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3,853 results for “videos”
Tracing and visualisation of contributing water sources in a model of flood inundation: video supplement
<p>These are video supplement files to Wilson & Coulthard (2021), produced using version 1.8f-WS of CAESAR-Lisflood software, <a href="https://doi.org/10.5281/zenodo.5541122">available on Zenodo here</a>. For a full description of the methodology and case studies, please refer to the paper which is available here: <a href="https://doi.org/10.5194/gmd-2021-340">https://doi.org/10.5194/gmd-2021-340</a>.</p> <p>Video animations (no audio) for the following case studies are included:</p> <p>1. <strong>Carlisle, United Kingdom</strong> (carlisleanimation-sourcetracing.avi and carlisleanimation-depthonly.avi):</p> <ul> <li>Simulation of the January 2005 flood event at the confluence of the Rivers Caldew, Petteril and Eden, using a 5 m grid.</li> <li>Both water source tracing and depth only versions are provided.</li> <li>In the water tracing version, blue colours represent flows from the River Eden, reds are from the River Petteril and greens are from the River Caldew; darker shades represent deeper water. Available on YouTube here: <a href="https://youtu.be/xOtOi06cXvA">https://youtu.be/xOtOi06cXvA</a></li> <li>In the depth only version, darker shades of blue represent deeper water, with no information about the water source in a grid cell. Available on YouTube here: <a href="https://youtu.be/aFz-sPRGHVE">https://youtu.be/aFz-sPRGHVE</a></li> </ul> <p>2. <strong>Avon-Heathcote estuary in Christchurch, New Zealand</strong> (avonheathcoteanimation.avi):</p> <ul> <li>Simulation for July 2017, which included a high flow event on 22 July, using a model grid of 10 m.</li> <li>Blue colours represent flows from tide, reds are from the River Avon and greens are from the Heathcote River; darker shades represent deeper water.</li> <li>Available on YouTube here: <a href="https://youtu.be/Fczr5tczzXU">https://youtu.be/Fczr5tczzXU</a></li> </ul> <p>3. <strong>Amazon </strong>(amazonanimation.avi):</p> <ul> <li>Simulation at the confluence of the Solimões (mainstem Amazon) and Purus rivers in the central Amazon, Brazil, for the period of 1 October 2013 through December 2014, using a ~270 m model grid.</li> <li>Red colours are from the Solimões, green colours are from the Purus; darker shades represent deeper water.</li> <li>Available on YouTube here: <a href="https://youtu.be/PknAL_8fd1I">https://youtu.be/PknAL_8fd1I</a></li> </ul> <p>4. <strong>Planar slope</strong> (planaranimation.avi):</p> <ul> <li>A simple test case consisting of a 2000 x 1000 m planar slope (0.001 m/m), with walls added at 250 m intervals across the slope, each of which has several gaps through which water can flow. Model grid was 5 m.</li> <li>Eight water sources were traced in total, with three visualised in the animation: red = source 2, green = source 4, blue = source 6. Depths are shown in the middle plot.</li> <li>Available on YouTube here: <a href="https://youtu.be/DTw8ysJtx8o">https://youtu.be/DTw8ysJtx8o</a></li> </ul> <p>Please feel free to use these animations, under the terms of the CC-BY-4.0 license. Please provide a link back to this site and a citation to Wilson & Coulthard (2021).</p> <p>Reference:</p> <p>Wilson, M. D. and Coulthard, T. J.: Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation, Geosci. Model Dev. Discuss. [preprint], <a href="https://doi.org/10.5194/gmd-2021-340">https://doi.org/10.5194/gmd-2021-340</a>, in review, 2021</p>
Onboarding/Registration (TRIPLE Video Tutorial Series)
<p>In this tutorial series, researcher Agnieszka Szulińska and Research Infrastructure Coordinator Edward Gray discuss gotriple.eu. This episode explores the opportunities GoTriple offers for registered users.</p>
Animation video on responsible research and innovation (RRI)
<p>This is a short animation video about responsible research and innovation. Watch it here: https://youtu.be/fOG5U2QweBo</p> <p>In research guided by responsible research and innovation (RRI), members of society work with scientists to align both the entire research process and its results with societal needs. The researcher and the research institute or research funder turn openly to citizens and involve the relevant societal actors in formulating research questions and collecting and analysing data. In RRI-guided research, scientists never forget to ask themselves: will our research do good to society and the Earth? What will its long-term social, ethical, and ecological consequences be? If a researcher or research-performing organisation integrates responsibility into their work, they will feel more useful and impactful in their profession, all in order to improve the lives of their fellow human and non-human beings. For this reason, it is important for researchers to reflect on their own organisational operations and change their daily routines. This is exactly what the Co-Change Project started to experiment with. In order to raise awareness of RRI, the project initiated ongoing deliberation within and beyond its own organisations through discussing ethics, gender, and open-science-related topics that engage all units that deal with research and innovation, as well as involving external experts and stakeholders in co-creating change.</p> <p> </p>
DATABASE OF THE DIGITAL ELEVATION MODELS OF THE SKEIÐARÁRSANDUR KETTLE-HOLES (S ICELAND), JUNE 2022 - Part II. VIDEO
<p>The database contains 83 video files in .MOV format, shot by a digital camera at 23.98 frames per second. The average length of videos is 100–600 seconds. They are documentation of fieldwork carried out in June 2022, aimed at preparing the footage to generate high-resolution digital elevation models (Part I) using the 'Structure from Motion' technique. The study covered kettle-holes of the glacial flood origin located at Skeiðarársandur in S Iceland.</p>
Chemotion ELN Instruction Videos
<p><strong>Chemotion ELN Instruction Videos</strong></p> <p>Chemotion<sup>[1]</sup> is an open source system for storing and managing experiments and molecular data in chemistry and its related sciences. The system mainly consists of two components: the electronic laboratory notebook (ELN) Chemotion ELN<sup>[2]</sup> for recording and evaluating experiments and their analytical data, and the Chemotion repository<sup>[3,4]</sup> for publishing these data. As the ELN is linked to the repository, entries can be easily transferred from the lab journal to the repository. Data, including their machine-readable metadata, are published in the repository under open access conditions and thus fulfill the FAIR data principles (findable, accessible, interoperable, reusable).<sup>[5]</sup> These principles serve as a foundation for sustainable data management within the framework of good scientific practice.<sup>[6]</sup></p> <p>For the use of Chemotion and to explain its basic functions, we created explanatory videos to help newcomers getting started with the ELN. These videos cover important topics from creating molecules, to evaluating analytic data, to sharing entries. The videos are divided into different topic blocks and deal in each case with specific functions. The structure of the topic blocks and videos is provided in the PDF document in this Zenodo entry. Please note that entries with a gray background in this document will only be available in later versions of the Zenodo entry.</p> <p><strong>Chemotion ELN Instruction Videos on YouTube</strong></p> <p>Find the instruction videos additionally on Chemotion's YouTube channel: <a href="https://www.youtube.com/playlist?list=PL1AonKd9WAd8cDjzXGiNu0ndoctNu0izs">https://www.youtube.com/playlist?list=PL1AonKd9WAd8cDjzXGiNu0ndoctNu0izs</a></p> <p><strong>Acknowledgement</strong></p> <p>First and foremost, the authors would like to thank Nicole Parks for recording the English audio tracks. The authors are grateful to Nicole Jung for further explanations of Chemotion's features and help in creating and revising the videos. Furthermore, the authors would like to thank Anna Manukânc for her help with the conceptual development of the videos.</p> <p><strong>Literature</strong></p> <p>[1] Chemotion. <a href="https://chemotion.net/">https://chemotion.net/</a> (accessed 02/19/2023).</p> <p>[2] Tremouilhac, P., Nguyen, A., Huang, Y.-C., Kotov, S., Lütjohann, D. S., Hübsch, F., Jung, N., Bräse, S., <em>J. Cheminform. </em><strong>2017,</strong> <em>9</em>, 54.</p> <p>[3] Tremouilhac, P., Lin, C.-L., Huang, P.-C., Huang, Y.-C., Nguyen, A., Jung, N., Bach, F., Ulrich, R., Neumair, B., Streit, A., Bräse, S., <em>Angew. Chem. Int. Ed. </em><strong>2020,</strong> <em>59</em>, 22771-22778; <em>Angew. Chem. </em><strong>2020,</strong> <em>132</em>, 22960-22968.</p> <p>[4] Tremouilhac, P., Huang, P.-C., Lin, C.-L., Huang, Y.-C., Nguyen, A., Jung, N., Bach, F., Bräse, S., <em>Chemistry–Methods </em><strong>2021,</strong> <em>1</em>, 8-11.</p> <p>[5] Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., ’t Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., Mons, B., <em>Sci. </em><em>Data </em><strong>2016,</strong> <em>3</em>, 160018.</p> <p>[6] Deutsche Forschungsgemeinschaft. <em>Guidelines for Safeguarding Good Research Practice. Code of Conduct</em>. <strong>2019</strong>, <a href="https://doi.org/10.5281/zenodo.3923602">https://doi.org/10.5281/zenodo.3923602</a>.</p> <p><strong>Useful Links</strong></p> <p>Chemotion. <a href="https://chemotion.net/">https://chemotion.net/</a> (accessed 02/19/2023).</p> <p>Chemotion ELN Documentation. <a href="https://chemotion.net/docs/eln/">https://chemotion.net/docs/eln/</a> (accessed 02/19/2023).</p> <p>Chemotion Repository. <a href="https://www.chemotion-repository.net/welcome">https://www.chemotion-repository.net/welcome</a> (accessed 02/13/2023).</p> <p>Molecule Archive of the Compound Platform. <a href="https://compound-platform.eu/home">https://compound-platform.eu/home</a> (accessed 02/13/2023).</p>
video data from inside a Ruhrstahl Heraeus degasser
<p>An Ruhrstahl Heraeus plant is used in steel making to adjust the chemical composition of the steel right before casting. The process and the setup to generate the images provided in this data set are described more in detail in DOI: 10.1002/srin.202200060. The two major treatment types are degassing and decarbonization. The first focuses on removing desolved gases from the melt. The second includes the removal of carbon from the melt in addition to the degassing.</p> <p>The data set provided here contains images taken from 90 treatments. Each folder contains two subfolders "empty" and "treatment". In the folder "empty" 119 images from the empty chamber right before the treatment starts. The folder "treatment" contains images from the treatment. These have already been filtered using processdata for good viewing conditions. The process data itself is not part of the data set.</p> <p>The INEVITABLE project, carried out in a collaboration with voestalpine Stahl GmbH, has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 869815. This paper reflects only the authors' views, and the European Commission is not responsible for any use that may be made of the information it contains. The authors moreover gratefully acknowledge the funding support of K1-MET GmbH, whose research program is supported by COMET (Competence Center for Excellent Technologies), the Austrian program for competence centers. COMET is funded by the Austrian ministries BMK and BMDW, the provinces of Upper Austria, Tyrol, and Styria, and the Styrian Business Promotion Agency (SFG).</p>
FT7 Anonymous (8) 3-key fagottino: measurements, photos, endoscopic video
<p>Dataset of FT7 Anonymous (8) 3-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video. Note: Better quality photos will be available at a later date.</p> <p> </p>
Video demonstrating dynamic calibration procedure
<p>Video recording shows dynamic calibration procedure, developed in the frameworks of ComTraForce project. The calibration was performed at the facilities of RISE Research Institutes of Sweden.</p>
Video demonstrating continuous calibration procedure
<p>Video recording shows continuous calibration procedure, developed in the frameworks of ComTraForce project. Authors would like to acknowledge SincoTec Test Systems GmbH for providing calibration facilities and explicitly Head of Measurement Department Mike Udhe for providing technical support.</p>
US National Native Bee Monitoring RCN Data Management Workshop: Public Domain Videos
<p>The US National Native Bee Monitoring Research Coordination Network (RCN) held a two-day workshop on data management best practices for native bee inventory, survey, and monitoring data on March 28 and 30, 2023. Videos in this data set were played at the workshop. These videos are released into the public domain. This data set includes the following videos:</p> <ul> <li>Ecological Metadata Standards to Enable Data Reuse by Julien Brun</li> <li>Useful Photo Management for Bee Species by Sam Droege</li> <li>Trait Data Models and Vocabulary by Jen Hammock</li> <li>Symbiota: open-source community portals for insect data management by Andrew Johnston</li> <li>Moving data from the field to the world by Jonathan Koch</li> <li>Exploring data using Discover Life by Clare Maffei</li> <li>USDA Data Sharing Policies and Opportunities by Cynthia Sims Parr</li> <li>Why Share Species Interaction Data? by Jorrit H. Poelen</li> <li>Big-Bee: Sharing Bee Interactions & Traits by Katja C. Seltmann</li> <li>Let’s talk about data by Katja C. Seltmann</li> <li>Responsible use of museum specimens & their data by Erika M. Tucker</li> </ul>
US National Native Bee Monitoring RCN Data Management Workshop: CC BY Videos
<p>The US National Native Bee Monitoring Research Coordination Network (RCN) held a two-day workshop on data management best practices for native bee inventory, survey, and monitoring data on March 28 and 30, 2023. Videos in this data set were played at the workshop. These videos are released with a CC BY license. Please cite the presenter(s) of the video(s) you use. This data set includes the following videos:</p> <ul> <li>The ABeeCs of Data Attribution: Please use your magic words by David Bloom</li> <li>Darwin Core Geography: How to make your locality data complete and accurate by David Bloom</li> <li>Best Practices for Managing Native Bee Molecular Data by Michael G. Branstetter</li> <li>A Trait Database for Bees by Elizabeth A. Crisfield</li> <li>Biotic interaction data and invasive species assessment by Quentin Groom</li> <li>OpenTraits Network (OTN) & TRY Plant Trait Database by Jens Kattge</li> <li>Semantics modeling of phenotypic trait data with ontologies by Diego S. Porto</li> <li>WorldFAIR: towards making plant-pollinator data FAIR by Maarten Trekels</li> </ul>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 10 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 10 most numerous video from the UCF101 dataset. For the top 5 version, see: <a href="https://doi.org/10.5281/zenodo.7924745">https://doi.org/10.5281/zenodo.7924745</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7882861/files/ucf101_top10.tar.gz -O ucf101_top10.tar.gz tar xf ucf101_top10.tar.gz</code></pre> <p> </p>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 5 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 5 most numerous video from the UCF101 dataset. For the top 10 version see: <a href="https://doi.org/10.5281/zenodo.7882861">https://doi.org/10.5281/zenodo.7882861</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7924745/files/ucf101_top5.tar.gz -O ucf101_top5.tar.gz tar xf ucf101_top5.tar.gz</code></pre>
Data set and classification method for low quality web traffic identification in video marketing campaigns
<p>Final outcomes of the InPreVi (AI4Media) project developed in 2022. </p> <p>1. Data set describing the statistics of the video ad marketing campaigns</p> <p>2. Script for web traffic classification</p>
Compilation of AI4DI Demonstrator Videos
<p>This video presents the demonstrators of the project's AI4DI technologies, across the different industry domains</p>
ROADMAP Technical Abstract Video: The Living Labs
<p>The Living Labs are one of the pillars of the ROADMAP project, which is focused on the responsible use and reduction of antimicrobials in livestock production. In this video, you will have the chance to get to know better how a Living Lab works, its importance in the process of getting awareness of AMU (antimicrobial use) and how tools are given to farmers in order to implement good practices, recommendations and acknowledging other ways of improving animal health and welfare.</p> <p>Watch the video here: https://youtu.be/afXe1eHX2lM</p>
Single Particle Investigation of Triolein Digestion using Optical Manipulation, Polarized Video Microscopy, and SAXS
<p>Hypothesis: Understanding how soft colloids, such as food emulsion droplets, transform based on their environment is critical for various applications, including drug and nutrient delivery and biotechnology. However, the mechanisms behind colloidal transformations within individual oil droplets still need to be better understood.</p> <p>Experiments: This study employs optical micromanipulation with microfluidics and polarized optical video microscopy to investigate the pancreatic lipase- and pH-triggered colloidal transformations in a single triolein droplet. Small-angle X-ray scattering (SAXS) provides complementary statistical insights and allows for detailed structural assignment.</p> <p>Findings: Optical video microscopy recorded the transformation of individual triolein emulsion droplets, with the smooth surface of these spherical particles becoming rough and the entire volume eventually being affected. The polarized microscopy revealed the coexistence of at least two distinct structures in a single particle during digestion, with their ratio and distribution altered by pH. The SAXS analysis assigned the optical anisotropy to emulsified inverse hexagonal- and multilamellar phases, coexisting with isotropic structures such as the micellar cubic phase. These results can help understand the liquid-liquid crystalline phase transformations inside an emulsion droplet and guide the design of advanced food emulsions.</p>
Thinking about Vector Symbolic Architectures (video recording)
<p>Video recording of the keynote presentation "Thinking about Vector Symbolic Architectures" given on 2023-06-15 at the <a href="https://sites.google.com/ltu.se/midnightvsa/home?authuser=0">Midnight Sun Workshop on Vector Symbolic Architectures</a> in Luleå, Sweden.</p> <p><strong>Abstract</strong></p> <p>Vector Symbolic Architectures are defined in terms of a very small set of operators acting on a vector space. The task of the VSA researcher is to discover the implications that follow from the definition in terms of the systems that can be implemented with VSAs. The VSA definitions are the researcher’s raw materials, but they also need tools to transform those raw materials into useful hypotheses and system designs. One important tool for a researcher is a conceptual framework, which specifies how the researcher thinks about VSAs and relates them to the other things they know. It is the researcher’s mental model of how VSAs work. The primary requirement for a conceptual framework is that it is productive; it should make it easy for the researcher to generate interesting hypotheses and designs. These hypotheses and designs don’t have to be correct, just plausible. Beating them into shape is a different part of the research process. Most VSA research papers contain a statement of the VSA definition. Very few mention the researcher’s conceptual framework. In this talk I will sketch out my conceptual framework - how I think about Vector Symbolic Architectures - in the hope that it might be interesting and useful to other researchers.</p>
Deeptangle Dataset: Labelled Experimental and Synthetic Videos of Swimming and Overlapping C. elegans worms
<p>This repository contains the dataset employed in the paper <a href="https://arxiv.org/abs/2301.04460">Fast spline detection in high density microscopy data</a>.</p> <p>Three files are provided:</p> <p>1. <em>videos.zip</em>: raw experimental videos.<br> 2. <em>labeled_data.zip</em>: labelled sections of experimental videos used for evaluation<br> 3. <em>syntehthic_dataset.zip</em>: synthetic dataset used for training</p> <p><strong>Labelled data</strong></p> <p>Sections are named as VIDEONAME_FRAMENUMBER_SECTIONID.<br> All labels are in labels.json and correspond to the middle frame of the clip (05.png).<br> Example plotting script is provided (<em>plot_data.py</em>).</p> <p><strong>Synthetic data</strong></p> <p>Clips are named as NUMBEROFWORMS_ID.<br> Labels (for all frames) are stored in labels.npy.<br> Example plotting script is provided (plot_data.py).</p> <p> </p> <p><strong>Related</strong></p> <p>Paper: <a href="https://arxiv.org/abs/2301.04460">https://arxiv.org/abs/2301.04460</a></p> <p>Deeptangle code: <a href="https://github.com/kirkegaardlab/deeptangle">https://github.com/kirkegaardlab/deeptangle</a></p> <p>Labelling tool: <a href="https://github.com/kirkegaardlab/deeptanglelabel">https://github.com/kirkegaardlab/deeptanglelabel</a></p> <p>---</p> <p>If used, please cite</p> <p><em>Albert Alonso & Julius B. Kirkegaard. Fast spline detection in high density microscopy data. 2023.</em></p>
Analysis of interventions of practice abstracts and videos
<p>This dataset containts the underlying data that was used to analyse the SmartCulTour identified interventions that were described under the production of abstracts and practice videos of D6.2. The data collection established:</p> <ul> <li>Context & background</li> <li>Reason why of the intervention</li> <li>Resources and tools</li> <li>Expected economic impact</li> <li>Expected social impact</li> <li>Expected cultural impact</li> <li>Expected environmental impact</li> <li>Success conditions</li> </ul> <p>These criteria were analysed by the SmartCulTour local experts and meant to feed into the updated taxonomy on cultural tourism interventions of D3.4. The interventions that are described are linked to the practice videos that can be found under the Living Labs section of the SmartCulTour website (<a href="http://www.smartcultour.eu">www.smartcultour.eu</a>) and pertain to:</p> <ul> <li>Rotterdam Living Lab: Planning for the future of Hoek van Holland & Bospolder-Tussendijken</li> <li>Scheldeland Living Lab: Hof van Coolhem: social employment and care project in tourism</li> <li>Scheldeland Living Lab: Bornem Castle: upgrades historical exhibitions & creates visitor centre</li> <li>Scheldeland Living Lab: Steam train Dendermonde-Puurs: volunteers protecting industrial heritage</li> <li>Utsjoki Living Lab: Traces in Utsjoki: inspiring respectful visitor behaviour in nature areas</li> <li>Utsjoki Living Lab: Placemaking as a technique to support meaningful visitor experiences</li> <li>Huesca Living Lab: The Somontano Wine Route: a resilient strategy for Huesca</li> <li>Huesca Living Lab: The Río Vero Cultural Park. From Palaeolithic human history to the present</li> <li>Split Living Lab: Making traditional Easter bread-Sirnica in Solin</li> <li>Split Living Lab: The cultural heritage of Sinj: the story of Alka</li> <li>Vicenza Living Lab: Vicenza: the city of Palladio</li> <li>Vicenza Living Lab: The international library "La Vigna" becomes an open innovation Living Lab</li> </ul>
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.