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49 results for “Space Exploration”
Data and code accompanying "'Safe spaces' and community building for climate scientists, exploring emotions through a case study", Haddaway and Duggan 2023
<p>Data and code accompanying "‘Safe spaces’ and community building for climate scientists, exploring emotions through a case study", Haddaway and Duggan 2023</p>
Single-atom catalysis in space: Computational exploration of Fischer–Tropsch reactions in astrophysical environments
<p>This supporting material contains:</p> <ul> <li>Cartesian coordinates of the PBE optimized minima and transition states for the reactions under study, in XYZ format.</li> <li>Inputs for the <a href="https://www.cp2k.org/">CP2K</a> and <a href="https://www.crystal.unito.it/">Crystal17</a> packages.</li> <li>Vibrational calculations with all the frequencies.</li> <li>Inputs and outputs for the benchmark study performed with the <a href="https://gaussian.com/">Gaussian16</a> package.</li> </ul>
Muscle Mass During Space Exploration
ClinicalTrials.gov study NCT00968344. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space
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Exploring and promoting green urban spaces in Vienna - can data about public perception help drive change?
<p>Citizen Science has become a vital source for data collection when the spatial and temporal extent of a project makes it too expensive to send experts into the field. However, involving citizens can go further than that – participatory projects focusing on subjective parameters can fill in the gap between local community needs and stakeholder approaches to tackle key social and environmental issues.</p> <p>The Horizon 2020 project, <a href="https://landsense.eu/">LandSense</a>, is building a modern citizen observatory for Land Use & Land Cover (LULC) monitoring, by engaging citizens to transform current approaches to environmental decision making. Citizen Observatories are community-driven mechanisms to complement existing environmental monitoring systems and can be fostered through mobile and web applications, allowing citizens to play a key role in environmental monitoring. Within this project, the City Oases mobile application, focused on the city of Vienna, has been developed that aims not only to stimulate civic engagement to monitor changes within the urban environment, but also to enable users to drive improvements by providing city planners with information about the public perception of urban spaces. <a href="https://play.google.com/store/apps/details?id=com.iiasa.cityoases">City Oases</a> was launched in March 2019.</p> <p>Where are the best places for a romantic date? Where can you skate? Where is it cool on a hot summer’s day? Open urban spaces can be used in many ways. Pick an activity in the CityOases app and we show the spots where you can do them, including the rating of previous users and pictures from the location. If you visit the spot you can rate it as well based on a few selected subjective criteria. If you know a cool spot that is openly accessible but not marked in our map yet? Just add it with a list of activities and some pictures. Additionally, you can input your perceptions including whether it is noisy, clean or if the infrastructure is attractive. The picture module within the application promotes you to take photos in the four cardinal directions. Users can search for specific activities, visit one of the points indicated on the map and then evaluate this point.</p> <p>Time period of data collection: 19/03/2019 - 10/11/2019</p> <p>Types of contributors: General Public, Students<br> Number of contributors: 50<br> Number of observations: 788<br> Number of photos: 1846</p> <p>Associated files: City Oases Vienna 2019.csv, City Oases Vienna 2019.geoJSON, City Oases Vienna 2019 Attributes.csv</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="https://www.umweltbundesamt.at/en/">Umweltbundesamt</a> (Environment Agency Austria) and the <a href="https://iiasa.ac.at/">International Institute for Applied Systems Analysis</a>.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
Data for "Exploring Cesium-Tellurium phase space via high-throughput calculations beyond the generalized-gradient approximation"
<p>The AiiDA archives of the high-throughput calculations presented in the paper "Exploring Cesium-Tellurium Phase Space via High-Throughput Density-Functional Theory Calculations".</p> <ul> <li>"calc_mp_Cs-Te.aiida" contains the calculations of the structures originating from the Materials Project database.</li> <li>"calc_oqmd_Cs-Te.aiida" contains the calculations of the structures originating from the open quantum materials database.</li> <li>"calc_mp_Cs-Te_manipulated.aiida" contains the calculations of the structures derived from chemically similar structures originating from the Materials Project database.</li> <li>"calc_oqmd_Cs-Te_manipulated.aiida" contains the calculations of the structures derived from chemically similar structures originating from the open quantum materials database.</li> <li>"calc_Cs5Te3_experimental.aiida" contains the calculations of the additional experimental structure added to the dataset.</li> </ul>
Exploration, representation and rationalization of the conformational phase-space of N-glycans
<p>Collection of structure and trajectory files of free N-glycans simulated in solution using either the CHARMM36m or GLYCAM06j force field. Simulations are either plain MD or enhanced sampled via the combination of replica exchange methods REST-RECT.</p>
Data set for Ligand additivity relationships enable efficient exploration of transition metal chemical space
<p>Dataset of transition metal complexes curated in pickle files and comma delimited format, scripts for CSD curation, and computed DFT properties for associated manuscript.</p>
Exploring the Search Space of Neural Network Combinations obtained with Efficient Model Stitching - Results Data
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Exploring the Chemical Space of Glycosylation in Noncovalent Protein Complexes: an Expedition along Different Structural Levels of Human Chorionic Gonadotropin Employing Mass Spectrometry
<p><strong>Supplementary files for "Exploring the Chemical Space of Glycosylation in Noncovalent Protein Complexes: an Expedition along Different Structural Levels of Human Chorionic Gonadotropin Employing Mass Spectrometry"</strong></p> <p><strong>Introduction</strong></p> <p>This data repository contains all previously unpublished raw data files for the manuscript “Exploring the Chemical Space of Glycosylation in Noncovalent Protein Complexes: an Expedition along Different Structural Levels of Human Chorionic Gonadotropin Employing Mass Spectrometry” by Maximilian Lebede<sup>||</sup>, Fiammetta Di Marco<sup>||</sup>, Wolfgang Esser-Skala, René Hennig, Therese Wohlschlager, Christian G. Huber.</p> <p><strong>Files</strong></p> <p>This repository contains 9 files:</p> <ul> <li><strong>Dimer Raw Files.zip</strong> folder containing 4 files of native-MS data (*.raw, Thermo RAW file format) of two batches of the drug product Ovitrelle® at native dimer level. </li> <li><strong>H11M9 Ovitrelle BA056714 Glycopeptide R1 230920_07.zip</strong> folder containing 1 file of HPLC-MS/MS glycopeptide data (*.raw, Thermo RAW file format) of one batch of the drug product Ovitrelle®.</li> <li><strong>H11M9 Ovitrelle BA056714 Glycopeptide R2 230920_08.zip</strong> folder containing 1 file of HPLC-MS/MS glycopeptide data (*.raw, Thermo RAW file format) of one batch of the drug product Ovitrelle®.</li> <li><strong>H11M9 Ovitrelle BA056714 Glycopeptide R3 230920_09.zip</strong> folder containing 1 file of HPLC-MS/MS glycopeptide data (*.raw, Thermo RAW file format) of one batch of the drug product Ovitrelle®.</li> <li><strong>H11M9 Ovitrelle BA059433 Glycopeptide R1 240920_15.zip</strong> folder containing 1 file of HPLC-MS/MS glycopeptide data (*.raw, Thermo RAW file format) of one batch of the drug product Ovitrelle®.</li> <li><strong>H11M9 Ovitrelle BA059433 Glycopeptide R2 240920_16.zip</strong> folder containing 1 file of HPLC-MS/MS glycopeptide data (*.raw, Thermo RAW file format) of one batch of the drug product Ovitrelle®.</li> <li><strong>H11M9 Ovitrelle BA059433 Glycopeptide R3 240920_17.zip</strong> folder containing 1 file of HPLC-MS/MS glycopeptide data (*.raw, Thermo RAW file format) of one batch of the drug product Ovitrelle®.</li> <li><strong>MoFi Settings.zip</strong> folder containing 12 files of MoFi settings (*.xml) to annotate deconvoluted spectra of hCG subunits and dimer of two Ovitrelle® batches, untreated and after desialylation. A typical MoFi setting file is build from protein sequence (*.FASTA), monosaccharide and frequent modification atomic composition (*.csv), glycan or glycoform library (*.csv) and deconvoluted spectrum in centroid (*.csv). Files are named as following: Settings_Ovitrelle_Batch number (BA056714 or BA059433)_Structural level (Alpha, Beta or Dimer)_Enzymatic treatement (Untreated or Sialidase).</li> <li><strong>Subunit Raw Files.zip</strong> folder containing 8 files of HPLC-MS data (*.raw, Thermo RAW file format) of two batches of the drug product Ovitrelle® at intact subunit level. </li> </ul> <p>Raw files are named as following: Instrument, Drug product (Ovitrelle), Batch number (BA056714 or BA059433), Structural level (Dimer, Subunits or Glycopeptides), Enzymatic treatment (untreated, Sialidase, PNGase F or PNGase F + Sialidase) and date. Glycopeptide data includes 3 replicates (R1-3).</p> <p><strong>License</strong></p> <p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit <a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a> .</p> <p> </p>
Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials
<p>This dataset contains a vertical slice of the data used to generate the results found in the publication "Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials"<br> It contains nested sampling input files and trajectory files for each potential studied, as well as the xml files and training data for the new potential, GAP-20U+gr.</p>
Data from: Efficient exploration of the space of reconciled gene trees
Gene trees record the combination of gene-level events, such as duplication, transfer and loss, and species-level events, such as speciation and extinction. Gene tree-species tree reconciliation methods model these processes by drawing gene trees into the species tree using a series of gene and species level events. The reconstruction of gene trees based on sequence alone almost always involves choosing between statistically equivalent or weakly distinguishable relationships that could be much better resolved based on a putative species tree. To exploit this potential for accurate reconstruction of gene trees the space of reconciled gene trees must be explored according to a joint model of sequence evolution and gene tree-species tree reconciliation. Here we present amalgamated likelihood estimation (ALE), a probabilistic approach to exhaustively explore all reconciled gene trees that can be amalgamated as a combination of clades observed in a sample of gene trees. We implement the ALE approach in the context of a reconciliation model (Szöllősi et al., 2013), which allows for the duplication, transfer and loss of genes. We use ALE to efficiently approximate the sum of the joint likelihood over amalgamations and to find the reconciled gene tree that maximizes the joint likelihood among all such trees. We demonstrate using simulations that gene trees reconstructed using the joint likelihood are substantially more accurate than those reconstructed using sequence alone. Using realistic gene tree topologies, branch lengths and alignment sizes, we demonstrate that ALE produces more accurate gene trees even if the model of sequence evolution is greatly simplified. Finally, examining 1099 gene families from 36 cyanobacterial genomes we find that joint likelihood-based inference results in a striking reduction in apparent phylogenetic discord, with resp. 24%,59% and 46% percent reductions in the mean numbers of duplications, transfers and losses per gene family. The open source implementation of ALE is available from https://github.com/ssolo/ALE.git.
Data from: Exploring variation in fitness surfaces over time or space
As the number of studies estimating selection on multiple traits has increased in recent years, fitness surfaces have become a fundamental tool for understanding multivariate selection and evolution. However, rigorous statistical comparisons of multivariate selection surfaces over time or space have been limited to parametric analyses of selection coefficients estimated using a quadratic regression model. Although parametric comparisons are useful when selection is approximately linear or quadratic in nature, they are limited when confronting the complex nature of rugged fitness surfaces. Here, I present a novel solution to comparing non-parametric fitness surfaces over time or space. Using a Tucker3 tensor decomposition, which is essentially a higher-order principal components analysis, I show how major features of fitness surfaces can be compared statistically. Combined with a bootstrap algorithm, I develop three statistical tests that identify 1) Differences in the shape of non-parametric fitness surfaces, 2) Differences in the contribution of each surface to variation in fitness across time or space, and 3) Specific areas of the surfaces (trait combinations) that vary significantly over time or space. I illustrate the tensor decomposition and statistical analyses using idealized fitness surfaces.
Data from: Exploring and visualising spaces of tree reconciliations
Tree reconciliation is the mathematical tool that is used to investigate the coevolution of organisms, such as hosts and parasites. A common approach to tree reconciliation involves specifying a model that assigns costs to certain events, such as cospeciation, and then tries to find a mapping between two specified phylogenetic trees which minimises the total cost of the implied events. For such models, it has been shown that there may be a huge number of optimal solutions, or at least solutions that are close to optimal. It is therefore of interest to be able to systematically compare and visualise whole collections of reconciliations between a specified pair of trees. In this paper, we consider various metrics on the set of all possible reconciliations between a pair of trees, some that have been defined before but also new metrics that we shall propose. We show that the diameter for the resulting spaces of reconciliations can in some cases be determined theoretically, information that we use to normalise and compare properties of the metrics. We also implement the metrics and compare their behaviour on several host parasite datasets, including the shapes of their distributions. In addition, we show that in combination with multidimensional scaling, the metrics can be useful for visualising large collections of reconciliations, much in the same way as phylogenetic tree metrics can be used to explore collections of phylogenetic trees. Implementations of the metrics can be downloaded from: https://team.inria.fr/erable/en/team-members/blerina-sinaimeri/reconciliation-distances/
DraftSeptember 16, 2024 (v1)DatasetOpen Exploring the conformational space of proteins with an efficient set of TlCA features based on GoMartini 3 descriptors
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Data from: Quantifying MCMC exploration of phylogenetic tree space
In order to gain an understanding of the effectiveness of phylogenetic Markov chain Monte Carlo (MCMC), it is important to understand how quickly the empirical distribution of the MCMC converges to the posterior distribution. In this paper we investigate this problem on phylogenetic tree topologies with a metric that is especially well suited to the task: the subtree prune-and-regraft (SPR) metric. This metric directly corresponds to the minimum number of MCMC rearrangements required to move between trees in common phylogenetic MCMC implementations. We develop a novel graph-based approach to analyze tree posteriors and find that the SPR metric is much more informative than simpler metrics that are unrelated to MCMC moves. In doing so we show conclusively that topological peaks do occur in Bayesian phylogenetic posteriors from real data sets as sampled with standard MCMC approaches, investigate the efficiency of Metropolis-coupled MCMC (MCMCMC) in traversing the valleys between peaks, and show that conditional clade distribution (CCD) can have systematic problems when there are multiple peaks.
Optimizing Performance and Energy Across Problem Sizes Through a Search Space Exploration and Machine Learning - dataset
<p>Dataset used for a journal submission at JPDC.</p>
Data from: Exploring variation in fitness surfaces over time or space
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Data from: Exploring and visualising spaces of tree reconciliations
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Data from: Quantifying MCMC exploration of phylogenetic tree space
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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.