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85 results for “Resource Modelling”
Data from: genetic resources of macroalgae: development of an efficient method using microsatellite markers in non-model organisms
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Model and data for: Economical defense of resources structures territorial space use in a cooperative carnivore
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Pre-trained models for Vietnamese Natural Resources and Environment Domain
<p>Two Pre-trained models for Vietnamese Natural Resources and Environment Domain:</p> <p>- fastText</p> <p>- Word2vec</p>
Data from: "Genome-wide microsatellite marker development from next-generation sequencing of two non-model bat species impacted by wind turbine mortality: Lasiurus borealis and L. cinereus (Vespertilionidae)" in Genomic Resources Notes accepted 1 October 2013 to 30 November 2013
Tree-roosting bats in the genus Lasiurus are widespread, migratory species that have not been well characterized for population genetic diversity and structure due to a lack of genetic resources. Generating genetic resources in Lasiurus is made pressing by the need for conservation genetic assessments of demographic trends in this genus, which comprise a large percentage of bat mortalities at wind turbine sites across North America. We report on marker development from whole-genome Illumina sequencing of the red bat (Lasirus borealis) and the hoary bat (L. cinereus). We generated paired-end libraries for a single individual of each species, sequenced on the Illumina HiSeq platform. We mapped a total of 46.6 million reads to the Myotis lucifigus reference genome, and used bioinformatics searches to identify tends of thousands of simple sequence repeats (SSRs) distributed across the bat genome. We selected 48 candidate microsatellite loci to develop cross-species primer sequences for Lasiurus, assembled these into multiplex combinations, and tested for amplification and polymorphism levels in a sample of 23 individuals from each of L. borealis and L. cinereus. In total, we identified 42 highly polymorphic loci that could be robustly amplified and scored, the majority of which (39) were also combinable into highly multiplexed assays of 4-8 loci each. The combination of new genomic sequence assemblies, a large set of highly polymorphic microsatellite loci, and the ability to efficiently multiplex represents a significant contribution to the genetic resources available for population and comparative genetic studies of bats.
OpenET model data for assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications
<h2>Overview</h2> <p>This dataset includes daily and monthly evapotranspiration (ET) data from the remote sensing models that comprise the [OpenET](https://openetdata.org/) ensemble as described in Melton et al., 2022 (https://doi.org/10.1111/1752-1688.12956); these data were extracted at specific locations within the contiguous United States that coincide with *in situ* measurement stations, including eddy covaraiance, Bowen-ratio, and lysimeter stations. Model ET data where extracted at each site in this dataset using flux footprints as described in Volk et al., (2023) (https://doi.org/10.1016/j.agrformet.2023.109307). These model data alongside the corresponding *in situ* ET data (https://doi.org/10.1016/j.dib.2023.109274) were subsequently used in the manuscript for the OpenET Phase II Intercomparison and Accuracy Assessment (https://doi.org/10.1038/s44221-023-00181-7). </p> <h3><br>Description of the data and file structure</h3> <p>The dataset is in a compressed (zipped) archive titled "OpenET_PhaseII_model_ET_dataset", so first it needs to be downloaded and extracted. The dataset is comprised of just three files. The first file is a Microsoft Excel file "Station_metadata.xlsx" that contains information about the *in situ* ET measurement stations where the OpenET model data was extracted. This file contains information such as site ID's, coordinates, land cover information, and site principal investigator (PI) contact information. Again, the corresponding *in situ* ET data are not included in this dataset. The other two files are tab-delimited text files containing timeseries the OpenET model data themselves, namely the daily ET [mm/day] and monthly ET [mm/month] as extracted for each model and the ensemble value as used in the OpenET Phase II Intercomparison and Accuracy Assessment. </p> <h3><br>Access information and code/software</h3> <p>OpenET data that was used here was produced using operational methods that are implemented on the Google Earth Engine platform. Monthly OpenET model data can be retrieved through Google Earth Data Catalog (e.g., https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0) or through the [online data explorer](https://openetdata.org/) or using the [OpenET API](https://openetdata.org/api-info/).</p>
Resources for the paper "Teaching a Multilingual Large Language Model to Understand Multilingual Speech via Multi-Instructional Training"
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Model input for "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"
<p>This dataset includes the model input files required to reproduce the simulations described in <em>"A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"</em>.</p> <p>When using these files to run the model, input files should be organized in a folder named <code>INPUT/</code> located within the directory where the model is executed. Additionally, key configuration files, such as <code>data_table</code>, <code>diag_table</code>, <code>field_table</code>, and <code>input.nml</code>should be placed in the main working directory.</p> <p>To manage file size, only a subset of the data is provided, covering the first year of the simulation period. Note that ERA5 atmospheric forcing data is not included in this dataset but can be accessed directly from the <a href="https://doi.org/10.24381/cds.adbb2d47" target="_new" rel="noopener">Copernicus Climate DataStore</a>.</p> <p> </p>
The success of alien plants in an arid ecosystem: Structural equation modeling reveals hidden effects of soil resources
<p> data</p>
Data from: Human judgment vs. quantitative models for the management of ecological resources
Despite major advances in quantitative approaches to natural resource management, there has been resistance to using these tools in the actual practice of managing ecological populations. Given a managed system and a set of assumptions, translated into a model, optimization methods can be used to solve for the most cost-effective management actions. However, when the underlying assumptions are not met, such methods can potentially lead to decisions that harm the environment and economy. Managers who develop decisions based on past experience and judgment, without the aid of mathematical models, can potentially learn about the system and develop flexible management strategies. However, these strategies are often based on subjective criteria and equally invalid and often unstated assumptions. Given the drawbacks of both methods, it is unclear whether simple quantitative models improve environmental decision making over expert opinion. In this study, we explore how well students, using their experience and judgment, manage simulated fishery populations in an online computer game and compare their management outcomes to the performance of model-based decisions. We consider harvest decisions generated using four different quantitative models: (1) the model used to produce the simulated population dynamics observed in the game, with the values of all parameters known (as a control), (2) the same model, but with unknown parameter values that must be estimated during the game from observed data, (3) models that are structurally different from those used to simulate the population dynamics, and (4) a model that ignores age structure. Humans on average performed much worse than the models in cases 1–3, but in a small minority of scenarios, models produced worse outcomes than those resulting from students making decisions based on experience and judgment. When the models ignored age structure, they generated poorly performing management decisions, but still outperformed students using experience and judgment 66% of the time.
Explaining Non-Entailment by Model Transformation for the Description Logic EL - IJCKG22 - Resources
<p><strong>resources.zip</strong> contains the experiment data and results, and a README file explaining how to rerun the experiment.</p>
Estimation if Nickel Laterite Resources Using Model Block Size in Lasolo Island District, North Konawe Regency
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
A Web-resource for Nutrient Use Efficiency related Genes, QTLs, and microRNA in important cereals and model plants
<p>Cereals are key contributors to global food security. Genes involved in the uptake (transport), assimilation and utilization of macro- and micronutrients are responsible for the presence of these nutrients in grain and straw. Although many genomic databases for cereals are available, there is currently no cohesive web resource of manually curated nutrient use efficiency (NtUE)-related genes and quantitative trait loci (QTLs). In this study, we present a <a href="http://bioclues.org/NtUE/index.php">web- resource</a> containing information on NtUE-related genes/QTLs and the corresponding available microRNAs for some of these genes in four major cereal crops (wheat (<em>Triticum aestivum</em>), rice (<em>Oryza sativa</em>), maize (<em>Zea mays</em>), barley (<em>Hordeum vulgare</em>)), two alien species related to wheat (<em>Triticum urartu</em> and<em> Aegilops tauschii</em>), and two model species (<em>Brachypodium distachyon </em>and <em>Arabidopsis thaliana</em>). Gene annotations integrated in the current web resource were manually curated from the existing databases and the available literature. The primary goal of developing this web resource is to provide descriptions of the NtUE-related genes and their functional annotation. MicroRNAs targeting some of the NtUE related genes and the QTLs for NtUE-related traits are also included. The genomic information embedded in the web resource should help users to search for the desired information.</p>
Model input for initial submission of "A regional physical-biogeochemical ocean model for marine resource applications in the Northeast Pacific (MOM6-COBALT-NEP10k v1.0)" to GMD
<p>This dataset contains the numerical model input files that were used to produce the model simulation presented in the initual submission "A regional physical-biogeochemical ocean model for marine resource applications in the Northeast Pacific (MOM6-COBALT-NEP10k v1.0)" to Geoscientific Model Development.</p> <p>When using these files to run a model, most files should be placed inside a directory named INPUT/ that resides in the working directory where the model is being run. The following files should be at the top level in the working directory: data_table, diag_table, field_table, input.nml.</p> <p>For large files, a subset in time is provided for the first year of the simulation. This dataset does not include the ERA5 atmospheric data, which can be downloaded directly from https://doi.org/10.24381/cds.adbb2d47.</p> <p>Portions of the initial and boundary condition data were generated using E.U. Copernicus Marine Service Information:https://doi.org/10.48670/moi-00021. Refer to the manuscript for references to other data sources.</p> <p>Codes for generating regional MOM6 initial conditions, boundary conditions and other necessary model inputs as well as diagnostic scripts are maintained on the NOAA CEFI GitHub Repository: https://github.com/NOAA-GFDL/CEFI-regional-MOM6/. </p>
Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions
<p>Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSF), or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from Golden Eagles (Aquila chrysaetos) from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.</p>
Model input for "A high-resolution physical-biogeochemical model for marine resource applications in the Northwest Atlantic (MOM6-COBALT-NWA12)"
<p>This dataset contains the numerical model input files that were used to produce the model simulation presented in "A high-resolution physical-biogeochemical model for marine resource applications in the Northwest Atlantic (MOM6-COBALT-NWA12)".</p> <p>When using these files to run a model, most files should be placed inside a directory named INPUT/ that resides in the working directory where the model is being run. The following files should be at the top level in the working directory: data_table, diag_table, field_table, input.nml.</p> <p>For large files, a subset in time is provided for the first year of the simulation. This dataset does not include the ERA5 atmospheric data, which can be downloaded directly from https://doi.org/10.24381/cds.adbb2d47.</p> <p>Portions of the initial and boundary condition data were generated using E.U. Copernicus Marine Service Information: \url{https://doi.org/10.48670/moi-00021}, \url{https://doi.org/10.48670/moi-00148}. Refer to the manuscript for references to other data sources.</p>
Data from: "Genome-wide microsatellite marker development from next-generation sequencing of two non-model bat species impacted by wind turbine mortality: Lasiurus borealis and L. cinereus (Vespertilionidae)" in Genomic Resources Notes accepted 1 October 2013 to 30 November 2013
Open the record for dataset details and reuse information.
Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions
Open the record for dataset details and reuse information.
Data from: Human judgment vs. quantitative models for the management of ecological resources
Open the record for dataset details and reuse information.
Data from: Estimating resource preferences of a native bumblebee: the effects of availability and use-availability models on preference estimatess
Identifying resource preference is considered essential for developing targeted conservation plans but, for many species, questions remain about the best way to estimate preference. Resource preferences for bees are particularly difficult to determine as the resources they collect, nectar and pollen, are challenging to estimate availability and collection. Resources are traditionally measured at the flower or inflorescence level, but these measures of availability do not correspond to the resources actually used by bees. Additionally, it is unclear as to whether common models including availability are appropriate for bees which may target resources regardless of available quantities. Here we first compare two common hypotheses of resource use – the 'random use hypothesis' and the 'linear preferences hypothesis' – using three different measures of availability (pollen, flower and inflorescence) – to determine if one measure of availability was better for understanding bee pollen use. Next, the superior model using availability was compared to a novel model of bee pollen use the 'target use hypothesis'. This model assumes that bees target some resources regardless of how much of each resource is available (but assuming resources are present at a site), and thus models preference without availability data. Of the models including availability, the linear preference model using inflorescence availability best explained the pollen use data. This suggests that bumblebee pollen use is non-random and that cues to identify and locate resources (i.e. display size and quantity) may be more important than the quantity of the resource available (i.e. pollen availability). Additionally, in most cases the target use model explained the data equal to or better than the other models suggesting bee resource use may be better modeled without measured availability data compared to linear models. These results could be important for expanding resource use analysis of bees that are difficult to quantify availability.
Data from: Fruiting strategies of perennial plants: a resource budget model to couple mast seeding to pollination efficiency and resource allocation strategies
Masting, a breeding strategy common in perennial plants, is defined by seed production that is highly variable over years and synchronized at the population level. Resource budget models (RBMs) proposed that masting relies on two processes: (i) the depletion of plant reserves following high fruiting levels, which leads to marked temporal fluctuations in fruiting; and (ii) outcross pollination that synchronizes seed crops among neighboring trees. We revisited the RBM approach to examine the extent to which masting could be impacted by the degree of pollination efficiency, by taking into account various logistic relationships between pollination success and pollen availability. To link masting to other reproductive traits, we split the reserve depletion coefficient into three biological parameters related to resource allocation strategies for flowering and fruiting. While outcross pollination is considered to be the key mechanism that synchronizes fruiting in RBMs, our model counterintuitively showed that intense masting should arise under low-efficiency pollination. When pollination is very efficient, medium-level masting may occur, provided that the costs of female flowering (relative to pollen production) and of fruiting (maximum fruit set and fruit size) are both very high. Our work highlights the powerful framework of RBMs, which include explicit biological parameters, to link fruiting dynamics to various reproductive traits and to provide new insights into the reproductive strategies of perennial plants.
ScienceDex guides
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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.