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60 results for “Adaptive sampling”
Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization"
<p>Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization".<br>Preprint of the paper available at: <a href="https://arxiv.org/abs/2309.03308">https://arxiv.org/abs/2309.03308</a></p>
Rivals Reloaded - Adapting to Sample-Based Speed–Accuracy Trade-Offs Through Competitive Pressure: Data
<p>Data and codebook for experiment described in publication titled Rivals Reloaded - Adapting to Sample-Based Speed–Accuracy Trade-Offs Through Competitive Pressure published in Journal of Experimental Psychology: Learning, Memory, and Cognition authored by Linda McCaughey, Johannes Prager and Klaus Fiedler </p>
Text-fig. 1. Map of westernmost part of Rio Grande do Sul showing the position of the sampling locality (star). Adapted from Oliveira and Kerber (2009). in A New Fossil Fabaceae Wood From The Pleistocene Touro Passo Formation Of Rio Grande Do Sul, Brazil
Text-fig. 1. Map of westernmost part of Rio Grande do Sul showing the position of the sampling locality (star). Adapted from Oliveira and Kerber (2009).
Scripts and sample information for: Molecular mechanisms of Eda-mediated adaptation to freshwater in threespine stickleback
<p><span>A main goal of evolutionary biology is to understand the genetic basis of adaptive evolution. Although the genes that underlie some adaptive phenotypes are now known, the molecular pathways and regulatory mechanisms mediating the phenotypic effects of those genes often remain a black box. Unveiling this black box is necessary to fully understand the genetic basis of adaptive phenotypes, and to understand why particular genes might be used during phenotypic evolution. Here, we investigated which genes and regulatory mechanisms are mediating the phenotypic effects of the <em>Eda</em> haplotype, a locus responsible for the loss of lateral plates and changes in the sensory lateral line of freshwater threespine stickleback (<em>Gasterosteus aculeatus</em>) populations. Using a combination of RNAseq and a cross design that isolated the Eda haplotype on a fixed genomic background, we found that the Eda haplotype affects both gene expression and alternative splicing of genes related to bone development, neuronal development and immunity. These include genes in conserved pathways, like the BMP, netrin and bradykinin signalling pathways, known to play a role in these biological processes. Furthermore, we found that differentially expressed and differentially spliced genes had different levels of connectivity and expression, suggesting that these factors might influence which regulatory mechanisms are used during phenotypic evolution. Taken together, these results provide a better understanding of the mechanisms mediating the effects of an important adaptive locus in stickleback and suggest that alternative splicing could be an important regulatory mechanism mediating adaptive phenotypes.</span></p>
Scripts and sample information for: Molecular mechanisms of Eda-mediated adaptation to freshwater in threespine stickleback
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Evaluation data for: Adaptive sampling by citizen scientists improves species distribution model performance: a simulation study
<p>All of the evaluation data for the simulations in the paper: Adaptive sampling by citizen scientists improves species distribution model performance: a simulation study. We considered the impact of five adaptive sampling methods on the performance of species distribution models (SDMs), please see the paper for more information. Contained in this repository are the evaluation metrics (AUC, mean square error (MSE) and correlation) for SDMs before and after adaptive sampling has taken place. The MSE and correlation evaluation metrics were calculated against the true distributions of the species. These files are those with "combined_outputs" in the titles. The repository also contains the observations of all the species in the simulations both before and after adaptive sampling (the files with "all_observations" in the title.</p> <p>These datasets are to be used with the plotting and evaluation scripts in the GitHub repository associated with the paper.</p>
03_HTMD_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes. </p> <p># The forders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p> <p> </p> <p> </p>
06_HTMD_Tunnels: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes. </p> <p># The folders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, <em>run_adaptiveMD.py</em> : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p> <p> </p>
Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p><strong>00_Caver.tar.gz </strong>- Contains CAVER (https://caver.cz/fil/download/manual/caver_userguide.pdf) input and output files used for identification of transport pathways in LinB86(PDB ID: 5LKA). <br>final_clustering<br>├── tunnel_custers # contains caver output files for individual tunnels clusters <br>│ ├── ...<br>├── analysis # contains .csv output files for botttlenecks and tunnels charecteristics of individual tunnels clusters <br>│ ├── ...</p> <p><strong>01_CaverDock_Tunnels_Profile.tar.gz</strong> - Contains tunnel clusters consisting of the top 100 tunnels and the CaverDock analysis files obtained. <br># Each folder (tun_cluster_p1a, tun_cluster_p1b, tun_cluster_p2, tun_cluster_p3) contains input raw files used for CaverDock calculations for individual snapshots of the respective tunnel clusters named as stripped_system*. The details of those files are:<br>- <em>calculations/*/caverdock.conf</em> : The config file input for caverdock calculation. <br>-<em> calculations/*/DBE.pdbqt </em>: Input file for the substrate DBE.<br>- <em>calculations/*/stripped_system*.pdbqt </em>: Input file for the Protein/Receptor<br>- c<em>alculations/*/stripped_system*.dsd </em>: Tunnel discretization file. Notes: <br>- <em>calculations/*/stripped_system*.pdb </em>: PDB file for the tunnel. </p> <p><strong>02_Minimization_and_Equilibration.tar.gz</strong> - Contains input and output files used for AMBER minimization and equilibration of the molecular systems and seed conformations used for adaptive sampling simulations.</p> <p><strong>03_HTMD_Bulk</strong> - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes. <br><strong>04_HTMD_Cavity</strong> - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes.<br><strong>05_HTMD_Cavity_Bulk</strong> -<strong> </strong>separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes. <br><strong>06_HTMD_Tunnels</strong> - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes. </p> <p><strong>07_MD-Analysis.tar.gz</strong> - Contains MD analysis files obtained from 45 micro-seconds adaptive sampling simulations at 310K. <br># Each folder contains input raw files used to calculate epochs convergence, distances, RMSD, RMSF, kinetics, percentage, sample proportions and tunnel lengths. The details of those files are:<br>- <em>Epoch_Convergence/epochs_dist_counts.csv</em> : Contains the counts of DBE distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å) for the 30 epochs. <br>- <em>Distances/*/dist_s_r*.csv</em> : Contains .csv file for the frames wise for all studied schemes. The analysis were performed using the cpptraj program (https://amber-md.github.io/cpptraj/CPPTRAJ.xhtml). The following columns are present:<br>D107_OD1_DBE_C1 <br>D107_OD2_DBE_C1 <br>D107_OD1_DBE_C2 <br>D107_OD2_DBE_C2 <br>N37_ND2_DBE_Br1 <br>N37_ND2_DBE_Br2 <br>W108_NE1_DBE_Br1 <br>W108_NE1_DBE_Br2 <br>D107_COM_DBE_COM <br>W108_COM_DBE_COM <br>N37_COM_DBE_COM <br>catal_COM_p1aCOM <br>catal_COM_p1bCOM <br>catal_COM_p2COM <br>catal_COM_p3COM <br>p1aCOM_DBE_COM <br>p1bCOM_DBE_COM <br>p2COM_DBE_COM <br>p3COM_DBE_COM <br>catal_COM_DBE_COM <br>p1aCOM_p1bCOM <br>p1aCOM_p2COM <br>p1aCOM_p3COM <br>p1bCOM_p2COM <br>p1bCOM_p3COM <br>p2COM_p3COM <br>- <em>RMSD_RMSF/*/*.csv</em> : Contains .csv files with RMSD and RMSF from the protein residues. For RMSF 1st row are residue number (1-295) and 2nd row are RMSF. For RMSD, 1st column are frame no. (0.1ns) and 2nd column are RMSD values respectively. The calcualtion were performed using pytraj (https://amber-md.github.io/pytraj/latest/index.html) program. Example input: pytraj.rmsd(traj, mask='1-295@CA') Example input: pytraj.rmsf(traj, mask=':1-295', options='byres')<br>- <em>COM_RMSF/.xlsx</em> : Contains the center of mass (COM) distances calculated using the bottleneck residues for catalytic residues (N38, D108, W109), p1a (D147, F151, and V173), p1b (D147, W177, and L248), p2 (L211 and L248), and p3 (L143, F151, and I213)<br>- <em>Kinetics/kinetics.txt</em> : Contains .csv file with kinetic information from studied scheme: Cavity, Cavity&Bulk and Tunnels for all the three replicates and calculated average kon, koff, koff/kon rates.<br>- <em>Percentages/.csv</em> : Contains csv files for the percentages of DBE localization and distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å).<br>- <em>Tunnels_lengths/.csv</em> : Contains <em>tunnel_lengths.csv</em>, <em>Summary of tunnel lengths.xlsx</em> files with lengths of top 100 tunnels snapshots for tunnel clusters p1a, p1b, p2 and p3 in <em>tunnel_lengths.csv</em> and summary of respective tunnel clusters generated from Caver output (for more details please check https://www.caver.cz/fil/download/manual/caver_userguide.pdf with keywork "summary.txt") in the <em>Summary of tunnel lengths.xlsx</em> file. <br>- <em>Sample_proportions/.csv</em> : Contains <em>sample_proportions.csv</em> file with proportions or fraction individual metastable states while performing the transition pathway analysis. For more details please check https://software.acellera.com/htmd/htmd.kinetics.html<br> or http://www.emma-project.org/v1.2.1/api/generated/pyemma.msm.flux.pathways.html?highlight=transition%20path<br> - <em>*.py</em> : Python scripts used to build and analysis Markov state models with use case and distances of ligand.<br>- <em>Generated_models/</em> : Contains <em>models_rep[].dat</em> files representating the matric data used to build MSM for respective schemes and replicates. The dirs are arranged as below:<br>├── Cavity<br>│ ├── model_rep1.dat<br>│ ├── model_rep2.dat<br>│ └── model_rep3.dat<br>├── Cavity_Bulk<br>│ ├── model_rep1.dat<br>│ ├── model_rep2.dat<br>│ └── model_rep3.dat<br>└── Tunnels<br> ├── model_rep1.dat<br> ├── model_rep2.dat<br> └── model_rep3.dat </p> <p><br><strong>08_TransportTools.tar.gz</strong> - Contains TransportTools (TT) analysis output, log and summary files for Cavity, Cavity&Bulk and Tunnels schemes. For more details on the workflow of TT, please visit https://github.com/labbit-eu/transport_tools<br>results-*_rep0 # results for replicate 1 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>results-*_rep1 # results for replicate 2 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>results-*_rep2 # results for replicate 3 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>- <em>event.csv</em> file contains the aggregated summary of events inferred from the <em>4-filtered_events_statistics.txt</em> files of each results of respective schemes</p> <p><strong>09_MSM_states.tar.gz</strong> - Contains the Markov state models (MSM) output files for Cavity, Cavity&Bulk and Tunnels schemes and three replicates. The MSMs were generated using the pyEMMA program and HTMD framework, for further details please follow https://software.acellera.com/htmd/documentation.html. The directories looks as below: <br>├── Bulk<br>│ ├── rep1 # MSM states for replicate 1<br>│ ├── rep2 # MSM states for replicate 2<br>│ ├── rep3 # MSM states for replicate 3<br>├── Cavity<br>│ ├── rep1 <br>│ ├── rep2 <br>│ ├── rep3 <br>├── Cavity&Bulk<br>│ ├── rep1 <br>│ ├── rep2<br>│ ├── rep3<br>├── Tunnels<br>│ ├── rep1 <br>│ ├── rep2<br>│ ├── rep3</p> <p><br><strong>10_MSM_fingerprints.tar.gz</strong> - Contains the Markov state models (MSMs) distances generated from repository dir <strong>09_MSM_states</strong> consisting the model*.pdb files. The distances were calculated using the cpptraj program of AMBER18 package.<br>- <em>MSM_Distances/*/rep*/*.csv</em> : Contains .csv file for the generated MSM models (0,1,2..). The following columns (calculated distances) are present in the .csv files:<br>D107_OD1_DBE_C1 <br>D107_OD2_DBE_C1 <br>D107_OD1_DBE_C2 <br>D107_OD2_DBE_C2 <br>N37_ND2_DBE_Br1 <br>N37_ND2_DBE_Br2 <br>W108_NE1_DBE_Br1 <br>W108_NE1_DBE_Br2 <br>D107_COM_DBE_COM <br>W108_COM_DBE_COM <br>N37_COM_DBE_COM <br>catal_COM_p1aCOM <br>catal_COM_p1bCOM <br>catal_COM_p2COM <br>catal_COM_p3COM <br>p1aCOM_DBE_COM <br>p1bCOM_DBE_COM <br>p2COM_DBE_COM <br>p3COM_DBE_COM <br>catal_COM_DBE_COM <br>p1aCOM_p1bCOM <br>p1aCOM_p2COM <br>p1aCOM_p3COM <br>p1bCOM_p2COM <br>p1bCOM_p3COM <br>p2COM_p3COM </p> <p><br><strong>11_ULS_clustering_and_transition_assignments.tar.gz</strong> - Contains files for analysis of utilization of the substrate DBE. Each folder contains two types of .csv files:<br>1. for the transition detection of DBE and the classification in Bulk (out_), Bottleneck (bt_), Unknown bottleneck (bt_unknown), Inside (in_) and <br>2. the second type as the charecterization on the tunnels utilization: Tunnel (p1a, p1b, p2, and p3), Mixed and Unknnown.<br># the details of the file arangements are as below for the studied schemes Bulk, Cavity, Cavity&Bulk and Tunnels:<br>├── average_tunnel_utilization_per_scheme.png<br>├── average_tunnel_utilization.png<br>├── Bulk<br>│ ├── Bulk_run_htmd_0_combined_df.csv<br>│ ├── Bulk_run_htmd_0_transitions_counts.csv<br>│ ├── Bulk_run_htmd_1_combined_df.csv<br>│ ├── Bulk_run_htmd_1_transitions_counts.csv<br>│ ├── Bulk_run_htmd_2_combined_df.csv<br>│ └── Bulk_run_htmd_2_transitions_counts.csv<br>├── Bulk&Cavity<br>│ ├── Cavity&Bulk_run_htmd_0_combined_df.csv<br>│ ├── Cavity&Bulk_run_htmd_0_transitions_counts.csv<br>│ ├── Cavity&Bulk_run_htmd_1_combined_df.csv<br>│ ├── Cavity&Bulk_run_htmd_1_transitions_counts.csv<br>│ ├── Cavity&Bulk_run_htmd_2_combined_df.csv<br>│ └── Cavity&Bulk_run_htmd_2_transitions_counts.csv<br>├── Cavity<br>│ ├── Cavity_run_htmd_0_combined_df.csv<br>│ ├── Cavity_run_htmd_0_transitions_counts.csv<br>│ ├── Cavity_run_htmd_1_combined_df.csv<br>│ ├── Cavity_run_htmd_1_transitions_counts.csv<br>│ ├── Cavity_run_htmd_2_combined_df.csv<br>│ └── Cavity_run_htmd_2_transitions_counts.csv<br>├── parse_distances_msm.py<br>├── schemes_comparison_piechart_per_scheme.png<br>└── Tunnels<br> ├── Tunnels_run_htmd_0_combined_df.csv<br> ├── Tunnels_run_htmd_0_transitions_counts.csv<br> ├── Tunnels_run_htmd_1_combined_df.csv<br> ├── Tunnels_run_htmd_1_transitions_counts.csv<br> ├── Tunnels_run_htmd_2_combined_df.csv<br> └── Tunnels_run_htmd_2_transitions_counts.csv</p> <p> </p>
05_HTMD_Cavity_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output, and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes. </p> <p># The folders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, *run_adaptiveMD.py* : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p>
04_HTMD_Cavity: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes. </p> <p># The forders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p>
Adaptive sampling-based structural prediction reveals opening of a GABAA receptor through the αβ interface
<p>These are the simulation of an open state of α1β2<span>γ</span><span>2 GABAA receptor. There are five replicates, each for 200 ns, saved with every ns freequency. The prot_masses.pdb is the desenstized starting state model and the .dcd files are simualtion frames of the open state. </span></p>
Measurement Matrix and Path Examples for Adaptive Sparse Sampling for Quasiparticle Interference Imaging
<p>precalculated measurement paths used with adaptive sparse sampling for quasiparticle interference imaging. </p>
Sampled ΔH/Δλ from Non-Adaptive ABFE Calculations
<p>For more information, please see https://github.com/michellab/Automated-ABFE-Paper and the associated work. This data exceeds github's file size limits, but is downloaded when the analysis notebooks in the repo mentioned are run.</p>
MD simulations files for: Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning
<div>Colvar files and related python scripts of Ala2 and Ala10 simulations.</div>
Data from: Fine-tuning the nested structure of pollination networks by adaptive interaction switching, biogeography and sampling effect in the Galápagos Islands
Open the record for dataset details and reuse information.
Increased time sampling in an evolve-and-resequence experiment with outcrossing Saccharomyces cerevisiae reveals multiple paths of adaptive change
<p>"Evolve and resequence" (E&R) studies combine experimental evolution and whole-genome sequencing to interrogate the genetics underlying adaptation. Due to ease of handling, E&R work with asexual organisms like bacteria can employ optimized experimental design, with large experiments and many generations of selection. By contrast, E&R experiments with sexually reproducing organisms are more difficult to implement, and design parameters vary dramatically among studies. Thus, efforts have been made to assess how these differences, such as number of independent replicates, or size of experimental populations, impact inference. We add to this work by investigating the role of time sampling – the number of discrete timepoints sequence data is collected from evolving populations. Using data from an E&R experiment with outcrossing <i>Saccharomyces cerevisiae </i>in which populations were sequenced 17 times over ~540 generations, we address the following questions: (i) do more timepoints improve the ability to identify candidate regions underlying selection? And (ii) does high-resolution sampling provide unique insight into evolutionary processes driving adaptation? We find that while time sampling does not improve the ability to identify candidate regions, high-resolution sampling does provide valuable opportunities to characterize evolutionary dynamics. Increased time sampling reveals three distinct trajectories for adaptive alleles: one consistent with classic population genetic theory (i.e. models assuming constant selection coefficients), and two where trajectories suggest more context-dependent responses (i.e. models involving dynamic selection coefficients). We conclude that while time sampling has limited impact on candidate region identification, sampling 8 or more timepoints has clear benefits for studying complex evolutionary dynamics.</p>
Data from: Accounting for observation processes across multiple levels of uncertainty improves inference of species distributions and guides adaptive sampling of environmental DNA
Understanding factors that influence observation processes is critical for accurate assessment of underlying ecological processes. When indirect methods of detection, such as environmental DNA, are used to determine species presence, additional levels of uncertainty from observation processes need to be accounted for. We conducted a field trial to evaluate observation processes of a terrestrial invasive species (wild pigs- Sus scrofa) from DNA in water bodies. We used a multi-scale occupancy analysis to estimate different levels of observation processes (detection, p): the probability DNA is available per sample (θ), the probability of capturing DNA per extraction (γ), and the probability of amplification per qPCR run (δ). We selected four sites for each of three water body types and collected 10 samples per water body during two months (September and October 2016) in central Texas. Our methodology can be used to guide sampling adaptively to minimize costs while improving inference of species distributions. Using a removal sampling approach was more efficient than pooling samples, and was unbiased. Availability of DNA varied by month, was considerably higher when water pH was near neutral, and was higher in ephemeral streams relative to wildlife guzzlers and ponds. To achieve a cumulative detection probability greater than 90% (including availability, capture, and amplification), future studies should collect 20 water samples per site, conduct at least 2 extractions per sample, and conduct 5 qPCR replicates per extraction. Accounting for multiple levels of uncertainty of observation processes improved estimation of the ecological processes and provided guidance for future sampling designs.
Fig. 3. Pairwise PCA plots for S. marinoi endometabolome samples extracted 24 in Metabolic adaptation of diatoms to hypersalinity
Fig. 3. Pairwise PCA plots for S. marinoi endometabolome samples extracted 24 (A, B) and 96 (C, D) hours after the salinity stress treatment. PCA plots of all data analyzed together (E, F). Panels A, C, and E show results from GC-MS. Panels B, D, and F result from the analysis of LC-MS data; the number of replicates analyzed is 4–5 (see Experimental 5.13.). (For interpretation of the colours in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2. Pairwise PCA plots for P. tricornutum endometabolome samples extracted 24 in Metabolic adaptation of diatoms to hypersalinity
Fig. 2. Pairwise PCA plots for P. tricornutum endometabolome samples extracted 24 (A, B) and 96 (C, D) hours after the salinity stress treatment. PCA plots of all data analyzed together (E, F). Panels A, C, and E show results from GC-MS. Panels B, D, and F result from the analysis of LC-MS data; the number of replicates analyzed is 5. (For interpretation of the colours in this figure legend, the reader is referred to the Web version of this article.)
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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
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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.