraffidae, show a difference from all the extinct taxa in having more flattened digits tightly packed together, suggesting convergent highly fused digits despite divergent ecologies and locomotions. These discoveries provide evidence for new understandings as to how bones fuse and question current hypotheses of digit loss.</p>
Von den 'digital humanities' zu einer humanen Digitalisierung
<p>The video shows the opening of the DHd 2016 conference.</p> <p>Welcoming words:</p> <p>Prof. Dr. Elisabeth Burr, Institut für Romanistik</p> <p>Prof. Dr. med. Beate A. Schücking, Rektorin der Universität Leipzig</p> <p>Dr. Michael Schimansky Amt für Wirtschaftsförderung der Stadt Leipzig - vertreten durch Brigitte Brück, Abteilungsleiterin Messe / Medien / Logistik / Gesundheit</p> <p>Prof. Dr. rer. pol. habil. Hans Wismeth, Präsident der Sächsischen Akademie der Wissenschaften</p> <p>Prof. Dr. Claudine Moulin, Vorsitzende des Verbands Digital Humanities im deutschsprachigen Raum e. V.</p> <p>Music: Kathrin Engelbach and Gerhard Heyer (Violine), Jürgen Manthey (Viola), Adam Jones (Violoncello)</p> <p>After this festive opening of the conference Katharina Anna Zweig from the TU Kaiserslautern gave her opening keynote. The prezi of her keynote is available <a href="https://prezi.com/c4qiks8lt98q/digital-humanities-and-human-digitization/?utm_campaign=share&utm_medium=copy">here</a>.</p> <p>The programme of the opening of the DHd 2016 conference is available <a href="http://dhd2016.de/?q=Eroeffnung">here</a>.</p>
Title Digitized Social Interactions: Reaching Beyond the Limits of the Naked Eye
<p>The complexity and non-linear dynamics of socio-motor phenomena underlying social interactions are often missed by observation methods that attempt to capture, describe, and rate the exchange in real time. Unknowingly to the rater, socio-motor behaviors of a dyad influence each other through implicit mirroring and shared cohesiveness that escape the naked eye. Implicit in their ratings nonetheless is the assumption that the other participant of the social dyad has an identical nervous system as that of the interlocutor, and that sensory-motor information is processed similarly by both agents’ brains. What happens when this is not the case? We here use the Autism Diagnostic Observation Schedule (ADOS) to formally study social dyadic interactions, at the macro- and micro-level of behaviors, by combining observation with digital data from wearables. We find that integrating subjective and objective data reveals fundamental new ways to improve standard clinical tools more generally into outcome measures of human behaviors and treatment effectiveness.</p> <p>This Supplementary Material contains the results from analyzing the gyroscope data in parallel with the results presented in the main paper from the linear acceleration data. Further the video (animation) explains the advantages of using a hybrid model of pencil and paper with digital biomarkers to advance clinical and basic research.</p>
Data from: Born digital biodiversity data: millions and billions
Given the dramatic pace of change of our planet, we need rapid collection of environmental data to document how species are coping and to evaluate the impact of our conservation interventions. To address this need, new classes of 'born digital' biodiversity records are now being collected and curated many orders of magnitude faster than traditional data. In addition to the millions of citizen science observations of species that have been accumulating over the last decade, the last few years have seen a surge of sensor data, with eMammal's camera trap archive passing 600,000 photo-vouchered specimens and Movebank's animal tracking database recently passing one billion animal locations. Data from digital sensors have other advantages over visual citizen science observation in that the level of survey effort is intrinsically documented and they can preserve digital vouchers that can be used to verify species identity. These novel digital specimens are leading spatial ecology into the era of Big Data and will require a big tent of collaborating organizations to make these databases sustainable and durable. We urge institutions to recognize the future of born digital records and invest in proper curation and standards so we can make the most of these records to inform management, inspire conservation action, and tell natural history stories about life on the planet.
Replication as a Way of Knowing in the Digital Humanities
<p>2<sup>nd</sup> Lecture</p>
Oral Music of the Maghreb and the Mashriq and the digital encoding of scores / transcriptions
<p>2<sup>nd</sup> Lecture</p>
Going digital: Added value of electronic data collection in 2018 Afghanistan Health Survey
<p>These data were collected and analyzed to compare measures of cost efficiency, data quality and user acceptability between paper-based and digitally collected data for a nationally representative household survey in Afghanistan. </p>
Coastal Digital Elevation Models and Transects of the Reef Island Fuvahmulah, the Maldives
<p>The data contains <strong>two Digital Elevation Models</strong> (DEMs) of the coastal zone in the south-east of <strong>Fuvahmulah, the Maldives</strong> (location: latitude -0.30° and longitude 73.43°). DEM files are in the file format <em>*.tif</em> (raster data georeferenced to WGS84). The DEMs contain elevation data on the area adjacent to the seaport. One DEM was measured in 2017, the other in 2019. The 2017 DEM is based on aerial imagery, recorded with the consumer-grade unmanned aerial vehicle (UAV, or drone) DJI Phantom 4 in 2017, while the 2019 DEM was recorded with a DJI Phantom 4 Pro. The data was then processed in <em>Agisoft Photoscan</em> with a Structure-from-Motion - MultiView Stereo algorithm (SfM-MVS).<br> <br> In addition, the data set contains <strong>elevation</strong> and <strong>location data</strong> of 4 transects from sections along the east coast of Fuvahmulah. The data is stored in <em>*.csv </em>files, containing longitude, latitude, height and distance.<br> <br> Finally, there are two further data files with <strong>gridded topographic and bathymetric data</strong>, ready for use in the depth-integrated <strong>Boussinesq type model <em>BOSZ</em></strong> (see Roeber and Cheung, 2012; doi.org/10.1016/j.coastaleng.2012.06.001). Currently, the numerical wave model, incorporates lateral wave makers, so that the elevation data needs to be rotated to account for different wave directions <span class="math-tex">\(\theta\)</span>. The bathymetry was recorded with a <em>Dr. Fahrentholz LituBox 15/200</em> and truncated to 200 meters water depth. The coastal topography results from coastal DEMs, while the island's mainland is set to about 2-3 meters (no elevation information was available for the island's inland area). The data was gridded and interpolated onto the grid with <em>The Generic Mapping Tools (GMT)</em>. The grid size is 7.5 x 7.5 meter.<br> The files are in <em>MATLAB® 5.0</em> file format <em>*.mat</em>, containing the variables '<em>length</em>' and '<em>width</em>' of the domain in meters, '<em>X</em>' and '<em>Y</em>' (computation grid <span class="math-tex">\(x, y\)</span> in degrees <span class="math-tex">\(^\circ\)</span> longitude and latitude), as well as '<em>BATHY</em>', being the elevation data for the X,Y grid. In addition, friction values as used in the model is stored in the variable '<em>Friction</em>', as well as the grid increments '<em>DX</em>' and '<em>DY</em>' (<span class="math-tex">\(\Delta x\)</span> and <span class="math-tex">\(\Delta y\)</span> in degrees <span class="math-tex">\(^\circ\)</span> longitude and latitude).</p> <p>Each folder contains either a README-file or Jupyter Notebook (Python 3). The Notebooks allow the user to access and view the files (Python 3 and Jupyter Notebook installation required)</p>
German-Swiss survey on the use of (digital) language resources
<p>This survey on the use of (digital) language resources in German-speaking Switzerland complements the Europe-wide survey conducted by the European Network of e-Lexicography (ENeL) and was carried out in close cooperation with the Leibniz Institute for the German Language (IDS) in Mannheim. The aim was to obtain information on the use of language resources from the user's perspective.</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.