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193 results for “labeled data”
Data from: Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning
<div> <div> <p>Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized.</p> <p>In the paper "Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning", we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event data. By taking advantage of the intrinsic time correlations, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model. The proposed method is validated on <strong>two experimental datasets</strong> based on silicon photomultipliers (SiPM) as main transducers:</p> <ol> <li>In the toy experiment, we collect data from a pair of SiPM sensors from a common laser source. The neural network model achieves the single-channel time resolution of 8.8 ps and exhibits robustness against concept drift in the dataset. </li> <li>In the electromagnetic calorimeter experiment, we collect data from an eight-channel calorimeter module. Several neural network models (Fully-Connected, Convolutional Neural Network and Long Short Term Memory) are tested to show their conformance to the underlying physical constraint and to judge their performance against traditional methods. </li> </ol> <p>In total, the proposed method works well in either ideal or noisy experimental condition and recovers the time information from waveform samples successfully and precisely. <strong>The dataset in this repository serves as a basis for similar researches on timing performance of SiPM-based nuclear detectors, and on application of neural networks to typical signals of nuclear radiation detectors.</strong></p> </div> </div>
Dead Sea Scrolls data collection (images, labels, prediction plots) for dating ancient manuscripts using radiocarbon and AI-based writing style analysis
<p>The dataset is associated with the following article:<br>Title: <strong>Dating ancient manuscripts using radiocarbon and AI-based writing style analysis</strong><br>Authors: Mladen Popović, Maruf A. Dhali, Lambert Schomaker, Johannes van der Plicht, Kaare Lund Rasmussen, Jacopo La Nasa, Ilaria Degano, Maria Perla Colombini, and Eibert Tigchelaar<br><em>(Under review)</em></p> <p>This data set is collected for the ERC project:<br>The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls<br>PI: Mladen Popović<br>Grant agreement ID: 640497<br>Project website: <a href="https://cordis.europa.eu/project/id/640497">https://cordis.europa.eu/project/id/640497</a></p> <p> </p> <p><strong>Copyright (c) </strong> University of Groningen, 2024. All rights reserved.<br><strong>Disclaimer and copyright notice for all data contained on the *.tar.gz files:</strong></p> <p><strong>1)</strong> permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes.</p> <p><strong>2) </strong>provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed.</p> <p><strong>3) </strong>provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data.</p> <p><strong>4) </strong>the user should refer to the first public article mentioned above on this data set.</p> <p><strong>5) </strong>the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site to obtain their own copy.</p> <p> </p> <p><strong>Organization of the data:<br></strong><em>(Update on 19 April 2024: OxCal data for accepted 2-sigma ranges are updated with the incusion and exclusion of minor peaks. New prediction plots are added after the model is trained with accepted 2-sigma ranges, including minor peaks. The old plots are also kept. <br><br><OLD Updates below; disregard><br>updated on 07-Feb-2024: OxCal data for selected ranges added in a new directory in addition to previously available original OxCal data. Enoch's prediction plots and test images are reorganized for easy access to the users.<br><OLD Updates above; disregard><br><br>Please use the files from this version and disregard the previous two versions: 10.5281/zenodo.10629480 and 10.5281/zenodo.8168210)</em></p> <p>There are four *.tar.gz files:</p> <p><em><strong>C14-Oxcal-data-updated.tar.gz</strong></em> contains one directory with radiocarbon data (OxCal [1] raw data) for all 30 manuscripts. Three additional directories contain name-corrected files for original OxCal data, files with accepted ranges, and files with accepted ranges including minor peaks. Please refer to the original article for details about OxCal data and the manuscripts. 25 out of 30 raw OxCal data are used (accepted ranges only) as the training labels during the training of Enoch, the date prediction model.</p> <p><em><strong>train-images-c14.tar.gz</strong></em> contains the clean and preprocessed (binarized, aligned, and arrangement corrected) training images for the 25 radiocarbon-dated training manuscripts (including 4Q52; 64 images in total). </p> <p><em><strong>test-images-all.tar.gz</strong></em> contains the clean and preprocessed test images for 135 previously undated manuscripts. The images are organized in three different directories: the first one with all 359 images for the 135 manuscripts, the second one with the selected 135 images, and the final one with 25 images to illustrate the poor quality of images. </p> <p><em><strong>Enoch-prediction-new-with-minor-peaks.tar.gz</strong></em> contains the new date prediction plots for each of the 135 test images, where Enoch was trained with the inclusion of minor peaks for the 2-sigma accepted ranges and with a data balancing threshold of 0.05. These plots are used by expert palaeographers' evaluation of Enoch's style-based date predictions of 135 previously undated manuscripts.</p> <p><em><strong>Enoch-predictions.tar.gz</strong></em> contains the date prediction plots for each of the 135 test images. There are two directories inside the *.tar.gz file:<br><br>- <em>prediction-plots-for-selected-135:</em> Prediction plots with data balancing threshold of 0.05. <br>- <em>extra-plots:</em> contains four additional directories:<br> - <em>Enoch-predictions-c14wo4Q52-balanced05:</em> Prediction plots with data balancing threshold of 0.05. <br> - <em>Enoch-predictions-c14wo4Q52-balanced10:</em> Prediction plots with data balancing threshold of 0.1.<br> - <em>Enoch-predictions-c14wo4Q52-unbalanced:</em> Unbalanced raw predictions.<br> - <em>Enoch-predictions-c14wo4Q52-combined:</em> Combined plots with all three prediction plots (unbalanced, 0.05, 0.1).<br>Please refer to the original article for more details.</p> <p>The updated code to run the plot is available here: <a href="https://doi.org/10.5281/zenodo.10998860">https://doi.org/10.5281/zenodo.10998860</a></p> <p><strong>If you have any questions, please get in touch with us:</strong><br>Mladen Popović <m.popovic(at)rug.nl><br>Maruf A. Dhali <m.a.dhali(at)rug.nl><br>Lambert Schomaker <l.r.b.schomaker(at)rug.nl></p> <p> </p> <p><strong>References:</strong><br>1. Bronk Ramsey, C. (2001). Development of the radiocarbon calibration program. <em>Radiocarbon</em>, <em>43</em>(2A), 355-363.</p>
Data: Computed tomography lacks sensitivity to image gold labelled mesenchymal stromal cells in vivo as evidenced by multispectral optoacoustic tomography.
<p>This data set includes all the raw data collected for the following article: "Computed tomography lacks sensitivity to image gold labelled mesenchymal stromal cells in vivo as evidenced by multispectral optoacoustic tomography."</p>
YOGData: Labelled data (YOLO and Mask R-CNN) for yogurt cup identification within production lines
<p><strong>D</strong><strong>ata abstract:</strong><br> The YogDATA dataset contains images from an industrial laboratory production line when it is functioned to quality yogurts. The case-study for the recognition of yogurt cups requires training of Mask R-CNN and YOLO v5.0 models with a set of corresponding images. Thus, it is important to collect the corresponding images to train and evaluate the class. Specifically, the YogDATA dataset includes the same labeled data for Mask R-CNN (coco format) and YOLO models. For the YOLO architecture, training and validation datsets include sets of images in jpg format and their annotations in txt file format. For the Mask R-CNN architecture, the annotation of the same sets of images are included in json file format (80% of images and annotations of each subset are in training set and 20% of images of each subset are in test set.) <br> </p> <p><strong>Paper abstract:</strong><br> The explosion of the digitisation of the traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. It is crucial to leverage modern automation tools and through-engineering solutions to increase their efficiency and continuously meet challenging standards. Towards this end, in this work, an intelligent algorithm based on machine vision and artificial intelligence, which identifies dairy products within production lines, is presented. Furthermore, in order to train and validate the model, the YogDATA dataset was created that includes yogurt cups within a production line. Specifically, we evaluate two deep learning models (Mask R-CNN and YOLO v5.0) to recognise and detect each yogurt cup in a production line, in order to automate the packaging processes of the products. According to our results, the performance precision of the two models is similar, estimating its at 99\%. </p> <p> </p>
Labeled data and models for COVID-19 vaccine related tweets with stance, location, and topics
<p>The dataset contains Tweet IDs along with the location and tweet timestamp. The tweets are labeled based on motivating/demotivating status, stance towards the COVID-19 vaccine, and topic in the tweet text. To comply with Twitter guidelines, we removed the tweet texts and author information. You can use Hydrator API to hydrate the tweets.</p> <p>The repository also contains the machine-learning models for topic modeling, de/motivation classifier, and stance detection from the tweets.</p>
Habitats labelled data
<p><span>This dataset has been created within the EU H2020 Natural Intelligence project (ID </span><span> 101016970</span><span>).</span></p> <p><span>The dataset contains labeled pictures of typical and early warning species of four different habitats, divided into four subfolders. </span></p> <p><span>Each subfolder contains:</span></p> <p><span>1) species data images (.jpg files)</span></p> <p><span>2) .txt files for each image containing the labelling information. The first value indicates the species (defined in the .yaml file), the remaining values describe the box vertices.</span></p> <p><span>3) a single .yaml file containing the parameter used in the labelling and detection AI training code:</span></p> <p><span> - path to the dataset root directory</span></p> <p><span> - path to the the train images directory</span></p> <p><span> - path to the validation images directory</span></p> <p><span> - path to the test images directory</span></p> <p><span> - number of classes (species)</span></p> <p><span> </span></p> <p><span>The four subfolders are named after the four habitats, i.e.,</span></p> <p><span>- </span><span>Dunes. This refers to EU habitats 2110 and 2120 in Italian dunes. The included species are:</span></p> <p><span> o </span><span>Achillea maritima – typical species</span></p> <p><span> o </span><span>Calamagrotis arenaria – typical species</span></p> <p><span> o </span><span>Carpobrotus acinaciformis – invasive species</span></p> <p><span> o </span><span>Eryngium maritimum – typical species</span></p> <p><span> o </span><span>Pancratium maritimum – typical species</span></p> <p><span> o </span><span>Thinopyrum junceum – typical species</span></p> <p><span>- </span><span>Grasslands. This refers to EU habitat 6210* in the Italian Central Apennines. The included species are:</span></p> <p><span> o </span><span>Asphodelus macrocarpus – early warning species</span></p> <p><span> o </span><span>Dactylorhiza sambucina – typical species</span></p> <p><span> o </span><span>Orchis morio – typical species</span></p> <p><span>- </span><span>Forests. This refers to EU habitat 9210* in the Italian Apennines. The included species are:</span></p> <p><span> o </span><span>Anemonoides nemorosa – typical species</span></p> <p><span> o </span><span>Corydalis cava – typical species</span></p> <p><span> o </span><span>Doronicum columnae – early warning species</span></p> <p><span> o </span><span>Anemonoides ranunculoides – typical species</span></p> <p><span>- </span><span>Screes. This refers to EU habitat 8110 and 8120 in the Italian Alps. The included species are:</span></p> <p><span> o </span>Cerastium sp.<span> – typical species</span></p> <p><span> o </span><span>Luzula alpino-pilosa – early warning species</span></p> <p><span> o </span><span>Saxifraga – typical species</span></p> <p><span> o </span><span>Ranunculus glacialis – typical species</span></p> <p><span> o </span><span>Geum reptans – typical species</span></p> <p><span> o </span><span>Papaver alpinum – typical species</span></p> <p><span> </span></p> <p><span>Researchers from a variety of disciplines can benefit from using this dataset because of its multidisciplinary scope. Botanists could evaluate the accuracy of this data as well as the habitat's conditions using the plant images that the robot captured, robotic engineers could test their AI algorithms for identifying and classifying different species using these data. The code used to label the images can be found here: <a href="https://github.com/ivangrov/ModifiedOpenLabelling">https://github.com/ivangrov/ModifiedOpenLabelling</a></span></p> <p><span> </span></p>
Assessment of Fair Trade education programs in France: data 2022 from the control group and those from the two experimental fields (the Fair Generation scheme and the Fair Trade Universities Label)
<p><span>see the technical report (period 2019-2021) on researchgate:</span></p> <p><span><a href="https://www.researchgate.net/publication/380823820_Evaluation_of_the_fair-trade_education_programs_Results_of_the_first_phase_of_the_Fair_Future_program">(PDF) Evaluation of the fair-trade education programs. Results of the first phase of the Fair Future program (researchgate.net)</a></span></p>
Assessment of Fair Trade education programs in France: data 2021 from the control group and those from the two experimental fields (the Fair Generation scheme and the Fair Trade Universities Label)
<p><span>see the technical report (period 2019-2021) on researchgate:</span></p> <p><span><a href="https://www.researchgate.net/publication/380823820_Evaluation_of_the_fair-trade_education_programs_Results_of_the_first_phase_of_the_Fair_Future_program">(PDF) Evaluation of the fair-trade education programs. Results of the first phase of the Fair Future program (researchgate.net)</a></span></p>
Рис. 1. Pararctia lapponica lemniscata (Stichel, 1911): 1–4 — имаго (1, 2 — самцы; 3, 4 — самки). Δанные сбора имаго: 1 — Буреинский заповеΑник, верховье р. Правая Бурея, 4 км В корΑона «Новый МеΑвежий», 1400 м наΑ уровнем моря, 24.06.2014; 2 — Буреинский заповеΑник, верховье р. Правая Бурея, окрестности корΑона «Новый МеΑвежий», 900 м наΑ уровнем моря; 4.07.2016; 3, 4 — там же, 29–30.06.2018 Fig. 1. Pararctia lapponica lemniscata (Stichel, 1911): 1 – 4 – adults (1, 2 – males; 3, 4 – females). Data labels for imago: 1 – Bureinsky Nature Reserve, upper reach of Pravaya Bureya River, 4 km E Novyi Medvezhii cordon, 1400 m above sea level, 24.06.2014; 2 – Bureinsky Nature Reserve, upper reach of Pravaya Bureya River, near Novyi Medvezhii cordon, 900 m above sea level, 4.07.2016; 3, 4 – at the same place, 29–30.06.2018 in On The Biology Of (Stichel, 1911) (Lepidoptera, Erebidae, Arctiinae) In Northern Amur Region
Рис. 1. Pararctia lapponica lemniscata (Stichel, 1911): 1–4 — имаго (1, 2 — самцы; 3, 4 — самки). Δанные сбора имаго: 1 — Буреинский заповеΑник, верховье р. Правая Бурея, 4 км В корΑона «Новый МеΑвежий», 1400 м наΑ уровнем моря, 24.06.2014; 2 — Буреинский заповеΑник, верховье р. Правая Бурея, окрестности корΑона «Новый МеΑвежий», 900 м наΑ уровнем моря; 4.07.2016; 3, 4 — там же, 29–30.06.2018 Fig. 1. Pararctia lapponica lemniscata (Stichel, 1911): 1 – 4 – adults (1, 2 – males; 3, 4 – females). Data labels for imago: 1 – Bureinsky Nature Reserve, upper reach of Pravaya Bureya River, 4 km E Novyi Medvezhii cordon, 1400 m above sea level, 24.06.2014; 2 – Bureinsky Nature Reserve, upper reach of Pravaya Bureya River, near Novyi Medvezhii cordon, 900 m above sea level, 4.07.2016; 3, 4 – at the same place, 29–30.06.2018
Рис. 3. Platarctia ornata: 1–4 — имаго, виΑ сверху (1, 2 — самцы; 3, 4 — самки); 5–10 — гусеницы сеΑьмого возраста (5, 6 — форма с черными и рыжими воΛосками; 7, 8 — форма с рыжими воΛосками; 9, 10 — форма с черными воΛосками); 11 — кокон; 12–14 — кукоΛка; 15 — неΑавно отроΑившийся самец. 5, 7, 9, 13 — виΑ сбоку; 6, 8, 10, 14 — виΑ сверху; 12 — виΑ снизу. Δанные сбора имаго: 1 — Буреинский заповеΑник, 4 км В корΑона «Новый МеΑвежий», 1400 м наΑ ур. м., 24.06.2014; 2, 3 — Буреинский заповеΑник, корΑон «Новый МеΑвежий», ex pupa 12–13.09.2018; 4 — там же, 4.07.2018 Fig. 3. Platarctia ornata: 1–4 — imago, dorsal view (1, 2 — males; 3, 4 — females); 5–10 — seventh instar larvae (5, 6 — with black and red hairs; 7, 8 — with red hairs; 9, 10 — with black hairs only); 11 — cocoon; 12–14 — pupа; 15 — newly emerged male. 5, 7, 9, 13 — lateral view; 6, 8, 10, 14 — dorsal view; 12 — ventral view. Data labels for imago insects: 1 — Bureinsky State Nature Reserve, 4 km E Novyi Medvezhii cordon, 1400 m above sea level, 24.06.2014; 2, 3 — Bureinsky State Nature Reserve, Novyi Medvezhii cordon, ex pupa 12–13.09.2018; 4 — same location, 4.07.2018 in Moths (Lepidoptera, Macroheterocera, Excluding Geometridae And Noctuidae S.L.) Of The Bureinsky State Nature Reserve And Adjacent Territories (Khabarovsk Krai, Russia)
Рис. 3. Platarctia ornata: 1–4 — имаго, виΑ сверху (1, 2 — самцы; 3, 4 — самки); 5–10 — гусеницы сеΑьмого возраста (5, 6 — форма с черными и рыжими воΛосками; 7, 8 — форма с рыжими воΛосками; 9, 10 — форма с черными воΛосками); 11 — кокон; 12–14 — кукоΛка; 15 — неΑавно отроΑившийся самец. 5, 7, 9, 13 — виΑ сбоку; 6, 8, 10, 14 — виΑ сверху; 12 — виΑ снизу. Δанные сбора имаго: 1 — Буреинский заповеΑник, 4 км В корΑона «Новый МеΑвежий», 1400 м наΑ ур. м., 24.06.2014; 2, 3 — Буреинский заповеΑник, корΑон «Новый МеΑвежий», ex pupa 12–13.09.2018; 4 — там же, 4.07.2018 Fig. 3. Platarctia ornata: 1–4 — imago, dorsal view (1, 2 — males; 3, 4 — females); 5–10 — seventh instar larvae (5, 6 — with black and red hairs; 7, 8 — with red hairs; 9, 10 — with black hairs only); 11 — cocoon; 12–14 — pupа; 15 — newly emerged male. 5, 7, 9, 13 — lateral view; 6, 8, 10, 14 — dorsal view; 12 — ventral view. Data labels for imago insects: 1 — Bureinsky State Nature Reserve, 4 km E Novyi Medvezhii cordon, 1400 m above sea level, 24.06.2014; 2, 3 — Bureinsky State Nature Reserve, Novyi Medvezhii cordon, ex pupa 12–13.09.2018; 4 — same location, 4.07.2018
Рис. 4. Grammia quenseli liturata: 1–4 — имаго (1, 2 — самцы, 3, 4 — самки); 5–7 — гусеницы сеΑьмого возраста (5, 6 — форма с ΑорсаΛьной Λинией, 7 — форма без ΑорсаΛьной Λинии); 8–10 — кукоΛка; 11, 12 — кремастер кукоΛки. 1–4, 5, 7, 10 — виΑ сверху; 6, 9, 12 — виΑ сбоку; 8, 11 — виΑ снизу. Δанные сбора имаго: 1, 3 — Буреинский заповеΑник, окрестности корΑона «Новый МеΑвежий», 5–6.07.2018; 2, 4 — там же, ex pupa 23–30.08.2018 Fig. 4. Grammia quenseli liturata: 1–4 — imago (1, 2 — males, 3, 4 — females); 5–7 — seventh instar larvae (5, 6 — with white dorsal line, 7 — without dorsal line); 8–10 — pupa; 11, 12 — cremaster. 1–4, 5, 7, 10 — dorsal view; 6, 9, 12 — lateral view; 8, 11 — ventral view. Data labels for imago: 1, 3 — Bureinsky State Nature Reserve, near Novyi Medvezhii cordon, 5–6.07.2018; 2, 4 — same location, 23–30.08.2018 in Moths (Lepidoptera, Macroheterocera, Excluding Geometridae And Noctuidae S.L.) Of The Bureinsky State Nature Reserve And Adjacent Territories (Khabarovsk Krai, Russia)
Рис. 4. Grammia quenseli liturata: 1–4 — имаго (1, 2 — самцы, 3, 4 — самки); 5–7 — гусеницы сеΑьмого возраста (5, 6 — форма с ΑорсаΛьной Λинией, 7 — форма без ΑорсаΛьной Λинии); 8–10 — кукоΛка; 11, 12 — кремастер кукоΛки. 1–4, 5, 7, 10 — виΑ сверху; 6, 9, 12 — виΑ сбоку; 8, 11 — виΑ снизу. Δанные сбора имаго: 1, 3 — Буреинский заповеΑник, окрестности корΑона «Новый МеΑвежий», 5–6.07.2018; 2, 4 — там же, ex pupa 23–30.08.2018 Fig. 4. Grammia quenseli liturata: 1–4 — imago (1, 2 — males, 3, 4 — females); 5–7 — seventh instar larvae (5, 6 — with white dorsal line, 7 — without dorsal line); 8–10 — pupa; 11, 12 — cremaster. 1–4, 5, 7, 10 — dorsal view; 6, 9, 12 — lateral view; 8, 11 — ventral view. Data labels for imago: 1, 3 — Bureinsky State Nature Reserve, near Novyi Medvezhii cordon, 5–6.07.2018; 2, 4 — same location, 23–30.08.2018
Data: In vivo fate of free and encapsulated iron oxide nanoparticles after injection of labelled stem cells
<p>This data set is composed of magnetic resonance images (MRI) that are supporting the article entitled <em>In vivo fate of free and encapsulated iron oxide nanoparticles after injection of labelled stem cells </em>by the same authors. Nanoparticle contrast agents are used to label stem cells and monitor their bio-distribution in pre-clinical models of disease. Due to the impact on the interpretation of imaging results, understanding the <em>in vivo</em> fate of the particles is important. The bio-distribution after intra-cardiac injection of labelled cells with superparamagnetic iron oxide nanoparticles was monitored longitudinally by MRI. </p>
Environmental issues of software & its labelling: questionnaire and survey data
<p>Questionnaire (original one in German, translated into English) and the survey data of my survey on environmental issues of software and its labelling, conducted in August to October 2016 (doctoral studies).</p>
Logistics Transport Label Data - 'Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts'
<p>Example dataset described in ICMLA2019 Paper 'Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts' (Dörr, Brandt, Meyer, Pouls).</p>
Tobii Pro Spectrum hand-labelled data set
<p>For recording this data set, the Tobii Pro Lab software and a Tobii Pro Spectrum eye tracking device with sampling frequencies up to 600 Hz were used. The provided monitor had a size of 23.8 inches and a 16:9 aspect ratio. The exact procedure for generating this data is described in (1).<br>Three recordings at 300 Hz and three recordings at 600 Hz are extracted from the data in (1). From this data, time spans of about 20 seconds are cut out and the fixations and saccades are labelled by hand. <br><br>(1) Timur Ezer, Matthias Greiner, Lisa Grabinger, Florian Hauser, and Jürgen Mottok. Eye tracking as technology in education: Data quality analysis and improvements. In ICERI2023 Proceedings, 16th annual International Conference of Education, Research and Innovation, pages 4500–4509, Valencia, Spain, 13-15 November, 2023 2023. IATED. ISBN 978-84-09-55942-8. doi: 10.21125/iceri.2023.1127. URL https://doi.org/10.21125/iceri.2023.1127 </p>
Original data sets of a DEER/PELDOR ring test of four doubly spin-labelled mutants of the protein YopO
<p>The dataset is discussed in manuscript "Benchmark test and guidelines for DEER/PELDOR experiments on nitroxide-labeled biomolecules" by Olav Schiemann, Caspar A. Heubach, Dinar Abdullin, Katrin Ackermann, Mykhailo Azarkh, Elena Bagryanskaya, Malte Drescher, Burkhard Endeward, Jack H. Freed, Laura Galazzo, Daniella Goldfarb, Tobias Hett, Laura Esteban Hofer, Luis Fábregas Ibáñez, Eric J. Hustedt, Svetlana Kucher, Ilya Kuprov, Janet E. Lovett, Andreas Meyer, Sharon Ruthstein, Sunil Saxena, Stefan Stoll, Christiane Timmel, Marilena Di Valentin, Hassane S. Mchaourab, Thomas F. Prisner, Bela E. Bode, Enrica Bordignon, Marina Bennati, Gunnar Jeschke. It was generated in a ring test by seven laboratories. The authors names of individual data are not assigned on purpose, rather data sets are referred to by laboratory identifiers A, B, C, D, E, F, G. The Supplementary Information for the above mentioned manuscript, which contains a link to this dataset, describes how the samples were prepared and how the measurements were performed.</p>
Hourly detections of echolocation clicks in Hawaiian Island HARP data from Hawai`i, Kaua`i, and Manawai with species labels
<p>This dataset consists of counts of detections of echolocation clicks at three sites in the Hawaiian Islands Archipelago. These sites are Hawaii, Kauai, and Manawai (also known as Pearl and Hermes Reef). Echolocation clicks have been labeled using a neural network classifier that was trained and tested on data from the Hawaiian Islands and can successfully identify many species of regionally present odontocetes. During the labeling process, clicks were grouped into one-minute bins and each bin was given a species' label. The data provided here is further binned at an hourly level, where counts of a given species represent the number of one-minute bins within a given hour that were labeled as that species (up to a maximum of 60). One file is provided per site, and files are in .csv format that can be read using any desired coding language.<span> </span></p>
The impact of pharmaceutical form and simulated side effects in an open-label-placebo RCT for improving psychological distress in highly stressed students (Open Data and Open Materials)
<p> Open-label placebo (OLP) may be utilized to reduce psychological distress. Yet, potential contextual effects have not been explored. We investigated the impact of pharmaceutical form and the simulation of side effects in a parallel group RCT (DRKS00030987). A sample of 177 highly stressed university students at risk of depression were randomly assigned by computer generated tables to a one-week intervention with active or passive OLP nasal spray or passive OLP capsule or a no-treatment control group. After the intervention, groups differed significantly in depressive symptoms but not regarding other outcomes of psychological distress (stress, anxiety, sleep quality, somatization), well-being or treatment expectation. OLP groups benefitted significantly more compared to the no-treatment control group (<em>d</em>=.40), OLP nasal spray groups significantly more than the OLP capsule group (<em>d</em>=.40) and the active OLP group significantly more than the passive OLP groups (<em>d</em>=.42). Interestingly, before intervention, most participants, regardless of group assignment, believed that the OLP capsule would be most beneficial. The effectiveness of OLP treatments seems to be highly influenced by the symptom focus conveyed by the OLP rationale. Moreover, pharmaceutical form and simulation of side effects may modulate efficacy, while explicit treatment expectation seems to play a minor role.</p>
Data for ms. Dairy cattle welfare – the relative effect of legislation, industry standards and labelled niche production in five European countries
<p>Repository R 1: Scores on dimension values and weight on dimension from 38 international experts. </p> <p>Repository R2: Country Benchmark scores from 38 international experts.</p>
Figure 2 in Lost and found in Ireland; how a data label resulted in a postal delivery to Metriocnemus (Inermipupa) carmencitabertarum (Orthocladiinae)
Figure 2. Back of envelope from the Northern Ireland Royal Mail Centre (N.I.M.C.) also showing the Irish "An Post" seal.
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.