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621 results for “methodologies”
Supplementary material 1 from: Basset Y, Donoso DA, Hajibabaei M, Wright MTG, Perez KHJ, Lamarre GPA, De León LF, Palacios-Vargas JG, Castaño-Meneses G, Rivera M, Perez F, Bobadilla R, Lopez Y, Ramirez JA, Barrios H (2020) Methodological considerations for monitoring soil/litter arthropods in tropical rainforests using DNA metabarcoding, with a special emphasis on ants, springtails and termites. Metabarcoding and Metagenomics 4: e58572. https://doi.org/10.3897/mbmg.4.58572
Methodological considerations for monitoring soil/litter arthropods in tropical rainforests using DNA metabarcoding, with a special emphasis on ants, springtails and termites
Data from: How to make methodological decisions when inferring social networks.
<p>Social network analyses allow studying the processes underlying the associations between individuals and the consequences of those associations. Constructing and analysing social networks can be challenging, especially when designing new studies as researchers are confronted with decisions about how to collect data and construct networks, and the answers are not always straightforward. The current lack of guidance on building a social network for a new study system might lead researchers to try several different methods, and risk generating false results arising from multiple hypotheses testing. Here, we suggest an approach for making decisions when starting social network research in a new study system that avoids the pitfall of multiple hypotheses testing. We argue that best edge definition for a network is a decision that can be made using <i>a priori</i> knowledge about the species, and that is independent from the hypotheses that the network will ultimately be used to evaluate. We illustrate this approach with a study conducted on a colonial cooperatively breeding bird, the sociable weaver. We first identified two ways of collecting data using different numbers of feeders and three ways to define associations among birds. We then evaluated which combination of data collection and association definition maximised (i) the assortment of individuals into previously known 'breeding groups' (birds that contribute towards the same nest and maintain cohesion when foraging), and (ii) socially differentiated relationships (more strong and weak relationships than expected by chance). This evaluation of different methods based on <i>a priori</i> knowledge of the study species can be implemented in a diverse array of study systems and makes the case for using existing, biologically meaningful knowledge about a system to help navigate the myriad of methodological decisions about data collection and network inference.</p>
PlosONE: The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach
<p>This data set for the publication "The challenge of learning a new language in adulthood: Evidence from a multi-methodological neuroscientific approach" in PlosONE (2021) comprises:</p> <p>EEG: Event-related brain potential data (single subjects, microvolt values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged) pseudoword-picture pairings (PPP) for learned as well as new PPPs for time windows 650-800 and 800-1300 ms.</p> <p>fNIRS: oxy-Hb and deoxy-Hb data (single subjects, beta values, regions of interest) for both correctly identified and correctly and incorrectly identified (merged) pseudoword-picture pairings (PPP) for learned as well as new PPPs</p> <p>Behavioral data: Task Performance during the recognition experiment (reaction times for and percentage of correctly identified learned and new PPPs)</p> <p> </p>
Communication Challenges in adopting scrum methodology in research-based projects
<p>The folder contains three other folders, one has the questionnaire question, the second one has participants' answers to the questionnaire, and the last one has data analysis files for the questionnaire. </p>
Data from: Towards a common methodology for developing logistic tree mortality models based on ring-width data
Tree mortality is a key process shaping forest dynamics. Thus, there is a growing need for indicators of the likelihood of tree death. During the last decades, an increasing number of tree-ring based studies have aimed to derive growth–mortality functions, mostly using logistic models. The results of these studies, however, are difficult to compare and synthesize due to the diversity of approaches used for the sampling strategy (number and characteristics of alive and death observations), the type of explanatory growth variables included (level, trend, etc.), and the length of the time window (number of years preceding the alive/death observation) that maximized the discrimination ability of each growth variable. We assess the implications of key methodological decisions when developing tree-ring based growth–mortality relationships using logistic mixed-effects regression models. As examples, we use published tree-ring datasets from Abies alba (13 different sites), Nothofagus dombeyi (one site), and Quercus petraea (one site). Our approach is based on a constant sampling size and aims at (1) assessing the dependency of growth–mortality relationships on the statistical sampling scheme used, (2) determining the type of explanatory growth variables that should be considered, and (3) identifying the best length of the time window used to calculate them. The performance of tree-ring-based mortality models was reasonably high for all three species (area under the receiving operator characteristics curve, AUC > 0.7). Growth level variables were the most important predictors of mortality probability for two species (A. alba, N. dombeyi), while growth-trend variables need to be considered for Q. petraea. In addition, the length of the time window used to calculate each growth variable was highly uncertain and depended on the sampling scheme, as some growth–mortality relationships varied with tree age. The present study accounts for the main sampling-related biases to determine reliable species-specific growth–mortality relationships. Our results highlight the importance of using a sampling strategy that is consistent with the research question. Moving towards a common methodology for developing reliable growth–mortality relationships is an important step towards improving our understanding of tree mortality across species and its representation in dynamic vegetation models.
Data from: Methodological congruence in phylogenomic analyses with morphological support for teiid lizards (Sauria: Teiidae)
A well-known issue in phylogenetics is discordance among gene trees, species trees, morphology, and other data types. Gene-tree discordance is often caused by incomplete lineage sorting, lateral gene transfer, and gene duplication. Multispecies-coalescent methods can account for incomplete lineage sorting and are believed by many to be more accurate than concatenation. However, simulation studies and empirical data have demonstrated that concatenation and species tree methods often recover similar topologies. We use three popular methods of phylogenetic reconstruction (one concatenation, two species tree) to evaluate relationships within Teiidae. These lizards are distributed across the United States to Argentina and the West Indies, and their classification has been controversial due to incomplete sampling and the discordance among various character types (chromosomes, DNA, musculature, osteology, etc.) used to reconstruct phylogenetic relationships. Recent morphological and molecular analyses of the group resurrected three genera and created five new genera to resolve non-monophyly in three historically ill-defined genera: Ameiva, Cnemidophorus, and Tupinambis. Here, we assess the phylogenetic relationships of the Teiidae using "next-generation" anchored-phylogenomics sequencing. Our final alignment includes 316 loci (488,656 bp DNA) for 244 individuals (56 species of teiids, representing all currently recognized genera) and all three methods (ExaML, MP-EST, and ASTRAL-II) recovered essentially identical topologies. Our results are basically in agreement with recent results from morphology and smaller molecular datasets, showing support for monophyly of the eight new genera. Interestingly, even with hundreds of loci, the relationships among some genera in Tupinambinae remain ambiguous (i.e. low nodal support for the position of Salvator and Dracaena).
Data from: Species delimitation with gene flow: a methodological comparison and population genomics approach to elucidate cryptic species boundaries in Malaysian Torrent Frogs
Accurately delimiting species boundaries is a non-trivial undertaking that can have significant effects on downstream inferences. We compared the efficacy of commonly-used species delimitation methods (SDMs) and a population genomics approach based on genome-wide single nucleotide polymorphisms (SNPs) to assess lineage separation in the Malaysian Torrent Frog Complex currently recognized as a single species (Amolops larutensis). First, we used morphological, mitochondrial DNA and genome-wide SNPs to identify putative species boundaries by implementing non-coalescent and coalescent-based SDMs (mPTP, iBPP, BFD*). We then tested the validity of putative boundaries by estimating spatiotemporal gene flow (fastsimcoal2, ABBA-BABA) to assess the extent of genetic isolation among putative species. Our results show that the A. larutensis complex runs the gamut of the speciation continuum from highly divergent, genetically isolated lineages (mean Fst = 0.9) to differentiating populations involving recent gene flow (mean Fst = 0.05; Nm > 5). As expected, SDMs were effective at delimiting divergent lineages in the absence of gene flow but overestimated species in the presence of marked population structure and gene flow. However, using a population genomics approach and the concept of species as separately evolving metapopulation lineages as the only necessary property of a species, we were able to objectively elucidate cryptic species boundaries in the presence of past and present gene flow. This study does not discount the utility of SDMs but highlights the danger of violating model assumptions and the importance of carefully considering methods that appropriately fit the diversification history of a particular system.
Data from: Discrimination of hybrid classes using cross-species amplification of microsatellite loci: methodological challenges and solutions in Daphnia
Microsatellite markers are important tools in population, conservation and forensic studies and are frequently used for species delineation, the detection of hybridization and introgression. Therefore, marker sets that amplify variable DNA regions in two species are required; however, cross-species amplification is often difficult, as genotyping errors such as null alleles may occur. In order to estimate the level of potential misidentifications based on genotyping errors, we compared the occurrence of parental alleles in laboratory and natural Daphnia hybrids (Daphnia longispina group). We tested a set of twelve microsatellite loci with regard to their suitability for unambiguous species and hybrid class identification using F1 hybrids bred in the laboratory. Further, a large set of 44 natural populations of D. cucullata, D. galeata and D. longispina (1715 individuals) as well as their interspecific hybrids were genotyped to validate the discriminatory power of different marker combinations. Species delineation using microsatellite multi-locus genotypes produced reliable results for all three studied species using assignment tests. D. galeata x cucullata hybrid detection was limited due to three loci exhibiting D. cucullata specific null alleles which most likely are caused by differences in primer binding sites of parental species. Overall discriminatory power in hybrid detection was improved when a subset of markers was identified that amplifies equally well in both species.
Data from: Smart nest box: a tool and methodology for monitoring of cavity-dwelling animals
1.Camera recording and video analysis have emerged as a successful non-invasive method for collecting a wide range of biological data on many different taxa of animals. However, camera monitoring has rarely been applied to long term surveillance of cavity or box-nesting species and ordinary off-the-shelf cameras are employed. 2.We present methodology and data on the effectiveness of nest box monitoring using a camera system embedded in four "smart nest boxes" (SNBoxes). We applied the SNBoxes to eight Tengmalm's owl (Aegolius funereus) nests in the Czech Republic during a five-month period in 2014. Each SNBox consisted of a pair of cameras with infrared lighting, an event detector, a radio-frequency identification reader, auxiliary sensors, and a 60 Ah 12 V battery to power the whole system. All devices used were centrally managed by an embedded computer with specifically developed software. 3.Using four SNBoxes, we observed owl nesting continually during the incubation, nestling, and fledgling phases, in total 309 days, resulting in 3382 owl video events. Batteries were changed every 6.5 days. A memory of 4 GB was found sufficient to store monthly data. We identified 12 types of male and female parental activities and their timing, the diet composition and frequency of prey delivery, the manner of prey storage, the light intensity at the time of each parental activity, the temperature inside the clutch and outside the box, and the duration of nestling period of each young. We also produced a video on owl nesting for the general public. 4.The SNBox and related methodology show enormous potential as a non-invasive tool for monitoring animals using boxes or natural cavities. The main advantage of the SNBox is the possibility to study both nocturnal and diurnal animal species and great flexibility in use of the software and hardware for different tasks. As a result, the SNBox provides an opportunity for novel insights into the breeding, roosting, hibernating, and food storage activities of a wide range of cavity-living birds, mammals, and reptiles.
Systemic inflammation: methodological approaches to identification of the common pathological process
<p>Files are dataset for submitted manuscript <strong>Systemic inflammation: methodological approaches to identification of the common pathological process. </strong>All primary data both of septic and nonifectious pathologies are included.</p>
HUMANE: Qualitative data on HUMANE methodology & dimensions' evaluation (REVEAL case)
<p>Through the data analysis, we first tried to verify the preliminary dimensions’ implementation of HUMANE methodology. </p>
Dataset for A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market
Open the record for dataset details and reuse information.
SURFBIO Training: Wet-dry methodologies for studying cell-surface and cell-cell interactions (2021)
<p>SURFBIO project webinars within WP2 (2021).</p><p>Content:</p><ul><li><strong>The state-of-the-art approaches, technologies and tools dedicated to microbial genome isolation, sequencing, and data analysis: </strong></li></ul><p>Part I: Technologies and computational workflows for analysis of genome data (María Suárez Díez, WUR).</p><p>Part II: Experimental techniques for microbial genome isolation (Juan Antonio Tamayo Ramos, UBU).</p><ul><li><strong>Webinar on transcriptomics analysis: from experimental design to data analysis: </strong></li></ul><p>Methodologies for isolation and purification of high-quality RNA from biotic and abiotic surfaces (Juan Antonio Tamayo Ramos, UBU).</p><p>Technologies and computational workflows for analysis of RNA data (Maria Suarez Diez, WUR).</p><ul><li><strong>Detailing state of the art approaches, from sample preparation to data analysis, dedicated to proteomics in the context of cell-cell and cell-surface interactions (Cristina Furlan, UBU).</strong></li><li><strong>Detailing state of the art approaches, technologies and tools dedicated to metabolomics approaches, by Juan José González Plaza (UBU).</strong></li><li><strong>Computational methods for omics data analysis and integration:</strong></li></ul><p>Statistical and computational workflows for omics data integration, by María Suárez-Díez (WUR).</p><ul><li><strong>Webinar on Introduction to FAIR data management in SURFBIO, by María Suárez-Díez (WUR).</strong></li></ul>
Data for: Methodological choices in size and density fractionation of soil carbon reserves – A case study on wood fiber sludge amended soils
<p>A myriad of methods is currently being applied in soil carbon research encompassing major variation in the basic principles and minor variation in details. The most proper method is dependent on the research question and soil type, wherefore a consensus will likely never be reached, and it is difficult to label any method inappropriate. Both the fundamental and the subtle choices in methodology affect the results, wherefore increasing the method-related understanding of soil, agricultural and environmental scientists is of utmost importance. In scientific literature, methods are often not thoroughly presented, let alone reasoned. With this data, we discuss methodological choices in soil carbon fractionation. In addition, our case study follows the effects of pulp mill sludge amendments on soil carbon in a Luvic Stagnosol and Dystric Arenosol. Organic materials are being applied to soil to increase productivity and soil carbon storage. Our results show the difficulties of accumulating carbon in the stable, mineral associated pool, when the carbon content is initially relatively high, and the sorption capacity of the mineral phase already largely occupied. </p>
Fourteen Years of Cellular Deconvolution: Methodology, Applications, Technical Evaluation, and Outstanding Challenges
<p>The CELLxGENE datasets are used to reproduce the benchmarking results in the article "Fourteen Years of Cellular Deconvolution: Applications, Benchmark, Methodology, and Challenges". The instruction to download the data from CELLxGENE can be found at https://github.com/tinnlab/DeconBenchmark-analysis.</p>
Fourteen Years of Cellular Deconvolution: Methodology, Applications, Technical Evaluation, and Outstanding Challenges
<p>The Tabula Sapiens dataset is used to reproduce the benchmarking results in the article "Fourteen Years of Cellular Deconvolution: Applications, Benchmark, Methodology, and Challenges". The data can be downloaded at https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5, by selecting "Tabula Sapiens - All Cells".</p>
Systematic review for optimizing sample size in dairy cow methane emission studies: a comprehensive methodological approach
<p>Collection of research data focusing on methane (CH4) emissions from dairy cows across various breeds and conditions. The dataset encompasses a range of studies published from 2012 to 2023, each documented with specific parameters including the study title, authors, country of research, cow breed, lactation status, methods used for CH4 measurement, experimental designs, CH4 yield and its variability, dry matter intake, and diet composition.</p>
Thermal modeling of a high-energy prismatic lithium-ion battery cell and module based on a new thermal characterization methodology
Open the record for dataset details and reuse information.
Data from: Automated size measurements of Halyomorpha halys (Stål)(Heteroptera: Pentatomidae) with simple image-based methodology
<p>This data is a companion to this paper: </p> <p>Amy Tabb, Johanna E Elsensohn, Tracy C Leskey, Automated size measurements of Halyomorpha halys (Stål)(Heteroptera: Pentatomidae) with simple image-based methodology," 2022, Florida Entomologist, 105(3):262-264. doi: 10.1653/024.105.0314 </p> <p>@article{tabb_automated_2022,<br>author = {Amy Tabb and Johanna E. Elsensohn and Tracy C. Leskey},<br>title = {{Automated Size Measurements of Halyomorpha halys (Stål) (Heteroptera: Pentatomidae) with Simple Image-Based Methodology}},<br>volume = {105},<br>journal = {Florida Entomologist},<br>number = {3},<br>publisher = {Florida Entomological Society},<br>pages = {262 -- 264},<br>keywords = {brown marmorated stink bug, chinche apestosa marrón marmorada, computer vision, morfología, morphology, visión por computador},<br>year = {2022},<br>doi = {10.1653/024.105.0314},<br>URL = {https://doi.org/10.1653/024.105.0314}<br>}</p> <p>The paper makes use of a tool called CASS. We list its information here as well:</p> <p>A. Tabb, Germán A. Holguín, and Rachel Naegele, “Using Cameras for Precise Measurement of Two-Dimensional Plant Features: CASS,” in High-Throughput Plant Phenotyping: Methods and Protocols, A. Lorence and K. Medina Jimenez, Eds. New York, NY: Springer US, 2022, pp. 87–94. doi: 10.1007/978-1-0716-2537-8_10. or https://arxiv.org/abs/1904.13187</p> <p><br><strong>Manually-measured data</strong></p> <p>The ground-truth data is in Dead-BMSB-measurements-ground-truth.xlsx.</p> <p><strong>Reproducing graphs</strong></p> <p>The directory plots has the following structure:<br>plots<br>- dataLength.dat <br>- gt_bottomLength.dat <br>- plottingOctave.m<br>- dataWidth.dat<br>- gt_width.txt</p> <p>To recreate the graphs from the paper, run script plottingOctave in GNU Octave. It is likely to run as well in Matlab, but we have not tested. We have used Octave vers. 4.2.1. dataLength.dat and dataWidth.dat are the results from the automated computer vision method.</p> <p><strong>Data structure</strong></p> <p>Raw image data of Brown Marmorated Stink Bug (BMSB) on calibration patterns is in input-images\images. The full structure of the input directory is as follows:</p> <p>input-images<br>- calibration_object_info.txt<br>- exif-log.txt<br>- sensor_size.txt<br>- created_template.png<br>- specification_file.txt<br>- images (directory)</p> <p>calibration_object_info.txt : Gives the size dimensions of the aruco pattern.</p> <p>exif-log.txt : Exif information from one of the images.</p> <p>sensor_size.txt : Image sensor dimensions of the iPhone 8, needed for CASS to perform calibration from a single image.</p> <p>created_template.png : The calibration pattern created by CASS; in the case of this project, the center of the pattern was masked and only the border patterns remain.</p> <p>specification_file.txt : Information concerning the aruco patterns, needed for CASS to perform calibration from a single image.</p> <p> images : The first three characters are the number of the insect, i.e. 001 is 1, 100 is 100, etc. All are images from an iPhone 8.</p> <p><strong>Processed images structure</strong><br><br>The processed images are in the directory result-images. Directory cass has the result of applying the single-image calibration and warping, such that every 10 pixels is equivalent to 1 mm, and cropped out from the surrounding background calibration patterns. The segmentation directory images have the insect segmented from the background, using the warped images from the cass directory. </p>
Artificial intelligence-based parametrization of Michaelis–Menten maximal velocity: Toward in silico New Approach Methodologies (NAMs)
<p><span>The development of mechanistic systems biology models necessitates the utilization of numerous kinetic parameters once the enzymatic mode of action has been identified. Moreover, wet lab experimentation is associated with particularly high costs, does not adhere to the principle of reducing the number of animal tests, and is a time-consuming procedure. Alternatively, an artificial intelligence-based method is proposed that utilizes enzyme amino acid structures as input data. This method combines NLP techniques with molecular fingerprints of the catalyzed reaction to determine Michaelis–Menten maximal velocities (Vmax). The molecular fingerprints employed include RCDK standard fingerprints (1024 bits), MACCS keys (166 bits), PubChem fingerprints (881 bits), and E-States fingerprints (79 bits). These were integrated to produce reaction fingerprints. The data were sourced from SABIO RK, providing a concrete framework to support training procedures. After the data preprocessing stage, the dataset was randomly split into a training set (70%), a validation set (10%), and a test set (20%), ensuring unique amino acid sequences for each subset. The data points with structures similar to those used to train the model as well as uncommon reactions were employed to further test the model. The developed models were optimized during the training procedure to predict Vmax values efficiently and reliably. By utilizing a fully connected neural network, these models can be applied to all organisms. The amino acid proportions of enzymes were also tested, which revealed that the amino acid content was an unreliable predictor of the Vmax. During testing, the model demonstrated better performance on known structures than on unseen data. In the given use case, the model trained solely on enzyme representations achieved an R-squared of 0.45 on unseen data and 0.70 on known structures. When enzyme representations were integrated with RCDK fingerprints, the model achieved an R-squared of 0.46 for unseen data and 0.62 for known structures.</span></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.