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66 results for “Migration modelling”

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zenodo48/100

QuantMig microsimulation population projection model and migration scenarios for 31 European countries

This open data deposit contains the data  and model code of QuantMig-Mic microsimulation population projection model for 31 European countries and accompanies deliverables D8.3: Model outputs for dissemination and D8.1: Microsimulation projection model. This Zenodo deposit contains datasets of model input (baseline population and immigration database) and output data (demography and components output tables) and model code with parameters of the Baseline scenario (termed Default in the model code) deposited in QuantMig_mic.zip file. To view the code the users must first install MODGEN software (or can view code files in Visual Studio). All scenarios share the same parameters except the immigrant population - to change the immigration assumptions the users can import immigration assumptions for any other scenario from the ImmigDataBase.csv and change it in the immigration module using the MODGEN user interface or using Visual Studio. The file structure and codebook for the data files is included in the cover note file "readme_quantmig_datasets.pdf" Detailed information about the QuantMig-Mic microsimulation model, its modules and parameters: Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf Instructions how to install MODGEN can be found in: Marois, G. and Potančoková, M. (2022) QuantMig-mic microsimulation tool. QuantMig Project Deliverable D8.1. International Institute for Applied Systems Analysis (IIASA). http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.1%20v1.1.pdf  Detailed information about the QuantMig migration scenarios can be found in: Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf A guide to the datasets and the codebook can be found in: readme_quantmig_datasets.pdf Countries included in the model: Austria, Belgium, Bulgaria, Croatia, Czechia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom

opencc-by-4.0Aug 2023View details →

zenodo44/100

Logical model for Molecular Pathways Enabling Tumour Cell Invasion and Migration

Understanding the etiology of metastasis is very important in clinical perspective, since it is estimated that metastasis accounts for 90% of cancer patient mortality. Metastasis results from a sequence of multiple steps including invasion and migration. The early stages of metastasis are tightly controlled in normal cells and can be drastically affected by malignant mutations; therefore, they might constitute the principal determinants of the overall metastatic rate even if the later stages take long to occur. To elucidate the role of individual mutations or their combinations affecting the metastatic development, a logical model has been constructed that recapitulates published experimental results of known gene perturbations on local invasion and migration processes, and predict the effect of not yet experimentally assessed mutations. The model has been validated using experimental data on transcriptome dynamics following TGF-β-dependent induction of Epithelial to Mesenchymal Transition in lung cancer cell lines. A method to associate gene expression profiles with different stable state solutions of the logical model has been developed for that purpose. In addition, we have systematically predicted alleviating (masking) and synergistic pairwise genetic interactions between the genes composing the model with respect to the probability of acquiring the metastatic phenotype. We focused on several unexpected synergistic genetic interactions leading to theoretically very high metastasis probability. Among them, the synergistic combination of Notch overexpression and p53 deletion shows one of the strongest effects, which is in agreement with a recent published experiment in a mouse model of gut cancer. The mathematical model can recapitulate experimental mutations in both cell line and mouse models. Furthermore, the model predicts new gene perturbations that affect the early steps of metastasis underlying potential intervention points for innovative therapeutic strategies in oncology.   Included files: <ul> <li>Master Model: the model includes detailed regulation of the major players involved in the crosstalks between Notch and p53 pathways</li> <li>Modular Model: the model is a reduction of the master model. To reduce the master model, we lumped together some entities that belonged to a module.</li> </ul>

opencc-by-4.0Nov 2015View details →

zenodo44/100

Data from: Estimation in the multinomial reencounter model - Where do migrating animals go and how do they survive in their destination area?

Abstract 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 Erithacus rubecula 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.

opencc-by-4.0Apr 2022View details →

zenodo44/100

Diffusion coefficients on amorphous polystyrene and modelling of migration levels from plastic packaging

This dataset is actually supplementary data of the scientific article: Martinez-Lopez, Brais; Gontard, Natalie and Peyron, Stephane "Worst case prediction of additives migration from polystyrene for food safety purposes: a model update" in Food Additives and Contaminants Part A, doi:10.1080/19440049.2017.1402129. If you use it, please cite it using the reference file we have provided. This description is the same as in the file "readme.txt", included in the upload. List of files: <ul> <li>The file database_D contains the experimental diffusivity data for amorphous polystyrene used for the figure 1b. It is a spreadsheet file with two tabs. In the first tab, the diffusion coefficients can be found by choosing molecule family (and the publication were they were found) and temperature in celsius degrees. The second tab contains the same diffusivity data, but they are ranged by increasing molecular weight and temperature. This file is available in open document (.ods) and microsoft excel (.xlsx) formats.</li> <li>The file migration modelling is also a spreadsheet file, and contains several tabs. The first tab (diffusion coefficient) is an implementation of equation 1, the predictive model for overestimated diffusion coefficients. The given Ap and tau parameter sets are the ones specified in Table 2 for amorphous polystyrene. The second tab (migration levels) is an implementation of equation 3, the solution to Fick's second law that is used to predict migration levels in food, for pre-selected values of alpha (equation 5). The tabs labeled alpha =... contain the sums used in the equation, whereas the tab "roots" contains the first 200 roots of trascendental equation 4, needed to calculate the sum or terms. This file is also available in open document (.ods) and microsoft excel (.xlsx) formats.</li> <li>The file "table.pdf" sums the main characteristics of the molecule families, together with the references where they were found (in the second page).</li> <li>The file reference.bib contains the reference that should be cited if you use this dataset for your own work.</li> <li>Finally, the file readme.txt contains this very same description.</li> </ul> These files have undergone thorough check, so there should not be any mistakes. In the rare event that you find one, please report it to the author so it can get fixed. bramar@food.dtu.dk Brais Martínez López, PhD Assistant professor DTU Fødevareinstituttet Danmarks Tekniske Universitet Søltofts Plads Bygning 227 2800 Kgs. Lyngby

opencc-by-4.0Aug 2017View details →

zenodo40/100

Dataset about An Exploratory Framework of Land-Sea Movement Model for Early Austronesians Migration

Dataset about An Exploratory Framework of Land-Sea Movement Model for Early Austronesians Migration https://zenodo.org/records/14997527

opencc-by-4.0Aug 2024View details →

zenodo40/100

Data and code related to publication "Migration pulsedness alters patterns of allele fixation and local adaptation in a mainland-island model" - Aubree et al. 2021

Those data sets and codes are related to the manuscript "Migration pulsedness alters patterns of allele fixation and local adaptation in a mainland-island model" available on BioRXiv. All the information that are necessary to use those data sets and codes are contained in the file "readme.txt".

opencc-by-4.0Jun 2021View details →

zenodo40/100

Simulated datasets from modelling demographic events and migration patterns

These are datasets generated from multi-state model (MSM) project on understanding demographic events and migration patterns in two urban slums of Nairobi City in  Kenya at the African Population and Health Research Center (APHRC). The project focuses on using MSM techniques to analyze residence demographic events in Nairobi urban slums, with an emphasis on key events such as: <ul> <li>Births</li> <li>Deaths</li> <li>Migration (in-migration and out-migration)</li> <li>Changes in residence status (exit and entry)</li> </ul> The primary aim of these datasets is to allow those who want to understand and model the demographic transitions in Nairobi's informal settlements, identifying factors that influence residence changes over time.

opencc-by-4.0Oct 2024View details →

zenodo40/100

Water migration through enzyme tunnels is sensitive to the choice of explicit water model (DhaA)

This repository contains data for the haloalkane dehalogenase DhaA. Data underpinning analyses of alditol oxidase (AldO) and cytochrome P450 2D6 (CYP2D6) are available from the related repository: <a href="https://doi.org/10.5281/zenodo.11545455">https://doi.org/10.5281/zenodo.11545455</a>   Content: tt_conda.yml -> conda environment used for the calculations.          Usage :          conda env create -f tt_conda.yml          conda activate tt_conda.yml  01_MD_simulations.tar.gz -> the files to run simulation, out and restart files from simulation and simulation analysis results, organized by models and Tunnel Conformational Groups (TCGs). ├── 01_inputs │   ├── opc │   │   ├── TCG_d1.0_o1.1 │   │   ├── TCG_d1.4_o1.2 │   │   ├── TCG_d1.8_o1.4 │   │   ├── TCG_d2.5_o2.1 │   │   └── TCG_d3.0_o2.5 │   ├── scripts │   ├── tip3p │   │   ├── TCG_d1.0_o1.1 │   │   ├── TCG_d1.4_o1.2 │   │   ├── TCG_d1.8_o1.4 │   │   ├── TCG_d2.5_o2.1 │   │   └── TCG_d3.0_o2.5 │   └── tip4pew │       ├── TCG_d1.0_o1.1 │       ├── TCG_d1.4_o1.2 │       ├── TCG_d1.8_o1.4 │       ├── TCG_d2.5_o2.1 │       └── TCG_d3.0_o2.5 ├── 02_outputs │   ├── opc │   │   ├── TCG_d1.0_o1.1 │   │   ├── TCG_d1.4_o1.2 │   │   ├── TCG_d1.8_o1.4 │   │   ├── TCG_d2.5_o2.1 │   │   └── TCG_d3.0_o2.5 │   ├── tip3p │   │   ├── TCG_d1.0_o1.1 │   │   ├── TCG_d1.4_o1.2 │   │   ├── TCG_d1.8_o1.4 │   │   ├── TCG_d2.5_o2.1 │   │   └── TCG_d3.0_o2.5 │   └── tip4pew │       ├── TCG_d1.0_o1.1 │       ├── TCG_d1.4_o1.2 │       ├── TCG_d1.8_o1.4 │       ├── TCG_d2.5_o2.1 │       └── TCG_d3.0_o2.5 └── 03_analysis               02_caver.tar.gz -> results of CAVER calculations, organized by models and TCGs. ├── config_files ├── opc │   ├── TCG_d1.0_o1.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.4_o1.2 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.8_o1.4 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d2.5_o2.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── TCG_d3.0_o2.5 │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 ├── tip3p │   ├── TCG_d1.0_o1.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.4_o1.2 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.8_o1.4 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d2.5_o2.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── TCG_d3.0_o2.5 │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 └── tip4pew     ├── TCG_d1.0_o1.1     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d1.4_o1.2     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d1.8_o1.4     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d2.5_o2.1     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     └── TCG_d3.0_o2.5         ├── 1         ├── 2         ├── 3         ├── 4         └── 5 03_aquaduct.tar.gz -> results of AQUA-DUCT calculations, organized by models and TCGs.   ├── opc │   ├── TCG_d1.0_o1.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.4_o1.2 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.8_o1.4 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d2.5_o2.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── TCG_d3.0_o2.5 │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 ├── tip3p │   ├── TCG_d1.0_o1.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.4_o1.2 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.8_o1.4 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d2.5_o2.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── TCG_d3.0_o2.5 │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 └── tip4pew     ├── TCG_d1.0_o1.1     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d1.4_o1.2     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d1.8_o1.4     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d2.5_o2.1     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     └── TCG_d3.0_o2.5         ├── 1         ├── 2         ├── 3         ├── 4         └── 5            04_transport_tools.tar.gz -> the results of TransportTools and analysis done from TransportTools results. ├── bottleneck_analyses │   ├── data │   │   └── super_clusters │   ├── statistics │   │   └── comparative_analysis │   └── visualization │       ├── comparative_analysis │       └── sources ├── overall_results │   ├── data │   │   ├── exact_matching_analysis │   │   └── super_clusters │   ├── statistics │   │   └── comparative_analysis │   └── visualization │       ├── comparative_analysis │       └── sources └── scripts     ├── bottleneck_residues     ├── presence_of_tunnels     └── water_transport_analysis         └── output 05_hbonds.tar.gz -> contains raw results of hydrogen bond analysis of transported waters for P1 tunnel of DhaA     06_control_NVE_calculations.tar.gz -> data obtained from NVE simulations of DhaA TCG o1.4d1.8 and files necessary to reproduce ├── 01_MD_simulations │   ├── 01_inputs │   │   ├── configs │   │   │   ├── NVE_equil.in │   │   │   ├── NVE_prod.in │   │   │   ├── NVT_cool.in │   │   │   └── NVT_equil.in │   │   ├── opc │   │   ├── tip3p │   │   └── tip4pew │   └── 02_outputs │       ├── opc │       ├── tip3p │       └── tip4pew ├── 02_caver │   ├── configs │   │   ├── calculate_tunnels.txt │   │   └── clustering.txt │   ├── opc │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── tip3p │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── tip4pew │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 ├── 03_aquaduct │   ├── opc │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── tip3p │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── tip4pew │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 └── 04_transport_tools     ├── data     ├── statistics     ├── transport_tools.log     ├── tt_config.in     └── visualization

opencc-zeroOct 2024View details →

zenodo40/100

Dataset part one to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"

This study was supported in parts by research funding from the Swiss National Science Foundation (grant number 310030_189144), the Thurgauische Stiftung für Wissenschaft und Forschung, and the State Secretariat for Education, Research and Innovation to DFL.

opencc-by-4.0Sep 2021View details →

zenodo40/100

Upslope migration of snow avalanches in a warming climate: data and model source files

Complete data and model source files corresponding to: Giacona, F., Eckert, N., Corona, C., Mainieri, R., Morin, S., Stoffel, M., Martin, B., Naaim, M. (2021). Upslope migration of snow avalanches in a warming climate. Proceedings of the National Academy of Sciences America, Nov 2021, 118 (44) e2107306118; DOI: 10.1073/pnas.2107306118

opencc-by-4.0Oct 2021View details →

dryad40/100

Code and initial metapopulation data for model construction and simulation analyses for: Genetic rescue from protected areas is modulated by migration, hunting rate and timing of harvest

Migrants from protected areas may buffer the risk of harvest-induced evolutionary changes in exploited populations that face strong selective harvest pressures in both terrestrial and marine ecosystems. Understanding the mechanisms favouring genetic rescue through migration could help ensure sustainable harvest outside protected areas and conserve genetic diversity inside those areas. We developed a stochastic individual-based metapopulation model to evaluate the potential for migration from protected areas to mitigate the evolutionary consequences of selective harvest. We parameterized the model with detailed data from individual monitoring of two populations of bighorn sheep subjected to trophy hunting. We tracked horn length through time in a metapopulation including large protected and trophy-hunted populations connected through male breeding migrations. We quantified and compared declines in horn length and rescue potential under various combinations of migration rate, hunting rate in hunted areas and temporal overlap in timing of harvest and migrations, which affects the migrants' survival and chances to breed within exploited areas. Our simulations suggest that the effects of size-selective harvest on male horn length in hunted populations can be dampened or avoided if harvest pressure is low, migration rate is substantial, and migrants have a low risk of being shot. Intense size-selective harvest impacts the phenotypic and genetic diversity in horn length, and population structure through changes in proportions of large-horned males, sex ratio and age structure. When hunting pressure is high and overlaps with male migrations, effects of selective removal also emerge in the protected population, so that instead of a genetic rescue of hunted populations, our model predicts undesirable effects inside protected areas. Our results stress the importance of a metapopulational approach to management, to promote genetic rescue from protected areas and limit ecological and evolutionary impacts of harvest on both harvested and protected populations.

opencc-zeroApr 2023View details →

zenodo40/100

Numerical model and natural river data for the timescale analysis of meandering channel migration

This is the archive of the numerical model and river centerline data used for analyzing the timescale related to meandering channel migration, which is tied to the manuscript submitted to Journal of Geophysical Research: Earth Surface: Li, Y., and Limaye, A. B., Timescale of the morphodynamic feedback between planform geometry and lateral migration of meandering rivers. Running this model needs a MATLAB® software environment. The model can be launched by the wrapper scripts saved under the folder "software code/example wrappers". The wrapper script called "wrapper01a_channelOnly_runModel.m" is used to generate all model simulations in this study.

opencc-by-4.0Aug 2023View details →

dryad40/100

Hierarchical heuristic species delimitation under the multispecies coalescent model with migration

The multispecies coalescent (MSC) model accommodates genealogical fluctuations across the genome and provides a natural framework for comparative analysis of genomic sequence data to infer the history of species divergence and gene flow. Given a set of populations, hypotheses of species delimitation (and species phylogeny) may be formulated as instances of MSC models (e.g., MSC for one species versus MSC for two species) and compared using Bayesian model selection. This approach, implemented in the program bpp, has been found to be prone to over-splitting. Alternatively, heuristic criteria based on population parameters under the MSC model (such as population/species divergence times, population sizes, and migration rates) estimated from genomic sequence data may be used to delimit species. Here we extend the approach of species delimitation using the genealogical divergence index (𝑔𝑑𝑖) to develop hierarchical merge and split algorithms for heuristic species delimitation and implement them in a python pipeline called hhsd. Applied to data simulated under a model of isolation by distance, the approach was able to recover the correct species delimitation, whereas model comparison by bpp failed. Analyses of empirical datasets suggest that the procedure may be less prone to over-splitting. We discuss possible strategies for accommodating paraphyletic species in the procedure, as well as the challenges of species delimitation based on heuristic criteria.

opencc-zeroSep 2023View details →

dryad40/100

Data from: Evaluating migration hypotheses for the extinct Glyptotherium using Ecological Niche Modeling

Open the record for dataset details and reuse information.

publicFeb 2025View details →

dryad40/100

Hierarchical heuristic species delimitation under the multispecies coalescent model with migration

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publicSep 2024View details →

dryad40/100

Code and initial metapopulation data for model construction and simulation analyses for: Genetic rescue from protected areas is modulated by migration, hunting rate and timing of harvest

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publicApr 2023View details →

dryad40/100

Data from: Linking continuous and discrete models of cell birth and migration

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publicMay 2024View details →

dryad36/100

Individual variability and versatility in an eco-evolutionary model of avian migration

Seasonal migration is a complex and variable behavior with the potential to promote reproductive isolation. In Eurasian blackcaps (Sylvia atricapilla), a migratory divide in central Europe separating populations with southwest and southeast autumn routes may facilitate isolation, and individuals using new wintering areas in Britain show divergence from Mediterranean winterers. We tracked 100 blackcaps in the wild to characterize these strategies. Blackcaps to the west and east of the divide used predominantly SW and SE directions, respectively, but close to the contact zone many individuals took intermediate (S) routes. At 14.0ºE, we documented a sharp transition from SW to SE migratory directions across only 27 (10–86) km, implying a strong selection gradient across the divide. Blackcaps wintering in Britain took northwesterly migration routes from continental European breeding grounds. They originated from a surprisingly extensive area, spanning 2000 km of the breeding range. British winterers bred in sympatry with SW-bound migrants but arrived 9.8 days earlier on the breeding grounds, suggesting some potential for assortative mating by timing. Overall, our data reveal complex variation in songbird migration and suggest that selection can maintain variation in migration direction across short distances while enabling the spread of a novel strategy across a wide range.

opencc-zeroOct 2020View details →

dryad36/100

Divergence time estimation using ddRAD data and an isolation-with-migration model applied to water vole populations of Arvicola

Molecular dating methods of population splits are crucial in evolutionary biology, but they present important difficulties due to the complexity of the genealogical relationships of genes and past migrations between populations. Using the double digest restriction-site associated DNA (ddRAD) technique and an isolation-with-migration (IM) model, we studied the evolutionary history of water vole populations of the genus Arvicola, a group of complex evolution with fossorial and semi-aquatic ecotypes. To do this, we first estimated mutation rates of ddRAD loci using a phylogenetic approach. An IM model was then used to estimate split times and other relevant demographic parameters. A set of 300 ddRAD loci that included 85 calibrated loci resulted in good mixing and model convergence. The results showed that the two populations of A. scherman present in the Iberian Peninsula split 34 thousand years ago, during the last glaciation. In addition, the much greater divergence from its sister species, A. amphibius, may help to clarify the controversial taxonomy of the genus. We conclude that this approach, based on ddRAD data and an IM model, is highly useful for analyzing the origin of populations and species.

opencc-zeroMar 2022View details →

zenodo36/100

Dataset part two to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"

Additional dataset to dataset part one (doi: 10.5281/zenodo.4719596) to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"

opencc-by-4.0Sep 2021View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record