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4,404 results for “Digitization”
Bibliographic Data from the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review
<p>This database contains all the bibliographic information about the 8673 records found after applying the Search Strategy used for the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review. Such strategy consisted on using seven initial keywords and similar terms of interest (namely: bridge and bridges, etc.): </p> <ul> <li>Bridge.</li> <li>Digital twin.</li> <li>Bridge information modelling.</li> <li>Finite elements.</li> <li>Bridge health monitoring.</li> <li>Anomaly detection algorithm.</li> <li>Cultural heritage.</li> </ul> <p>Six initial queries were done combining the first keyword with the rest of them:</p> <ul> <li>bridge* AND "digital twin*"</li> <li>bridge* AND (BrIM OR "bridge information model*")</li> <li>bridge* AND (FEM OR FEA OR "finite element method*" OR "finite element analy*")</li> <li>bridge* AND ("bridge health monitoring" OR "structural health monitoring")</li> <li>bridge* AND (ADA OR "anomaly detection algorithm*")</li> <li>bridge* AND ("cultural heritage" OR "monument* bridge*" OR "old bridge*" OR "ancient bridge*" OR "historic* bridge*")</li> </ul> <p>As a first screening step, the combination of these 6 initial searches was done to obtain relevant works containing at least three of the main keywords of interest:</p> <ul> <li>#1 AND #2</li> <li>#1 AND #3</li> <li>#1 AND #4</li> <li>#1 AND #5</li> <li>#1 AND #6</li> <li>#2 AND #3</li> <li>#2 AND #4</li> <li>#2 AND #5</li> <li>#2 AND #6</li> <li>#3 AND #4</li> <li>#3 AND #5</li> <li>#3 AND #6</li> <li>#4 AND #5</li> <li>#4 AND #6</li> <li>#5 AND #6</li> </ul> <p>All records found in Scopus where downloaded both in .ris and .csv format and are included in this database. The search was conducted on 10/12/2022.</p> <p>Note: Searches 10, 14, 17 and 21 did not return any records.</p>
Dataset for presentation Le Digital Humanities nei corsi di studio e di dottorato: questioni formative, disciplinari, istituzionali
<p>This is the dataset upon which the slides of my talk "Le Digital Humanities nei corsi di studio e di dottorato: questioni formative, disciplinari, istituzionali" was based.</p> <ul> <li><strong><em>Singoli insegnamenti di informatica umanistica in Italia (responses).csv </em></strong>is a CSV file including the 'raw' replies of the survey mentioned in the slides. The column "User display name" represents the user filling the survey. It is always "Anonymous user", except when I replyied myself: in this case it is "ilbuonme"</li> <li><strong><em>insegnamenti.ods</em></strong> is an ODS spreadsheet including data and graphs on individual Digital Humanities classes in Italian universities. This spreadsheet derives from the CSV file, but includes additional information deriving from my personal research.</li> <li><em><strong>cds.ods</strong></em> is an ODS spreadsheet including data and graphs on BA and MA programs in the Digital Humanities in Italy. It is not related to the CSV file.</li> </ul>
Ensemble Digital Terrain Model (EDTM) of the world
<p>Layers include: Ensemble Digital Terrain Model (EDTM) in 250-m resolution. Unit is in metre(m) and precision is in decimetre (dm). Maps are downscaled from 30-m resolution to 250-m in order to fit the size limit. We provide 30-m EDTM and its standard deviation as links:</p> <ul> <li><strong>30-m EDTM</strong></li> </ul> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif">https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_p10_30m_s_2018_go_epsg4326_v20230221.tif</a></p> <ul> <li><strong>Standard deviation</strong></li> </ul> <p><a href="https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_std_30m_s_2018_go_epsg4326_v20230221.tif"><strong>https://s3.eu-central-1.wasabisys.com/openlandmap/dtm/dtm.bareearth_ensemble_std_30m_s_2018_go_epsg4326_v20230221.tif </strong></a></p> <p>Derived using <a href="https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm">ALOS AW3D</a>, <a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model">GLO-30</a>, <a href="http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/">MERITDEM</a>, and national DTMs. We derived a lower 10% quantile from all maps. In order to create bare earth data, we used <a href="https://glad.umd.edu/dataset/gedi/">canopy height</a> (canopy height > 2m) and standard deviation (sd > 6m) to mask building and forest in AW3D and GLO-30. Practical processing is written <a href="https://gitlab.opengeohub.org/yu-feng.ho/faen-artifact/-/blob/main/ensemble_dtm.ipynb">here</a> in Python.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL in Cloud Optimised GeoTiff (COG). File naming convention:</p> <ul> <li>dtm.bareearth = variable: digital terrain model (m), bare earth</li> <li>ensemble = determination method: ensemble of mutli-source dsm and dtm</li> <li>p10/std = aggregation/statistics method: 10th percentile / standard deviation</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s = vertical reference: at surface,</li> <li>go = bounding box: global land without Antarctica</li> <li>epsg.4326 = ESPG code: epsg.4326</li> <li>v20230221 = version code: creation date 20230221</li> </ul>
CHEK To-be Digital Building Permit process map
<p>To-be process map for digital building permit process as developed within the HORIZON EUROPE project 'Change toolkit for Digital Building Permit' (CHEK) https://chekdbp.eu </p> <p>It is described in the CHEK project deliverable D1.1.</p> <p>This project has received funding from the European Union’s Horizon Europe programme under Grant Agreement No.101058559</p> <p>The file is provided in<br> - SVG format, open vector editable format;<br> - Visio format, proprietary but editable format;<br> - PDF format.</p>
UKRI Digital Research Infrastructure Mapping Survey Dataset (for Net Zero Scoping Project)
<p>This dataset was generated as an output for the DRI Mapping exercise carried out during the UKRI Net Zero Digital Research Infrastructure (DRI) Scoping Project undertaken from 2021-2023. The "README.md" provides more information about the dataset and how to use it.</p> <p>The report associated with this dataset is available at:</p> <p>https://doi.org/10.5281/zenodo.7805987</p>
Example of Force Digital Calibration Certificate used in ComTraForce 18SIB08 project to demonstrate Digital Twin concept
<p>Force Digital Calibration Certificate (DCC) was developed in the frameworks of 18SIB08 ComTraForce project. It was used to demonstrate the way of data connection between the physical object (force transducer) and virtual object (Finite Element model) within the developed Digital Twin concept. The developed at PTB v3.1.2 xsd schema was used to convert analog calibration certificate to machine readable XML format. The DCC covers static and continuous calibration processes. Note that the current Force DCC is not a Good Practice example. Please follow further developments of force DCC Good Practice example at https://gitlab.com/ptb/dcc.</p>
Geotagged Digital Traces
<p>This dataset, divided into files by city, contains geotagged digital traces collected from different social media platforms, detailed below.</p> <p>• Tweets - Cheng et al. [1]</p> <p>• Gowalla [2]</p> <p>• Tweets - Lamsal [3]</p> <p>• YELP[4]</p> <p>• Tweets - Kejriwal et al. [5]</p> <p>• Geotagged Tweets [6]</p> <p>• UrbanActivity, [7]</p> <p>• Brightkite [8]</p> <p>• Weeplaces [8]</p> <p>• Flickr [9]</p> <p>• Foursquare [10]</p> <p> </p> <p>Each file is named according to the city to which the digital traces were associated and contains the columns:</p> <ul> <li>Source: contains the name of the source platform</li> <li>Event_date: contains the date associated with the digital trace</li> <li>Lat: latitude of the digital trace</li> <li>Lng: length of the digital trace</li> </ul> <p>The definition of city/town used is provided by Simplemaps [11], which considers a city/town any inhabited place as determined by U.S. government agencies. The location of cities and their respective centers were obtained from the World Cities Database provided by the same company.</p> <p>A specific group of these cities was utilized for the research presented in the article submitted to Sensors Journal:</p> <p>Muñoz-Cancino, R., Rios, S. A., & Graña, M. (2023). Clustering cities over features extracted from multiple virtual sensors measuring micro-level activity patterns allows to discriminate large-scale city characteristics. Sensors, Under Review. </p> <p>Comprehensive guidelines and the selection criteria can be found in the abovementioned article.</p> <p> </p> <p> </p> <p>References</p> <p>[1] Zhiyuan Cheng, James Caverlee, and Kyumin Lee. You are where you tweet: A content-based approach to geo-locating twitter users. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM '10, page 759{768, New York, NY, USA, 2010. Association for Computing Machinery.<br> [2] Eunjoon Cho, Seth A. Myers, and Jure Leskovec. Friendship and mobility: User movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '11, page 1082{1090, New York, NY, USA, 2011. Association for Computing Machinery.<br> [3] Yunhe Feng and Wenjun Zhou. Is working from home the new norm? an observational study based on a large geo-tagged covid-19 twitter dataset, 2020.<br> [4] Yelp Inc. Yelp Open Dataset, 2021. Retrieved from https://www.yelp.com/dataset. Accessed October 26, 2021.<br> [5] Mayank Kejriwal and Sara Melotte. A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas, January 2021.<br> [6] Rabindra Lamsal. Design and analysis of a large-scale covid-19 tweets dataset. Applied Intelligence, 51(5):2790{2804, 2021.<br> [7] Geraud Le Falher, Aristides Gionis, and Michael Mathioudakis. Where is the Soho of Rome? Measures and algorithms for finding similar neighborhoods in cities. In 9th AAAI Conference on Web and Social Media - ICWSM 2015, Oxford, United Kingdom, May 2015.<br> [8] Yong Liu, WeiWei, Aixin Sun, and Chunyan Miao. Exploiting geographical neighborhood characteristics for location recommendation. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM '14, page 739{748, New York, NY,USA, 2014. Association for Computing Machinery.<br> [9] Hatem Mousselly-Sergieh, Daniel Watzinger, Bastian Huber, Mario Doller, Elood Egyed-Zsigmond, and Harald Kosch. World-wide scale geotagged image dataset for automatic image annotation and reverse geotagging. In Proceedings of the 5th ACM Multimedia Systems Conference, MMSys '14, page 47{52, New York, NY, USA, 2014. Association for Computing Machinery.<br> [10] Dingqi Yang, Daqing Zhang, Vincent W. Zheng, and Zhiyong Yu. Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(1):129{142, 2015.<br> [11] Simple Maps. Basic World Cities Database, 2021. Retrieved from https://simplemaps.com/data/world-cities. Accessed September 3, 2021.</p>
EMB3Rs Open digital research data
<p>The EMB3Rs Unified Modelling Platform is a tool to assist on modelling the recovery of excess heat and its reuse to meet final energy demand within and beyond the boundaries of industrial sites. The tool consists of a knowledge base and several simulation modules.<br> This database comprises the research data generated in the course of the EMB3Rs project by using the EMB3Rs platform.<br> The pdf file contains the detailed description of the database content</p> <p>It has been deposited at Zenodo’s open data repository with DOI 10.5281/zenodo.7994255.</p>
Database of Digital Technology for Co-creation (2DTC)
<p>This CSV file contains a list of 50 technologies commonly used in the co-creation process. The database is organised around a taxonomy for digital technology used in co-creation, developed by the Health CASCADE consortium. It can be used to select the most adapted digital technology for specific co-creation processes based on detailed functional and non-functional requirements.</p> <p>Futur development will allow taxonomy development, increase the number of classified technologies, and update the available ones.</p>
A2.2a Digital repositories data citation practices. Supplementary material
<p>Data to complement the quantitative analysis of data citation practices in digital repositories based on metadata records from the re3data.org repositories registry.</p> <p>Data was retrieved using re3data.org API on 23-02-2023 and 06-03-2023 and processed using the OpenRefine software.</p> <p>Part of "A FAIR-enabling citation model for Cultural Heritage Objects" project activities.</p>
Digital Elevation Models of Hunga Volcano; pre- and post- 15 January 2022 eruption
<p>This dataset contains digital elevation models (DEM) of the Hunga Volcano complex. The first is a pre-2022 eruption elevation model. The second is a post-2022 eruption elevation model.</p><p>Hunga Volcano is a volcanic complex near the island of Tongatapu in the Kingdom of Tonga. The volcano rises from ~2,500 m depth, a caldera at its summit, and two islands, Hunga Tonga and Hunga-Ha'apai, at the on the rim of the caldera. An eruption during December 2014-January 2015 was centered between the islands and combined them into one larger structure named Hunga Tonga – Hunga Ha'apai (HTHH). HTHH erupted violently on 15th January 2022, sending large clouds of ash into the atmosphere, triggering a tsunami, and reducing the size of the islands of Hunga Tonga and Hunga Ha'apai.</p><p>As a result of this event, the NIWA-Nippon Foundation Tonga Eruption Seabed Mapping Project (TESMaP) is a multidisciplinary research plan involving geological, oceanographic and biological studies that centered around three objectives:</p><ol><li>To determine the impacts of volcanic ash on ocean productivity, species composition, and biogeochemical cycling in the water column.</li><li>To determine the immediate nature and extent of the impact of ash fall/turbidity flows on deep-sea sediments and benthic ecosystems.</li><li>To determine the recovery potential of the deep-sea ecosystem.</li></ol><p>This project involved two survey voyages of the volcano and its surrounding waters. The first was carried out from <i>RV Tangaroa</i> in April and May 2022 (Mackay et al., 2022) and the second was carried out by the <i>USV Maxlimer</i> in August 2022.</p><p>TESMaP was funded from a combination of sources including The Nippon Foundation, Japan; the Natural Environmental Research Council, UK, Japan Agency for Marine Earth Science and Technology, the Tangaroa Reference Group (TRG) for ship time and the NIWA Oceans Centre. Support was given by The Nippon Foundation Seabed 2030 project and by GEBCO Alumni.</p>
Data - Low-Noise Phase-Sensitive Optical Parametric Amplifier with Lossless Local Pump Generation using a Digital Dither Optical Phase-Locked Loop
<p>This dataset contains measurement data and processing code for the results published in "Low-Noise Phase-Sensitive Optical Parametric Amplifier with Lossless Local Pump Generation using a Digital Dither Optical Phase-Locked Loop". The Pyrpl code change used in the work is also attached.</p> <p>This work was funded by the Swedish Research Council (grant VR-2015-00535).</p>
Karten und Netzwerkabbildungen zum Datensatz "Digital erschlossene NS-Arbeitsbücher aus der "Waldwerke GmbH Passau""
<p>Das Repositorium enthält Karten und Netzwerkvisualisierungen zum Datensatz "Digital erschlossene NS-Arbeitsbücher (Laufzeit: 1935–1945) aus der „Waldwerke GmbH Passau“ (1942–1945)" (DOI: 10.5281/zenodo.7573559).</p> <p>Die Karten stellen in verschiedener Zusammensetzung die Geburts- und Beschäftigungsorte der Inhaberinnen und Inhaber der Arbeitsbücher dar. Sie wurden mithilfe von <a href="https://qgis.org/">QGIS</a> erstellt.</p> <p>Die Netzwerkvisualisierungen zeigen die Beziehungen zwischen beschäftigenden Betrieben und dort beschäftigten Arbeiterinnen bzw. Arbeitern. Eine Kante zeigt dabei an, dass eine Person mindestens einmal im verknüpften Betrieb beschäftigt war. Sie wurden mithilfe von <a href="https://gephi.org/">Gephi</a> erstellt.</p> <p>Die Abbildungen sind Farbvarianten der in folgendem Aufsatz erstmals publizierten Abbildungen:</p> <p><em>Alina Ostrowski, Jorit Hopp, Benjamin Seebröker, Lukas Bartl, Markus Gerstmeier, Heiko Brendel, Simon Donig</em> und <em>Malte Rehbein</em>: Arbeitsmigration in der süddeutschen NS-Kriegswirtschaft. Computergestützte Datenexploration mittels historischer Geoinformation und Netzwerkanalyse, in: Geschichte in Wissenschaft und Unterricht 74 H. 9/10 (2023), S. 550–570.</p> <p>Dort findet sich auch Genaueres zur Auswahl der Daten und Erstellung der Abbildungen sowie zur historischen Einordnung der Visualisierungen.</p>
D^2EPC BIM-based Digital Twin data model example and real-time building measurements
<p>An example building digital twin data model, developed within the H2020 project D^2EPC, corresponding to the first out of six Case Studies (CERTH nZEB Smart House DIH). The following files are provided:</p><p>i) The BIM-based data model of the building parameters (.json file)</p><p>ii) Building real-time collected measurements within the project (in separate .json files):</p><ul><li>Living room: CO2, temperature, humidity, luminance, presence, PM2.5, TVOCs, loudness, smoke</li><li>Office: temperature, humidity, luminance, presence</li><li>Entire ground floor: HVAC system electrical energy consumption</li><li>Entire first floor: HVAC system electrical energy consumption</li><li>Entire building: electrical energy consumption (lighting & appliances)</li><li>Building PV installation: electrical energy production</li></ul><p> </p>
Local water years for 4-digit hydrologic unit areas across the conterminous United States
Quantifying and predicting precipitation and water flow, and their influence on ecosystems is challenged by the dynamic relationships between and timing of precipitation and water fluxes. To help with these challenges, scientists use “water year” to examine and predict the impacts of precipitation and relevant extreme climatic and hydrological events on ecosystems. However, traditional water year definitions used in the U.S. have limited considerations of areal variations in climate and hydrology, which need to be considered when studying ecosystems at regional or national scales. We developed local water year (LWY) values that consider spatial variation using existing definitions whereby the water year begins in the month with the lowest or highest average monthly streamflow. We employed a spatial interpolation technique to assign the start and end months of two LWY timeframes to 202 subregions across the conterminous U.S. that range from 4,384 to 134,755 km2. This dataset can be linked with diverse climate, terrestrial, and aquatic data for broad-scale studies.
Digital Elevation Model (DEM) of the Duplin River and adjacent intertidal areas near Sapelo Island, Georgia
Topographic and bathymetric data were collected for the Duplin River and adjacent intertidal areas near Sapelo Island, Georgia, using high-precision multibeam SONAR equipment. The bathymetric survey was performed from 09-Dec-2009 to 12-Dec-2009. This study was conducted to create a base map of bathymetry, morphology and physical habitat of the Duplin River, which is a primary study site of the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) project.
Digital Elevation Model (DEM) of Doboy Sound at the mouth of the Duplin River near Sapelo Island, Georgia
The purpose of this study was to map the bathymetry of Doboy Sound near the mouth of the Duplin River adjacent to Sapelo Island, Georgia. This study extends a previous bathymetry mapping project conducted in 2009. The primary objective of the Duplin River project in 2009 was to provide data in support of understanding the sediment and water exchange process between intertidal areas and tidal creeks of the Duplin River. The Center for Marine and Wetland Studies (CMWS) surveyed the Doboy Sound using the Simrad EM3002D Multibeam Echosounder (MBES) in April 2011. A digital elevation model (DEM) was then developed based on the depth survey data.
Corrected LIDAR-derived digital elevation model of the Duplin River salt marshes
LIDAR (light detection and ranging) data were acquired on March 9-10, 2009 by the National Center for Airborne Laser Mapping (NCALM) for the Duplin River (35 km2). A 1 m spatial resolution gridded digital elevation models (DEM) was produced from these data. The accuracy of the DEM was assessed using real time kinematic (RTK) GPS ground reference data and elevations were corrected following the method of Hladik and Alber (2012).
Hubbard Brook Experimental Forest: 1 meter LiDAR-derived and Hydro-enforced Digital Elevation Models, 2012
This data package contains a 1 m LiDAR-derived digital elevation model (DEM) and a 1 m hydro-enforced DEM across Hubbard Brook EF. The LiDAR was collected during leaf-off and snow-free conditions by Photo Science, Inc. in April 2012 for the White Mountain National Forest (WMNF). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Digital elevation models derived from UAV overflights of the fifteen NPP study sites at Jornada Basin LTER in 2019
This data package contains digital elevation models (DEMs) of each of the 15 NPP study sites at the Jornada Basin LTER in southern New Mexico, USA. The models were derived from raw images collected during uncrewed aerial vehicle (UAV) overflight missions conducted in late summer and early autumn of 2019 (1 overflight day per site). For each site one or two missions were flown during an afternoon using a DJI Phantom 4 UAV, and between 450 and 1300 12.4 megapixel RGB images were captured. A subset of images captured at each site were loaded into Agisoft Metashape software to derive digital elevation models using a structure from motion method. These models include elevations of vegetation and other aboveground features. The raw images are not provided but can be made available via project PIs. This data package includes one centimeter resolution DEMs of all 15 sites as geotiff raster files. Other derived products from these UAV missions, including orthomosaic photos, digital terrain models, and sparse point clouds, are available in other EDI data packages (knb-lter-jrn.210543001, knb-lter-jrn.210543003, and knb-lter-jrn.210543004, respectively). This study is complete.
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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.