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656 results for “go”
MetaFunc Databases: nr-go database
<p>MetaFunc is a computational pipeline that can take input reads and pass it through a pipeline that will then analyse host genes from the reads on one side, and microbiome taxonomies and gene ontology annotations on the other, and finally allowing for microbe-host gene correlations. This dataset contains databases used for analysing the microbiome component of the pipeline. Full description of the pipeline can be found at https://metafunc.readthedocs.io/en/latest/#.</p>
Go-nogo categorization and detection task
Open the record for dataset details and reuse information.
Stocktaking GO FAIR Discovery IN - Use cases, infrastructure
<p>In order to build a better ecosystem for data discovery tools the Data Discovery Implementation Group of GO Fair (https://www.go-fair.org/implementation-networks/overview/discovery) collected use cases between 2019 and 2020 from a variety of sources. We also detail the ‘Actors’ for these use cases and the ‘Source’ providing links, whenever possible. Since we found over a hundred individual use cases, we decided to cluster them to provide a better overview. The clustering, as well as the results of a small survey among data infrastructure specialists to find how they rate the importance of the clusters are detailed in the documentation to this dataset, a draft of which can currently be found <a href="https://docs.google.com/document/d/1sq78eCFYgmWcMFYcbNonA2KrkUO1qdGr7d49tHuRcRM/edit?usp=sharing">here</a>. The code and data to produce the figures in the documentation are available as R code in the GO_FAIR_Discovery_Use_case-master.zip file. The use cases themselves are available as Excel sheet and csv. </p>
Data release for "Things that might Go bump in the night: Assessing structure in the binary black hole mass spectrum"
<p>Data release accompanying "Things that might go bump in the night: Assessing structure in the binary black hole mass spectrum"</p> <p>Included are:</p> <ul> <li>500 mock catalogs containing 69 events each, in netCDF4 format (can be found in `with_z_evo_lalprior_69_evs_prod_mock_PE.tar.gz`)</li> <li>A corresponding injection set using O3 sensitivity (`with_z_evo_lalprior_69_evs_prod_injections.h5`)</li> <li>Files containing hyperposterior samples resulting from a Power Law + Spline fit to 100 of the 69-event mock catalogs (`PowerLawSpline_69evs_20knots_2t100_*_result.json`)</li> <li>Files containing hyperposterior samples resulting from a smoothed power law fit to 100 of the 69-event mock catalogs (`Truncated_69evs_*_result.json`)</li> </ul> <p>Code using these files to create all plots in the paper can be found at https://git.ligo.org/amanda.farah/bump-significance</p> <p>Code used to create the mock catalogs can be found at https://git.ligo.org/amanda.farah/mock-PE</p>
CS:GO Bets Time series
<p>This is a dataset made taking the data from hltv about the odds which come from different Online Gambling Companies (OGCs). It shows the bet data, the result of the match and the timestamp.<br> </p>
Raw and processed GO term data to support running GCEA analyses using ensemble-based nulls, as described in the manuscript, 'Overcoming bias in gene category enrichment analyses of brain-wide transcriptomic data'.
<p>Data to support a toolbox for performing gene category enrichment analyses, including against ensembles of null phenotypes.</p> <p>Descriptions of how these data files can be used for this purpose are in the documentation for the toolbox, at https://github.com/benfulcher/GCEA_FalsePositives</p>
GO-SHIP Easy Ocean: Formatted and gridded ship-based hydrographic section data
<p><a href="https://www.go-ship.org">GO-SHIP</a> (The Global Ocean Ship-based Hydrographic Investigations Program) has developed the protocols and methods to generate a data product that concatenates all occupations of individual sections into a time-series; the GO-SHIP Easy Ocean. Here we provide access to the analysis-ready gridded GO-SHIP Easy Ocean product that enhances the accessibility of this unique data set that spans four decades, comprised of more than 40 cross-ocean transects, many with multiple repeats.</p> <p>This product, of uniformly calibrated CTD (temperature, salinity and oxygen) data, provides easy access to and use of the high-quality hydrographic temperature and salinity data that span more than 40 years. The GO-SHIP Easy Ocean product will underpin the quality control of autonomous platforms, provide a ready assessment of ocean-only and coupled climate model simulations, and be used in specific research projects. The GO-SHIP Easy Oceanis a companion to the GLODAP inorganic and carbon product. The section data are available from Zenodo in two standard arrangements: Uninterpolated (reported) and interpolated (gridded). For both arrangements, five quantities are recorded; in situ temperature in ITS-90 scale, in situ salinity in PSS-78 scale, the dissolved oxygen concentration in μmol/kg, Conservative Temperature in °C, and Absolute Salinity in g/kg. The data are available in various formats.</p> <p>Cite <a href="https://doi.org/10.1038/s41597-022-01212-w">Katsumata et al (2022)</a> when using this product and include the following acknowledgment statement in any publication or derived product:</p> <p><em>Data were collected and made publicly available by the International Global Ship-based Hydrographic Investigations Program GO-SHIP (https://www.go-ship.org/) and the national programs that contribute to it.</em></p>
Dataset and software for support of the article "To Chaos or Not To Chaos: going into the resilience of the ecosystem".
<p>The CAVeg model, as well as its dependencies, and the CAVeg data files with the configurations for the two and three ecophysiological types, including the data needed to perform the Lyapunov exponents calculations with the CRAN-R DChaos package are available in this dataset.</p>
Data from: Estimation in the multinomial reencounter model - Where do migrating animals go and how do they survive in their destination area?
<p><strong>Abstract</strong></p> <p>Spatial variation in survival has individual fitness consequences and influences population dynamics. Which space animals use during the annual cycle determines how they are affected by this spatial variability. Therefore, knowing spatial patterns of survival and space use is crucial to understand demography of migrating animals. Extracting information on survival and space use from observation data, in particular dead recovery data, requires explicitly identifying the observation process. We build a fully stochastic model for animals marked in populations of origin, which were found dead in spatially discrete destination areas. It acts on the population level and includes parameters for use of space, survival and recovery probability. The model is based on the division coefficient and the multinomial reencounter model. We use a likelihood-based approach, derive Restricted Maximum Likelihood-like estimates for all parameters and prove their existence and uniqueness. In a simulation study we demonstrate the performance of the model by using Bayesian estimators derived by the Markov chain Monte Carlo method. We obtain unbiased estimates for survival and recovery probability if the sample size is large enough. Moreover, we apply the model to real-world data of European robins <em>Erithacus rubecula</em> ringed at a stopover site. We obtain annual survival estimates for different spatially discrete non-breeding areas. Additionally, we can reproduce already known patterns of use of space for this species. We would like to thank the Greifswalder Oie Bird Observatory of the Verein Jordsand, Ahrensburg, and the Hiddensee Bird Ringing Centre, Güstrow, for providing the robin data.</p>
Microbes go to school - Output repository
<p>This dataset is an output repository for the final report of the Agora project "Microbes go to school" funded by SNF from 2020 to 2022. This project aims at using service-learning to bridge the gap between university and school, and disseminate knowledge in microbiology and biodiversity in the classroom by engaging students as communicators. In this repository, you'll find general content about the project (gallery, course descriptions, and the article we published), pedagogical content (protocols of the activities edited by us, original content produced by the students that was evaluated, and the feedback form that we sent to the teachers to evaluate the students), and outreach content (guide for trainers, newsletters and recipes).</p>
GO-FISH: Geolocated Ocean-Fishery Identified Spawning Habitats
<p>This dataset represents geocoded spawning regions for 1,045 marine fish species described in the Fishbase (https://www.fishbase.se/) and Science and Conservation of Fish Aggregations (SCRFA, <a href="https://www.scrfa.org/database/">https://www.scrfa.org/database/</a>) datasets. These global databases have painstakingly aggregated the fieldwork of countless biologists and ecologists to summarize our knowledge of fish species. We further constrained geographic locations using AquaMaps (<a href="https://www.aquamaps.org/">https://www.aquamaps.org</a>) to produce 2,931 polygons or groups of polygons, which we call "spawning regions".</p> <p>Reproduction code for the dataset is available at <a href="https://github.com/openmodels/spawning-dataset">https://github.com/openmodels/spawning-dataset</a>, archived at <a href="../records/11098955">https://zenodo.org/records/11098955</a>.</p>
[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process
<p><br>Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical<br>emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here</p>
Official GO FAIR Foundation icons for the Three-Point FAIRification Framework
<p>The official icons for the Three-Point FAIRification Framework (3PFF): Metadata for Machines Workshops, FAIR Implementation Profiles and FAIR Orchestration, created by the GO FAIR Foundation.</p>
GO Term annotations for five plants species from Phytozome by FANTASIA
<p>This is the GO term annotation made with FANTASIA for five species (Arabidopsis thaliana, Oryza sativa, Zea mays, Populus trichocarpa, and Solanum lycopersicum) from the Phytozome 13 datasets as proof of concept for this tool.</p>
Dataset: Breaking Type-Safety in Go: An Empirical Study on the Usage of the unsafe Package
<p>This dataset contains all script used in the study, as well as the raw data extracted from the repositories and the processed data used to analyze our RQs in the manuscript "Breaking Type-Safety in Go: An Empirical Study on the Usage of the unsafe Package".</p> <p>For more information on how to understand the folder structure, scripts, and dataset, please read the README.md. </p> <p> </p> <p> </p>
Going against the grain – Texture orientation affects direction of exploratory movement
<p>In haptic perception sensory signals depend on how we actively move our hands. For textures with periodically repeating grooves, movement direction can determine temporal cues to spatial frequency. Moving in line with texture orientation does not generate temporal cues. In contrast, moving orthog-onally to texture orientation maximizes the temporal frequency of stimulation, and thus optimizes temporal cues. Participants performed a spatial frequency discrimination task between stimuli of two types. The first type showed the de-scribed relationship between movement direction and temporal cues, the second stimulus type did not. We expected that when temporal cues can be optimized by moving in a certain direction, movements will be adjusted to this direction. However, movement adjustments were assumed to be based on sensory infor-mation, which accumulates over the exploration process. We analyzed 3 indi-vidual segments of the exploration process. As expected, participants only ad-justed movement directions in the final exploration segment and only for the stimulus type, in which movement direction influenced temporal cues. We con-clude that sensory signals on the texture orientation are used online during ex-ploration in order to adjust subsequent movements. Once sufficient sensory evi-dence on the texture orientation was accumulated, movements were directed to optimize temporal cues.</p> <p><strong>Lezkan</strong>, A. & <strong>Drewing</strong>, K. (2016). Going against the grain – Texture orientation affects direction of exploratory movement, part I. <em>Haptics: Perception, Devices, Control, and Applications</em> (pp. 430-440).</p> <p>The Zip file contains all data relative to the publication.</p> <p>A description of the variables is contained in the file VARIABLE_CODES.txt</p>
Figura 1 in Levantamento de cigarras (Hemiptera: Cicadidae) em área de mata de galeria no município de Israelândia (GO)
Figura 1. Curva de rarefação (Mao Tau) (círculo) e estimativa de espécie (Jackknife 1) (quadrado) obtidos para cigarras coletadas com o uso de armadilha luminosa em área de mata de galeria localizada no município de Israelândia durante o período de agosto de 2017 a julho de 2018. Os pontos indicam o valor médio e as barras o intervalo de confiança a 95%.
Figura 2 in Levantamento de cigarras (Hemiptera: Cicadidae) em área de mata de galeria no município de Israelândia (GO)
Figura 2. Número de cigarras (acima) e de espécies (abaixo) coletadas por mês com o uso de armadilha luminosa em área de mata de galeria localizada no município de Israelândia durante o período de agosto de 2017 a julho de 2018.
Going beyond Top EFT - Code and Data
<p>The file BeyondTopEFT.zip contains all the code and processed data for reproducing the results in the <a href="https://arxiv.org/abs/2312.00670">Beyond Top EFT paper</a>.</p><p>The raw data is provided as separate tarballs.</p><p>Additional instructions can be found in the README file contained in BeyondTopEFT.zip or in the <a href="https://github.com/andlessa/SMStoEFT">GitHub repository</a>.</p><p> </p>
Data from: Bees go up, flowers go down: Increased resource limitation from late spring to summer in agricultural landscapes
<p>Data underlying the publication "Bees go up, flowers go down: Increased resource limitation from late spring to summer in agricultural landscapes". Site coordinates are excluded from this dataset for data protection.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.