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A heterometallic [LnLn'Ln] lanthanide complex as a qubit with embedded quantum error correction. Open data set
<p>Includes data relevant for publication with DOI <a href="https://doi.org/10.1021/acsnano.0c03167">10.1039/d0sc03107k</a> plus a table with information on how the data were obtained and processed.</p>
Data sets supporting "Report on EU socio-ecological systems" (MAGIC Deliverable 4.2)
<p>Description: Report on EU socio-ecological systems (Deliverable 4.2; WP4; EU H2020 MAGIC Project)</p> <p>Subject and Keywords: nexus (i.e. food, water, energy, land use, climate and environment); quantitative story telling (QST); metabolic pattern; robustness of narratives; EU’ sustainability strategy</p> <p>Abstract: The sustainability agenda builds on key components (i.e. food, water, energy, land use, climate and environment) that are inherently interconnected in a Nexus. MAGIC uses an innovative approach to test the robustness of narratives about the Nexus in Europe and focuses on the EU sustainable strategy. The aim of this deliverable is to operationalize and test a tool-kit to structure the quantitative analysis of the metabolic pattern of social-ecological systems in relation to their sustainability and the Nexus, at different levels of aggregation and spatial scales (EU, country or regional level). We use “quantitative story telling” as an alternative approach to use scientific information generated and better inform policy-makers. This deliverable build on the methodological approach and the basic features of the theoretical framework of accounting called Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM). The application of the tool-kit in this deliverable has the only goal of illustrating the potentiality of the approach. In a series of chapters we i) present the theoretical background and features of the tool-kit used to characterize the state-of-the-play (diagnostic analysis) with regard to the sustainability of SES; ii) provide an overview of the results obtained in the analysis of 8 EU countries (i.e. France, Germany, Italy, Netherlands, Romania, Spain, Sweden and United Kingdom); iii) demonstrate the capability of the toolkit to perform the analysis at different scales (in this case, at a lower aggregated level -NUTS2-); and iv) highlight the methodological breakthrough provided by relational analysis and its relevance for policy making with regard to the water-energy-food-environment Nexus</p> <p>Publisher: MAGIC</p> <p>Contributors: 18 co-authors from 4 organisations</p> <p>Citation: Ripoll-Bosch and Giampietro (Editors). 2019. Report on EU socio-ecological systems. MAGIC (H2020–GA 689669) Project Deliverable 4.2, Revision</p> <p>Date: March/18</p> <p>Language: English</p> <p>Rights Management: The access is open</p>
Supplementary material (individual data set): Egeler, G.-A. & Baur, P. (2020). Menüwahl in der Hochschulmensa: Fleisch oder Vegi? Ergebnisse eines 12-wöchigen Feldexperiments (NOVANIMAL Working Paper No. 5). ZHAW. https://doi.org/10.21256/zhaw-1405
<p><strong>Meal choice at two university canteens in a field experiment during 12 weeks: anonymised individual menu sales data</strong></p> <p>How do canteen visitors respond to a revised offer of meat-based and plant-based meals? Selected innovations were simultaneously implemented and tested in a trans­disciplinary field experiment in two university canteens over a 12-week period in the autumn semester 2017. Throughout this time, the meat dishes and ‘veg-meals’ (ovo-lacto-vegetarian and vegan meals) were randomly distributed among the three menu lines, the veg-meals were not marketed and advertised as such and the previous vegetarian menu line was abolished. Weeks where the usual number of meat dishes were on offer (the ‘base weeks’) alternated with weeks where the share of veg-meals was increased (the ‘intervention weeks’). </p> <p>The field experiment did not have a negative impact on the number of meals sold or the turnover compared to the two previous years. Women choose meat dishes less often than men. This connection applies in the base weeks and intervention weeks, in all age groups, among both students and among staff. Remarkably, the share of (non-labelled) vegan dishes is comparable for women and men over all age groups, independent of university affiliation (student, staff). Authentic vegan dishes were particularly welcome. Veg-meals could also be sold on the more expensive menu line. There was a better correlation between meal choice, eating habits and attitudes (health, environment, animal welfare, social aspects) than expected. </p> <p><strong>The dataset the dataset contains over <em>21'000</em> anonymised individual menu sales. The analyses and results are summarized in the working paper No. 5 <a href="https://doi.org/10.21256/zhaw-1405">https://doi.org/10.21256/zhaw-1405</a></strong></p> <p>The corresponding scripts are: </p> <p>- <a href="http://doi.org/10.5281/zenodo.4034686">10.5281/zenodo.4034686</a></p> <p>- <a href="http://doi.org/10.5281/zenodo.4034698">10.5281/zenodo.4034698</a></p> <p>- <a href="http://doi.org/10.5281/zenodo.4244258">10.5281/zenodo.4244258</a> (newer Version)</p> <p>For more information visit the <a href="http://novanimal.ch">novanimal.ch</a> website.</p>
Augmentation Via Registration: AutoImplant 2020 Augmented Data Set
<p>Augmented data derived from the AutoImplant 2020 Challenge. Data has been augmented via non-linear SyN registrations using Advanced Normalization Tools (ANTs). This data was used to obtain first place in the AutoImplant 2020 challenge. The AutoImplant 2020 Challenge data was derived from the QC500 dataset from qure.ai. The original dataset is licensed under the CC BY-NC-SA 4.0 license (Attribution-NonCommercial-ShareAlike) and complies with the End User License Agreement (EULA) which are both detailed in the "LICENSE" file in this folder. Please refer to the "LICENSE" file for terms of use.</p> <p>If you use this data, please cite the following paper:</p> <p>Ellis D.G., Aizenberg M.R. (2020) Deep Learning Using Augmentation via Registration: 1st Place Solution to the AutoImplant 2020 Challenge. In: Li J., Egger J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty. AutoImplant 2020. Lecture Notes in Computer Science, vol 12439. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-64327-0_6">https://doi.org/10.1007/978-3-030-64327-0_6</a></p>
Data Set htwddKogRob-InfDynSim for Localization in Highly Crowded Environments
<p>This data set presents a major challenge for robot localization in highly crowded environments. The total distance travelled over all runs is 113.3 km. 50 dynamic obstacles (see htwddKogRob-InfDynSim_dynObstacles.png) were inserted into the map (see htwddKogRob-InfDynSim.png | 1px <span class="math-tex">\(\widehat{=}\)</span> 0.1m).</p> <p>The work was first presented in:</p> <ul> <li>A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization</li> <li>Authors: Frank Bahrmann, Sven Hellbach, Hans-Joachim Böhme</li> <li>Date of Publication: 2016/10/6</li> <li>Conference: Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics</li> <li>Publisher: ACTA Press</li> </ul> <p>Additionally, we present a video with the proposed algorithm and an insight of this dataset under:</p> <ul> <li>youtube.com/AugustDerSmarte</li> <li>https://www.youtube.com/watch?v=26NBFN_XeQg</li> </ul> <p><strong>Instructions for use</strong></p> <p>The zip archives contain ascii files, which hold the log files of the robot observations and robot poses. Since this data set was recorded in a simulated environment, the logfiles include both a changed starting position and a ground-truth pose. For further information, please refer to the header of the logfile. To simplify the parsing of the files, you can use these two Java snippets:</p> <p> </p> <p><strong>Laser Range Measurements:</strong></p> <pre><code class="language-java"> List<Double> ranges = new ArrayList<>(numOfLaserRays); List<Error> errors = new ArrayList<>(numOfLaserRays); String s = line.substring(4); String delimiter = "()"; StringTokenizer tokenizer = new StringTokenizer(s, delimiter); while(tokenizer.hasMoreElements()){ String[] arr = tokenizer.nextToken().split(";"); boolean usable = (arr[0].equals("0")?false:true); double range = Double.parseDouble(arr[1]); ranges.add(range); errors.add(usable?Error.OKAY:Error.INVALID_MEASUREMENT); }</code></pre> <p><strong>Poses:</strong></p> <pre><code class="language-java"> String poseString = line.split(":")[2]; String[] elements = poseString.substring(1, poseString.length()-1).split(";"); double x = Double.parseDouble(elements[0]); double y = Double.parseDouble(elements[1]); double phi = Double.parseDouble(elements[2]);</code></pre> <p> </p>
Appendix: Refactoring Data Set
<p>A large-scale data set containing refactorings mined from 92.800 open-source Java projects enriched with code metrics. Additionally, the data set contains Stable-Instances of the Class-, Method-, Variable- and Field-Level.</p> <p>The source code of the data collection tool can be found on GitHub: https://github.com/refactoring-ai/Data-Collection.<br> </p> <p> </p>
The ALeRCE Light Curve Classifier: labeled set, features, and classifications
<p>Labeled set, features, and classifications of the ZTF alert stream (up to 2020/06/09) presented in the article "Alert Classification for the ALeRCE Broker System: The Light Curve Classifier", Sánchez-Sáez et al., accepted for publication in the Astronomical Journal.</p> <p>The labeled set is used by the ALeRCE broker (<a href="http://alerce.science">alerce.science</a>) to provide updated classifications for sources with new ZTF alerts every day. To obtain updated classifications visit <a href="http://alerce.online">alerce.online</a>. In addition, more examples and instructions on how to use the ALeRCE database and classifications can be found on the <a href="http://alerce.science">alerce.science</a> website. </p> <p> </p> <p>File description:</p> <ul> <li>"labeled_set_lc_classifier_SanchezSaez_2020.csv" contains the labeled set described in Section 2.2 of the paper.</li> <li>"features_for_lc_classifier_20200609.csv" contains the features computed for the unlabeled ZTF set (up to 2020/06/09). The features are described in Section 3, and Appendix A of the paper.</li> <li>"ALeRCE_lc_classifier_outputs_ZTF_unlabeled_set_20200609.csv" contains the probabilities obtained for each level of the model, as well as the final predictions and probabilities (up to 2020/06/09). For more details about the model see Sections 4.1 and 5.3.</li> </ul> <p> </p> <p> </p>
Data set for validation of a Python script for computation of Protein-Ligand Interaction Fingerprints
<p><strong>1. Data set for for validation of the Protein-Ligand Interaction Fingerprints, which includes examples of protein structures (original PDB and equilibrated) and molecular dynamics trajectories (equilibration and ligand dissociation generated using Random Acceleration MD simulations, RAMD)</strong></p> <p><strong>mdifp_validation_data.tar.gz - </strong>archive that contains benchmark dataset for evaluation of the protein-ligand IFP protocol (PDB structures of protonated complexes, ligands, and MOL2 files of ligands) published in D. B. Kokha, B. Doser, S. Richter, F. Ormersbach, X. Cheng, R. C. Wade "A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprints Analysis of Ligand Trajectories" J. Chem. Phys. <strong>153</strong>, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a></p> <p>(2020) <a href="https://arxiv.org/abs/2006.11066">arXiv:2006.11066</a> </p> <p><strong>2YKI </strong>- protein-ligand complex , PDB ID 2YKI<br> - 2yki_MOE.pdb complex with hydrogen added and energy minimized using MOE software (https://www.chemcomp.com/)<br> - ligand_2yki_MOE.mol2 and ligand_2yki_MOE.pdb - ligand structure with hydrogens prepered by MOE software (https://www.chemcomp.com/)</p> <p><strong>6EI5</strong> - MD trajectory of the protein-ligand complex generated from PDB ID 6EI5<br> - ref-min.pdb minimized structure<br> - ref.prmtop topology file<br> - moe.mol2 - ligand structure in mol2 format<br> - amber2namd2.dcd generated MD trajectory </p> <p><strong>SAD_3-RAMD-03-2020.pkl </strong>- a pkl dataset with IFPs generated from RAMD dissociation trajectory of the complex PDB ID: 5LQ9 (trajectories from the paper Front. Mol. Biosci., 2019 DOI:10.3389/fmolb.2019.00036)</p> <p><strong>HSP90_Gromacs.zip </strong>- an archive that contains three pkl data sets of protein-ligand IFPs (for three HSP90 complexes; PDB ID: 5J64, 5J86, 5LQ9) generated from RAMD dissociation trajectories simulated using new Gromacs-RAMD engine (https://github.com/HITS-MCM/gromacs-ramd)</p> <p>The rest of the files contains data obtained from simulation of the complex of <strong>GPCR muscarinic receptor M2 (PDB ID:4MQT);</strong> immersed in a mixed membrane: 50% CHL, 30% POPC, 20% POPE) with a small molecule agonist iperoxo. <br> - <strong>IXO.pdb and moe.mol2 </strong>- PDBand MOL2 structure of iperoxo<br> - <strong>AMBER_eq.tar.gz</strong> - structure of the equilibrated complex generated using AMBER software<br> -<strong> NAMD_eq.tar.gz </strong>- two equilibration trajectories in dcd format generated using NAMD software <br> - <strong>RAMD_eq.tar.gz </strong>- dissociation tarjectoris of iprtoxo from the M2 protein generated from the last snapshot of two NAMD equilibration trajectories (for each case 2 RAMD dissociaiton trajectories are available) </p> <p>( *csv files were added erroneously and do not belong to the project)</p>
Data set - Lagrangian observations and modelling of turbulence along a tidally influenced river - Refined model
<p>The 'Dataset_Kaipara_Lagrangian_refined.mat' file contains Lagrangian observations collected in the Kaipara river and corresponding model predictions, after refinement of the model. </p> <p> </p> <p>The 'Kaipara_model_refined.mat' files contains the grid and bathymetry of a model of the Kaipara River, New Zealand, created notably in order to study turbulence in a Lagrangian frame of reference. </p>
Supplementary information for: Using networks to identify structure in phylogenetic tree sets
<p>Modern phylogenomic studies produce large sets of trees that can represent variation in inferred phylogenies across genes, uncertainty in estimated phylogenies for a given gene, or both. Standard practice is to condense this variation down to a small set of point estimates or consensus trees in order to facilitate display and interpretation. However, doing so results in the loss of enormous amounts of information about the structure of the underlying tree set. Here, we propose new approaches to explore and detect structure in the tree set itself. These approaches rely on the well-developed mathematical foundations of community detection in networks and leverage two different network types. The first type uses nodes to represent trees and connects these nodes with edges whose weights are determined by the similarity (affinity) of the trees. The second type uses nodes to represent bipartitions and connects nodes with edges whose weights represent the covariance in bipartition presence/absence across trees in the set. These two network types carry information that is complementary, but not identical. A variety of methods may be applied to both networks in order to identify interesting community structure. These community detection approaches provide a rich view of the information contained in phylogenomic data sets and facilitate investigation into the forces driving inferred phylogenetic variation across genomes.</p>
MER Opportunity and Spirit Rovers Pancam Images Labeled Data Set
<p><strong>Introduction</strong></p> <p>The data set is based on 3,004 images collected by the Pancam instruments mounted on the Opportunity and Spirit rovers from NASA's Mars Exploration Rovers (MER) mission. We used rotation, skewing, and shearing augmentation methods to increase the total collection to 70,864 (see Image Augmentation section for more information). Based on the <a href="https://merdatacatalog.com/survey">MER Data Catalog User Survey</a> [1], we identified 25 classes of both scientific (e.g. soil trench, float rocks, etc.) and engineering (e.g. rover deck, Pancam calibration target, etc.) interests (see Classes section for more information). The 3,004 images were labeled on <a href="https://www.zooniverse.org/">Zooniverse platform</a>, and each image is allowed to be assigned with multiple labels. The images are either 512 x 512 or 1024 x 1024 pixels in size (see Image Sampling section for more information).</p> <p><strong>Classes</strong></p> <p>There is a total of 25 classes for this data set. See the list below for class names, counts, and percentages (the percentages are computed as count divided by 3,004). Note that the total counts don't sum up to 3,004 and the percentages don't sum up to 1.0 because each image may be assigned with more than one class. </p> <ul> <li>Class name, count, percentage of dataset</li> <li>Rover Deck, 222, 7.39%</li> <li>Pancam Calibration Target, 14, 0.47%</li> <li>Arm Hardware, 4, 0.13%</li> <li>Other Hardware, 116, 3.86%</li> <li>Rover Tracks, 301, 10.02%</li> <li>Soil Trench, 34, 1.13%</li> <li>RAT Brushed Target, 17, 0.57%</li> <li>RAT Hole, 30, 1.00%</li> <li>Rock Outcrop, 1915, 63.75%</li> <li>Float Rocks, 860, 28.63%</li> <li>Clasts, 1676, 55.79%</li> <li>Rocks (misc), 249, 8.29%</li> <li>Bright Soil, 122, 4.06%</li> <li>Dunes/Ripples, 1000, 33.29%</li> <li>Rock (Linear Features), 943, 31.39%</li> <li>Rock (Round Features), 219, 7.29%</li> <li>Soil, 2891, 96.24%</li> <li>Astronomy, 12, 0.40%</li> <li>Spherules, 868, 28.89%</li> <li>Distant Vista, 903, 30.23%</li> <li>Sky, 954, 31.76%</li> <li>Close-up Rock, 23, 0.77%</li> <li>Nearby Surface, 2006, 66.78%</li> <li>Rover Parts, 301, 10.02%</li> <li>Artifacts, 28, 0.93%</li> </ul> <p><strong>Image Sampling</strong></p> <p>Images in the MER rover Pancam archive are of sizes ranging from 64x64 to 1024x1024 pixels. The largest size, 1024x1024, was by far the most common size in the archive. For the deep learning dataset, we elected to sample only 1024x1024 and 512x512 images as the higher resolution would be beneficial to feature extraction.</p> <p>In order to ensure that the data set is representative of the total image archive of 4.3 million images, we elected to sample via "site code". Each Pancam image has a corresponding two-digit alphanumeric "site code" which is used to track location throughout its mission. Since each "site code" corresponds to a different general location, sampling a fixed proportion of images taken from each site ensure that the data set contained some images from each location. In this way, we could ensure that a model performing well on this dataset would generalize well to the unlabeled archive data as a whole. We randomly sampled 20% of the images at each site within the subset of Pancam data fitting all other image criteria, applying a floor function to non-whole number sample sizes, resulting in a dataset of 3,004 images.</p> <p><strong>Train/validation/test sets split</strong></p> <p>The 3,004 images were split into train, validation, and test data sets. The split was done so that roughly 60, 15, and 25 percent of the 3,004 images would end up as train, validation, and test data sets respectively, while ensuing that images from a given site are not split between train/validaiton/test data sets. This resulted in 1,806 train images, 456 validation images, and 742 test images. </p> <p><strong>Augmentation</strong></p> <p>To augment the images in train and validation data sets (note that images in the test data set were not augmented), three augmentation methods were chosen that best represent transformations that could be realistically seen in Pancam images. The three augmentations methods are rotation, skew, and shear. The augmentation methods were applied with random magnitude, followed by a random horizontal flipping, to create 30 augmented images for each image. Since each transformation is followed by a square crop in order to keep input shape consistent, we had to constrict the magnitude limits of each augmentation to avoid cropping out important features at the edges of input images. Thus, rotations were limited to 15 degrees in either direction, the 3-dimensional skew was limited to 45 degrees in any direction, and shearing was limited to 10 degrees in either direction. Note that augmentation was done only on training and validation images. </p> <p><strong>Directory Contents</strong></p> <ul> <li>images: contains all 70,864 images</li> <li>train-set-v1.1.0.txt: label file for the training data set</li> <li>val-set-v1.1.0.txt: label file for the validation data set</li> <li>test-set-v1.1.0.txt: label file for the testing data set</li> </ul> <p>Images with relatively short file names (e.g., 1p128287181mrd0000p2303l2m1.img.jpg) are original images, and images with long file names (e.g., 1p128287181mrd0000p2303l2m1.img.jpg_04140167-5781-49bd-a913-6d4d0a61dab1.jpg) are augmented images. The label files are formatted as "Image name, Class1, Class2, ..., ClassN".</p> <p> </p> <p><strong>Reference</strong></p> <p>[1] S.B. Cole, J.C. Aubele, B.A. Cohen, S.M. Milkovich, and S.A. Shields, Identifying Community Needs for a Mars Exploration Rovers (MER), Daata Catalog, 51st Lunar and Planetary Science Conference (LPSC), 2020.</p>
Complete set of raw and processed datasets, as well as associated Jupyter notebooks for analysis, associated with manuscript entitled: "The MOUSE project: a practical approach for obtaining traceable, wide-range X-ray scattering information"
<p>This dataset is a complete set of raw, processed and analyzed data, complete with Jupiter notebooks, associated with the manuscript mentioned in the title. </p> <p>In the manuscript, we provide a ``systems architecture''-like overview and detailed discussions of the methodological and instrumental components that, together, comprise the "MOUSE" project (<strong>M</strong>ethodology <strong>O</strong>ptimization for <strong>U</strong>ltrafine <strong>S</strong>tructure <strong>E</strong>xploration). Through this project, we aim to provide a comprehensive methodology for obtaining the highest quality X-ray scattering information (at small and wide angles) from measurements on materials science samples. </p>
Setting the evolutionary timeline: Tillandsia landbeckii in the Chilean Atacama Desert
<p>In the Chilean Atacama Desert <i>Tillandsia landbeckii</i> Phil. is forming a unique vegetation type known as <i>Tillandsia</i> lomas. This vegetation consists in its typical configuration of one single vascular plant species only. The species forms regular linear structures in a sloped landscape and is largely depending on sand movement and fog occurrence as dominant source of water supply. Without developing a typical root system there are also few other terrestrial <i>Tillandsia</i> species growing on bare sand in Chile and Peru such as <i>T. marconae</i> Till & Vitel, <i>T. virescens</i> Ruiz & Pavon, <i>T. purpurea</i> Ruiz & Pavon or <i>T. latifolia</i> Mayen. Although phylogenetic evidence is limited, convergent evolution of this unique growth behavior is evident. The Atacama Desert is one of the oldest hyperarid areas globally alongside the Namib. The predominantly arid and hyper-arid climate exists since the Early Miocene, which raises the question about timing of <i>T. landbeckii</i> evolutionary history. Our study highlights the onset of diversification in <i>T. landbeckii</i> and demonstrates also subsequent secondary genetic contact with <i>T. purpurea</i> during Late Pleistocene using whole plastome DNA sequence data.</p>
Figs 1–6 in Alleviating the taxonomic impediment of DNA barcoding and setting a bad precedent: names for ten species of 'Astraptes fulgerator' (Lepidoptera: Hesperiidae: Eudaminae) with DNA-based diagnoses
Figs 1–6. Intraspecific variation in final instar larval Astraptes spp. 1–3. A. fruticibus 03-SRNP-14690, 05-SRNP-45170, 03-SRNP-30304; 4–6. A. inflatio 02-SRNP-32206, 05-SRNP-96, 02-SRNP-24519; images from Janzen & Hallwachs (2009), which the authors have indicated are in the public domain (Janzen et al., 2009).
Data set for study '"Optimal intervention strategies to mitigate the COVID-19 pandemic effects"
<p>Data associated with the findings presented in the study '"Optimal intervention strategies to mitigate the COVID-19 pandemic effects"</p>
Data from: Comparison of taxon-specific versus general locus sets for targeted sequence capture for plant phylogenomics
Premise of the study: Targeted sequence capture can be used to efficiently gather sequence data for large numbers of loci, such as single-copy nuclear loci. Most published studies in plants have used taxon-specific locus sets developed individually for a clade using multiple genomic and transcriptomic resources. General locus sets can also be developed from loci that have been identified as single-copy and having orthologs in large clades of plants. Methods: We identify and compare a taxon-specific locus set and three general locus sets (COSII, APVO SSC, PPR) for targeted sequence capture in Buddleja (Scrophulariaceae) and outgroups. We evaluate their performance in terms of assembly success, sequence variability, and resolution and support of inferred phylogenetic trees. Results: The taxon-specific locus set had the most target loci. Assembly success was high for all locus sets in Buddleja samples. For outgroups, general locus sets had greater assembly success. Taxon-specific and PPR loci had the highest average variability. The taxon-specific dataset produced the best supported tree, but all datasets showed improved resolution over previous non-sequence capture datasets. Discussion: General loci can be a useful source of sequence capture targets, especially if multiple genomic resources are not available for a taxon.
Data from: The third dimension: a novel set-up for filming coelacanths in their natural environment
Here, we describe a novel design to obtain three-dimensional data on the movements of aquatic organisms at depths of up to 140 m. The set-up consists of two synchronized high-speed cameras fixed to two articulated arms. The set-up was successfully used to film and quantify the locomotion of coelacanths Latimeria chalumnae living at a depth of about 120 m in Sodwana Bay, South Africa. As an example, the detailed motion of the dorsal fin is presented here. This set-up can be used for any underwater applications that require synchronized video recordings of medium- to large-sized animals.
River Erpe (Berlin, Germany) groundwater levels, temperature and 222-radon data set, June 2019
<p>Time series of groundwater levels and temperature, chloride concentrations as well as 222-radon activities in piezometers P0 to P9 and P11 located close to Heidemühle at the River Erpe, Berlin, Germany, collected between June and November 2019. In "Rn_eql_incubations.csv", "mea" denotes the mean activity and "sdd" the associated standard deviation.</p>
Data from: Squamate Conserved Loci (SqCL): a unified set of conserved loci for phylogenomics and population genetics of squamate reptiles
The identification of conserved loci across genomes, along with advances in target capture methods and high-throughput sequencing, has helped spur a phylogenomics revolution by enabling researchers to gather large numbers of homologous loci across clades of interest with minimal upfront investment in locus design. Target capture for vertebrate animals is currently dominated by two approaches – anchored hybrid enrichment (AHE) and ultraconserved elements (UCE) – and both approaches have proven useful for addressing questions in phylogenomics, phylogeography, and population genomics. However, these two sets of loci have minimal overlap with each other; moreover, they do not include many traditional loci that that have been used for phylogenetics. Here, we combine across UCE, AHE, and traditional phylogenetic gene locus sets to generate the Squamate Conserved Loci (SqCL) set, a single integrated probe set that can generate high-quality and highly complete data across all three loci types. We use these probes to generate data for 44 phylogenetically-disparate taxa that collectively span approximately 33% of terrestrial vertebrate diversity. Our results generated an average of 4.29 Mb across 4709 loci per individual, of which an average of 2.99 Mb was sequenced to high enough coverage (≥10×) to use for population genetic analyses. We validate the utility of these loci for both phylogenomic and population genomic questions, provide a comparison among these locus sets of their relative usefulness, and suggest areas for future improvement.
Data from: Effect of ecological momentary assessment, goal-setting and personalized phone-calls on adherence to interval walking training using the InterWalk application among patients with type 2 diabetes – a pilot randomized controlled trial
Objectives: The objective was to investigate the feasibility and usability of structured text-messages, goal-setting and phone-calls on adherence to a 12-week self-conducted interval walking training (IWT) program, delivered by the InterWalk smartphone among patients with type 2 diabetes (T2D). Methods: In a two-arm pilot randomized controlled trial (Denmark, March 2014 to February 2015), patients with T2D (18-80 years with a Body Mass Index of 18 and 40 kg/m2) were randomly allocated to 12 weeks of IWT with (intervention) or without additional support (control). The primary outcome was the difference between groups in accumulated time of interval walking training across 12 weeks. All patients were encouraged to use the InterWalk application to perform IWT for ≥90 minute/week. Patients in the intervention group made individual goals regarding lifestyle change, received automated text-messages once a week, inquiring about exercise adherence. In case of consistent non-adherence, the patients would receive a phone-call inquiring about the reason for non-adherence. The control group did not receive additional support. Information about training adherence was assessed objectively. Usability of structured text-messages was assessed based on response rates and self-reported satisfaction after 12-weeks. Results: Thirty-seven patients with T2D (66 years, 65% female, hemoglobin 1Ac 50.3 mmol/mol) where included (n=18 and n=19 in intervention and control group, respectively). The retention rate was 83%. The intervention group accumulated [95%CI] 345 -7, 698 minutes of IWT more than the control group. The response rate for the text-messages was 83% (68% for males and 90% for females). Forty-one percent of the intervention and 25% of the control group were very satisfied with their participation. Conclusion: The combination of structured text-messages, goal-setting with the possibility of follow-up phone calls are considered feasible interventions to attain training adherence when using the InterWalk app during a 12-week period in patients with T2D. Some uncertainty about the effect size of adherence remains.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.