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Digital Resistance Telegram
Digital Resistance Support Source: Objaverse 1.0 / Sketchfab
Digital Kids: Imagine, Build and Reveal, #15
Entry from V&A REVEAL Festival workshop - Digital Kids: Imagine, Build and Reveal - Saturday 1 - Sunday 2 July, 2017. Participants reimagined the Museum in Minecraft. Assets created by @BlockWorks Source: Objaverse 1.0 / Sketchfab
Statue of Marcus Aurelius, digitally re-gilded
A modified version of the model posted on Sketchfab by [noe-3d.at](https://sketchfab.com/www.noe-3d.at) as [Reiterstatue Marc Aurel](https://sketchfab.com/3d-models/reiterstatue-marc-aurel-6acbd43f7a9849fb9b951f9ab219673b). This version has been modified in the following ways: The base has been removed to correspond to the original statue in the Capitoline Museum. The textures and lighting have been modified to make the statue look like it still has its original gilding. Source: Objaverse 1.0 / Sketchfab
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
Supplementary material 2 from: Mertens JEJ, Van Roie M, Merckx J, Dekoninck W (2017) The use of low cost compact cameras with focus stacking functionality in entomological digitization projects. ZooKeys 712: 141-154. https://doi.org/10.3897/zookeys.712.20505
Figure S2 : Explanation note: Comparison of the narrow (C, F), normal (A, B, D, G) and wide (E, H) focal step size in two specimens of different 'depth' (A, C–E: Allochroma sp., 2 mm deep; B, E–H: Doryphora sp., 12 mm deep, measured from top of elytra to lowest tarsi). The narrow setting is marginally sharper in some areas; however, deeper parts of the specimen are not in focus. The wide setting produces artefacts around some of the edges, sometimes resulting in less sharp regions.
Supplementary material 1 from: Mertens JEJ, Van Roie M, Merckx J, Dekoninck W (2017) The use of low cost compact cameras with focus stacking functionality in entomological digitization projects. ZooKeys 712: 141-154. https://doi.org/10.3897/zookeys.712.20505
Figure S1 : Explanation note: Lightbox setup, the camera rests on top of the cut-off bucket, its lens protruding though the hole in the middle. The specimen is usually shielded from direct light by a free-standing cylinder of tracing paper (not depicted). The LED strips on the inside are powered through a 12V adapter.
A Structured Review on the Human Digital Twin (until March 2024)
<p>This bibliography is related to the paper "Perspectives-Observer-Transparency - Structured Review of the Human Digital Twin and Novel Paradigm for Modelling the Human in Human-To-Anything Interaction" by Nils Mandischer, Alexander Atanasyan, Michael Schluse, Jürgen Roßmann, and Lars Mikelsons, currently under submission to the IEEE International Conference on Systems, Man, and Cybernetics as of April 2024.<br> <br>The references are collected as part of a structured literature review on modelling approaches for humans and artificial agents and the perspectives taken on them. Particular interest lays on the (Human) Digital Twin which proved to be a relatively nascent approach that quickly gained interest at the time of writing. Please refer to the publication for a more detailed description of the review method.<br> <br>The conference paper DOI will be provided to Zenodo upon publication. For updates, additional context, or inquiries, please contact Alexander Atanasyan [Atanasyan@mmi.rwth-aachen.de] or Nils Mandischer [nils.mandischer@uni-a.de].</p>
A Dataset on Digital Trade Provisions
TAPED A Dataset on Digital Trade Provisions The TAPED (Trade Agreement Provisions on Electronic-commerce and Data) dataset seeks to comprehensively trace developments in the area of digital trade governance. The dataset includes a detailed mapping and coding of all preferential trade agreements (PTAs) that cover chapters, provisions, annexes and side documents that directly or indirectly regulate digital trade. TAPED covers more than 430 PTAs concluded since 2000. A total of 116 different items were coded, including provisions on digital trade, intellectual property, key services sectors, government procurement, trade in goods, as well as general and specific exceptions and new cross-cutting data economy issues. The codebook made available below explains in detail our methodology and the questions behind the coded items. The TAPED dataset was initially launched as part of the research project 'Governance of Big Data in Trade Agreements' and received funding from the Swiss National Science Foundation under the National Research Programme (NRP)75: Big Data (2017–2021). Instrumental in the creation of the dataset were in particular Dr Rodrigo Polanco, who was a postdoctoral fellow under the project, as well as Dr Rahel Schär. The scope and substance of the TAPED dataset were announced in 2020 with the publication: Mira Burri and Rodrigo Polanco, 'Digital Trade Provisions in Preferential Trade Agreements: Introducing a New Dataset', Journal of International Economic Law 23:1 (2020), 1-34. Currently, the TAPED dataset is continued, expanded and modified under the ERC Consolidator Grant project 'TRADE LAW 4.0: Trade Law for the Data-Driven Economy' (2021–2026), which is financed by the European Research Council and led by Mira Burri as a Principal Investigator. Versions of the TAPED dataset as of 2022 should be cited as: Mira Burri, Maria Vasquez Callo-Müller and Kholofelo Kugler, TAPED: Trade Agreement Provisions on Electronic Commerce and Data, available at: https://unilu.ch/taped with the date of retrieval.
Digital Image Correlation for the Structural Helth Monitoring of composite GFRP materials
Open the record for dataset details and reuse information.
Connecting the Digital Bridges Factors Influencing Farmers' Attitude towards e-Governance
<p><span>The e-governance streamlines the delivery of government services to the farmers, such as agricultural subsidies, credit facilities, insurance schemes, extension services and other citizen charters. The objective of this study was to determine the factors influencing the attitude of farmers towards e-governance. The scale used for attitude measurement was standardized through Judge-rating and pre-testing and forty-one statements were used to assess the attitude of the farmers on a Likert scale. One hundred twenty-five (125) farmers from Phulbari upazila in Dinajpur district of Bangladesh were selected for data collection using a multi-stage random sampling procedure through structured interview method.<span> </span>The findings revealed that a significant proportion (68.0 percent) of the respondents held a moderately to highly favourable attitude towards e-governance. Although seven socio-economic factors had significant relationship with farmers’ attitude towards e-governance; the key influencing factors identification through multiple linear regression and stepwise regression indicated that knowledge of e-governance, educational qualification, age, and organizational participation were the most important. Thus, to enhance farmer engagement and trust in e-governance, policy formulation should be aimed at improving farmers’ knowledge of e-governance involves a multifaceted approach that addresses their specific needs and challenges need to be addressed.</span></p>
Empowering Communities in Cybersecurity: Building Capacity Together for Digital Security Workshop
<p>This event is part of our broader research objective to develop a cybersecurity capacity building framework for rural communities in the Indo-Pacific region. Funded and supported by the Oceania Cyber Security Centre (OCSC), based in Melbourne, Australia.</p> <p>The OCSC, a not-for-profit organisation jointly owned by eight Victorian universities, specialises in cybersecurity assessment, evaluation, and research. Their focus on building regional capabilities encompasses national cybersecurity capacity assessments, independent evaluations, localized research, and educational programs. </p> <p>This workshop, held at Claro Hotel on February 22, 2024, and collaboratively organised with Swinburne University of Technology, Melbourne, Universitas Hasanuddin, Universitas Negeri Makassar, and Politeknik Negeri Ujung Pandang. It serves as a forum bringing together participants from diverse backgrounds, such as education, healthcare, banking, and more, to discuss the challenges and opportunities in cybersecurity. The participants were divided into two groups and given cybersecurity cases to discuss. The primary goal of this workshop is to develop an effective framework to enhance cybersecurity capacity in the community.</p> <p>In this workshop, we value the contributions and feedback from participants, which help us understand their perceptions and awareness of cybersecurity. The focus group discussions, lasting 60-90 minutes, provided valuable insights into how to increase awareness and skills in cybersecurity across various sectors.</p> <p>After the workshop, we summarised the discussion outcomes and concluded the key findings generated. This provided a more comprehensive view of the challenges and opportunities in cybersecurity, laying the groundwork for further steps in enhancing community cybersecurity capacity.</p> <p> </p>
Empowering Communities in Cybersecurity: Building Capacity Together for Digital Security Workshop
<p>This event is part of our broader research objective to develop a cybersecurity capacity building framework for rural communities in the Indo-Pacific region. Funded and supported by the Oceania Cyber Security Centre (OCSC), based in Melbourne, Australia.</p> <p>The OCSC, a not-for-profit organisation jointly owned by eight Victorian universities, specialises in cybersecurity assessment, evaluation, and research. Their focus on building regional capabilities encompasses national cybersecurity capacity assessments, independent evaluations, localized research, and educational programs. </p> <p>This workshop, held at Claro Hotel on February 22, 2024, and collaboratively organised with Swinburne University of Technology, Melbourne, Universitas Hasanuddin, Universitas Negeri Makassar, and Politeknik Negeri Ujung Pandang. It serves as a forum bringing together participants from diverse backgrounds, such as education, healthcare, banking, and more, to discuss the challenges and opportunities in cybersecurity. The participants were divided into two groups and given cybersecurity cases to discuss. The primary goal of this workshop is to develop an effective framework to enhance cybersecurity capacity in the community.</p> <p>In this workshop, we value the contributions and feedback from participants, which help us understand their perceptions and awareness of cybersecurity. The focus group discussions, lasting 60-90 minutes, provided valuable insights into how to increase awareness and skills in cybersecurity across various sectors.</p> <p>After the workshop, we summarised the discussion outcomes and concluded the key findings generated. This provided a more comprehensive view of the challenges and opportunities in cybersecurity, laying the groundwork for further steps in enhancing community cybersecurity capacity.</p>
Datasets for Digital tools and gamification to improve the consumers' waste recycling activity in smart cities
<p>In the data description you can find following:</p> <p>1 Methods of choosing participants</p> <p>2 Translations of survey questions<br>2.1 Survey A<br>2.2 Survey B<br>2.3 Survey C</p> <p>3 Demographic analysis<br>3.1 Respondents' age<br>3.2 Household sizes and dwelling types</p>
Laboratory visualization of fault asymmetry formation via acoustic emission and digital imaging correlation
Open the record for dataset details and reuse information.
A course in small business technology that prepares entrepreneurs for the digital age
<p>It changes all the time in the digital world we live in now. This means that small businesses have a lot of options and a lot of problems. Today's companies work, talk to customers, and compete in the market very differently compared to the past. To do well in this fast-paced world, entrepreneurs need to learn the right things. You should now take a course in small business technology.</p> <h1>A small business technology course to help close the digital divide</h1> <p>Big and small businesses don't always have the same level of digital skills. This has been a problem for a long time. Big companies often have their IT departments and a lot of cash to spend on new tech. However, smaller companies often have trouble keeping up. They might not understand how to use technology well, have the money, or know how to do it.</p> <p>Here's where a <a href="https://www.besttechstrategies.com/" target="_blank" rel="noopener"><strong>Small Business Technology Course</strong></a> comes in: it gives business owners useful tips and information they can use right away. General IT classes might only talk about technical problems, but a small business technology course looks at everything. It talks about how those tools can be used in different business areas and also goes over the basics of technology.</p> <h2>This course is about business technology strategy and how to get around in the digital world.</h2> <p>An important part of a Small Business Technology Course is making a business technology strategy. Simply put, this means making sure that technology projects help the business reach its general goals so that it can grow and come up with new ideas. In the eyes of many small business owners, technology is more of a cost than an investment. Yes, technology can help you succeed if you know how to use it correctly.</p> <h2>When business owners take a course in business technology planning, they learn how to</h2> <p>Think about the chances and the things that are needed. Business owners can find the places where technology can help by knowing the specific issues and needs of their company. You can change technology to reach different objectives, like making things easier, making the customer experience better, or beating the competition.</p> <p>It can be hard to choose the right piece of technology because there are so many to choose from. An online <a href="https://www.bestadsontv.com/profile/467684/Best-Tech-Strategies" target="_blank" rel="noopener"><strong><span>Business Technology Strategy Course</span></strong></a> can teach you how to weigh your options. Scalability, compatibility, and return on investment (ROI) are some of the things that the course talks about. This is the only way that new technologies can help the business.</p> <p>Set up and improve: A lot of businesses fail at the setup stage. It is important to follow through on a well-thought-out plan. Through case studies and hands-on learning, entrepreneurs learn how to use technology solutions and solve everyday problems in the best way. In a world that changes quickly, the school also stresses how important it is to keep improving.</p> <h2>People who own businesses can learn from online IT classes for them.</h2> <p>Small business owners may find it hard to get better at what they do because they don't have enough time or room, or other common barriers to education. Others say that online learning tools have made education more accessible to all, making it easier than ever to get into IT classes.</p> <h2>Companies that take online IT courses can gain in several ways, such as:</h2> <p>There is no set schedule for entrepreneurs to learn, which helps them balance their work and home responsibilities well. People with busy lives can still take online classes on the weekends, during lunch breaks, or after work.</p> <p>Value for money: Going to regular classes can be pricey, and you may also have to pay for things like housing and transportation. But online classes are often less expensive, so companies of all kinds can learn something useful.</p> <p>There are a lot of different classes you can take online. These courses cover a huge range of tech topics, from learning how to use a computer to learning how to code. This makes sure that business owners learn things that are useful for their company by letting them tailor their learning to their needs and interests.</p> <p>We no longer have to worry about geography when we are learning. Folks who want to learn about best practices and new trends from around the world can sign up for classes given by professors from top universities and business experts from all over the world.</p> <h2>Entrepreneurs getting the tools they need to</h2> <p>It's not enough to have a great product or service in the business world today. You must know a lot about how to use technology to speed up, grow, and come up with new ideas to do it well. When business owners take a Small Business Technology Course, they learn the skills and information they need to get around in today's complicated world.</p> <p>People who run small businesses can compete with those of larger companies if they keep learning new things and use the available tools. There's no difference between those who want to learn the basics of safety, use the power of data analytics, or get excited about new technologies like AI.</p>
Dataset Tingkat Kejahatan Internet dan Pentingnya Pendidikan Digital pada Anak dan Remaja
<p>Dataset berisi data hasil respon survey terkait "Pendidikan Digital: Kunci untuk Melindungi Anak dari Penyalahgunaan Teknologi dan Kejahatan Internet di Indonesia". </p>
NOAA Coastwatch Satellite Course (Set up an Application Model of Digital Satellite Data Simulation by Video Graphic Technology of Oceanic data Remotely Sensed of algerian coast)
<p>The goal of the course is to familiarize NOAA/university researchers, Sea Grant professionals and agency/org. partners with different types of ocean satellite data, different tools, and teach participants how to use satellite data in their own research/outreach using their choice of software (NOAA ,2023)</p>
Effectiveness of labels in digital art experience: psychophysiological and behavioral evidence
<p><span>The "Experimental condition" folder comprises data from 40 participants: folders from S01 to S34 include Empatica and Pupil Labs recordings, along with a MATLAB matrix; folders from S35 to S40 contain only the MATLAB matrix.</span></p> <p><span>The "Control condition" folder contains data from 20 participants: folders from S01 to S15 include Empatica and Pupil Labs recordings, along with a MATLAB matrix; folders from S16 to S20 only contain the MATLAB matrix.</span></p> <p><span> </span></p> <p><span>In the MATLAB matrix, each row corresponds to a trial. The columns indicate:</span></p> <p><span>1. </span><span>Number of painting</span></p> <p><span>2. </span><span>Start time of reading label</span></p> <p><span>3. </span><span>End time of reading label</span></p> <p><span>4. </span><span>Start time of viewing painting</span></p> <p><span>5. </span><span>End time of viewing painting</span></p> <p><span>6. </span><span>Response to the item questionnaire about <strong>liking</strong> (<em>“How much do you like the painting?”</em>) - 5-points Likert scale</span></p> <p><span>7. </span><span>Response to the item questionnaire about <strong>interest </strong>(<em>“How interested are you in the painting?”</em>) - 5-points Likert scale</span></p> <p><span>8. </span><span>Response to the item questionnaire about <strong>complexity</strong> (<em>“How complex do you find the painting?”</em>) - 5-points Likert scale</span></p> <p><span>9. </span><span>Response to the item questionnaire about <strong>comprehensibility</strong> (<em>“How comprehensible do you find the painting?”</em>) - 5-points Likert scale</span></p> <p><span>10.</span><span> Response to the item questionnaire about <strong>title informativeness</strong> (“<em>How informative do you find the title of the painting?"</em>) - 5-points Likert scale</span></p> <p><span>11.</span><span>Response to the item questionnaire about <strong>positive emotions</strong> (“<em>How much does this painting evoke positive emotions in you?”</em>) - 5-points Likert scale</span></p> <p><span>12.</span><span>Response to the item questionnaire about <strong>negative emotions</strong> (“<em>How much does this painting evoke negative emotions in you?”</em>) - 5-points Likert scale</span></p> <p><span>13.</span><span>Response to the item questionnaire about <strong>curiosity for other works</strong> (<em>“How curious are you about seeing works by the same author?”</em>) - 5-points Likert scale</span></p> <p><span> </span></p> <p><span>The “Empatica” folder contains several <em>”.csv” </em>files extracted from the Empatica wristband. In our work, we used:</span></p> <p><span>- </span><span>EDA - data from the electrodermal activity sensor expressed in microsiemens (μS). The sample rate is 4 Hz.</span></p> <p><span>- </span><span>HR - Average heart rate extracted from the BVP signal. The first row is the initial time of the session expressed as unix timestamp in UTC. The second row is the sample rate expressed in Hz.</span></p> <p><span>The “Pupil” folder contains several “<em>.csv” </em>files obtained from the Pupil Core Eye Tracker. These files have been exported using the Pupil Player Software. In our work, we used the exported file “pupil_positions.csv”, that includes:</span></p> <p><span>- </span><span>timestamp - timestamp of the source image frame</span></p> <p><span>- </span><span>index – associated frame: closest world video frame</span></p> <p><span>- </span><span>id - 0 or 1 for right and left eye (from the wearer's point of view)</span></p> <p><span>- </span><span>confidence - is an assessment by the pupil detector on how sure we can be on this measurement. A value of `0` indicates no confidence. `1` indicates perfect confidence.</span></p> <p><span>- </span><span>norm_pos_x - x position in the eye image frame in normalized coordinates</span></p> <p><span>- </span><span>norm_pos_y - y position in the eye image frame in normalized coordinates</span></p> <p><span>- </span><span>diameter - diameter of the pupil in image pixels as observed in the eye image frame (is not corrected for perspective)</span></p> <p><span>- </span><span>method - string that indicates what detector was used to detect the pupil (2d the pupil appears as an ellipse available in `3d c++` and `2D c++` detector)</span></p> <p><span>- </span><span>ellipse_center_x - x center of the pupil in image pixels</span></p> <p><span>- </span><span>ellipse_center_y - y center of the pupil in image pixels</span></p> <p><span>- </span><span>ellipse_axis_a - first axis of the pupil ellipse in pixels</span></p> <p><span>- </span><span>ellipse_axis_b - second axis of the pupil ellipse in pixels</span></p> <p><span>- </span><span>ellipse_angle - angle of the ellipse in degree.</span></p> <p><span> </span></p> <p><span>The gaze data, as described in the Data processing section of our work, were extracted from the video recordings of each session, which were not uploaded here for privacy reasons.</span></p>
APROXIMACIÓN A LAS CONTRIBUCIONES TEMÁTICAS SOBRE MARKETING DIGITAL
<p><span>La investigación tuvo como objetivo realizar una aproximación a las contribuciones temáticas sobre Marketing Digital (MD). Se trata de un estudio descriptivo con diseño documental, basado en la revisión bibliográfica realizado bajo los supuestos de la metodología cualitativa. Se utilizó la técnica de análisis de contenido teniendo como unidades de información 86 artículos científicos publicados en el período 2018-2020, registrados en la base de datos REDALYC. Las contribuciones temáticas resultantes enfatizan aspectos tales como: los factores que definen el comportamiento del cliente; la forma óptima de utilizar las tecnologías de información y comunicación, como vía para lograr la innovación en el marketing y en el contexto organizacional, de forma general; el impacto del MD en el cumplimiento de los requerimientos específicos de poblaciones ubicadas en sectores públicos y privados; así como, la gestión estratégica de recursos y capacidades en el área analizada. Como conclusión se destaca que el estudio de las contribuciones temáticas responde a la necesidad de conocer y comprender el alcance del MD en las organizaciones y sus implicaciones en el desempeño, así como su impacto en determinados tipos de organización.</span></p>
Digital image correlation displacements and strains around a growing fatigue crack in an AA2024-T3 aluminium alloy
<p>This repository contains the data used in the research article:</p> <p>Strohmann, Melching, Paysan, Dietrich, Requena, Breitbarth. Next generation fatigue crack growth experiments of aerospace materials. <em>Scientific Reports</em>, 2024, <a href="https://doi.org/10.1038/s41598-024-63915-x" target="_blank" rel="noopener">https://doi.org/10.1038/s41598-024-63915-x</a>.</p> <p> </p> <p><strong>General Description</strong><br>The dataset contains digital image correlation (DIC) data of a growing fatigue crack in an AA2024-T3 alloy. For the experiment, two independent DIC measurement devices were used – a global full-field DIC and a local microscopic DIC. Thus, the dataset consists of two main directories. One for the 3D DIC data ("global_3d_dic") and a second one for the 2D microscopic DIC data ("local_2d_dic"). Both of these are described and connected by rich metadata.<br>The 3D DIC data directory contains two subdirectories (a "Nodemaps" directory and a "Connections" directory). Both of them contain 797 '.txt'-files. The "Nodemaps" give the DIC results (i.e. coordinates, displacement and strains) for every timestep throughout the experiment. These are usually maximum, minimum, and mean load of a certain load cycle. However, for few crack lengths, we obtained DIC data for a higher number (ca. 100) of images within one load cycle. The nodemaps' format and structure is optimized for data processing in the open-source Python package <a title="CrackPy" href="https://doi.org/10.5281/zenodo.10990494" target="_blank" rel="noopener">CrackPy</a>. The last integer number of each filename can be interpreted as a 'timestep' throughout the experiment. The "Connection" files represent the connections of the DIC facet center coordinates. These are necessary to export the "Nodemap" data to any mesh like dataset, e.g. for VTK. <br>The 2D microscopic DIC directory contains 3 subdirectories ("80", "90", "95") for predefined positions with respect to the specimen coordinate system. For each location, a number of DIC data are stored, again within two subdirectories "Nodemaps" and "Connections" as '.txt'-files. For the 2D DIC data, the last integer of each name cannot be correlated to a timestep. Instead, we provide a descriptive file "local_2d_microscopic_coordinates_by_nodemaps.csv" linking each and every "Nodemap"-file to its respective coordinates and timestep (i.e. the load cycles).</p> <p>To describe the data, we distinguish between<br>1. Higher-level metadata - these data contain information about the experiment and material. The data do not change between timesteps and are given within this description.<br>2. Timestep metadata - these data contain information about one timestep of the experiment and are stored in the header of each "Nodemap"-file.</p> <p> </p> <p><strong>Higher-level Metadata</strong><br>The experiment is described in detail in the reference publication by <a title="Strohmann et al. (2024)" href="https://www.researchsquare.com/article/rs-3128435/v1" target="_blank" rel="noopener">Strohmann et al. (2024)</a> and a summary is given below. Moreover, we provide a dictionary in javascript object notation explaining terms which are used in the higher-level metadata. We use such a dictionary since no standardized ontology is currently available. This dictionary is stored in the main directory as "higher_level_metadata_dictionary.json".</p> <p><em>Material </em><br>A commercially available AA2024-T3 aluminum alloy was tested in L-T orientation, i.e. rolling direction, L, parallel to the load axis. The specimen had a width W = 160 mm cut from a rolled sheet of 2 mm.</p> <p><em>Digital image correlation</em><br>For 3D DIC, we used a GOM Aramis 12M system with a facet size of 20 x 20 pixels and a 16 pixels facet distance. One facet, therefore, covers ~0.614 x 0.614 mm². For the 2D microscopic DIC we captured images using a Zeiss STEMI 206C light optical microscope (LOM), equipped with a Basler a2A5320-23µmPro global shutter CMOS camera. One image has a size of 10.2 x 5.7 mm², 5328 x 3040 Pixels and a facet size of 40x40 pixels (distance of facet center points 30 pixels). The LOM was mounted to a robotic arm, a KUKA lbr Iiwa Cobot.</p> <p><em>Fatigue crack growth</em><br>We used a standard uniaxial servo-hydraulic testing rig. We applied a cyclic load ranging from Fmin = 4.5 kN to Fmax = 15 kN, i.e. R=Fmin/Fmax = 0.3. Throughout the experiment, we measured the crack length using direct current potential drop (DCPD).</p> <p><em>Image acquisition during fatigue crack growth</em><br>We acquired reference images for the DIC calculations before the experiment. For the global DIC, this is simply an image of the unloaded specimen. For the local microscopic DIC, the reference images are acquired in a checker board pattern with an overlap of 70 %. The depth of focus was calibrated for each image individually following (see <a title="Paysan et al. (2023)" href="https://doi.org/10.1007/s11340-023-00964-9" target="_blank" rel="noopener">Paysan et al. (2023)</a>). Images were acquired every 0.5 mm of crack extension at minimum, maximum and 0.5(Fmax- Fmin).</p> <p> </p> <p><strong>Timestep Metadata</strong><br>The timestep-wise metadata is stored in the individual DIC output files, "Nodemaps". We explain the terms used in a second dictionary, "timestep_level_metadata_dictionary.json". For all DIC data, we stored all data coming from the machine controller, i.e. number of cycles, force, displacement of the cylinder and also potential and crack length calculated from the potential as well as current values for back face strain gauges at both back faces of the MT specimen. In addition, for the local microscopic DIC data, we also store the current location of the center point of the image with respect to the global coordinate system provided by the current position of the robot carrying the LOM.</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.