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42 results for “Migration dataset”
Bird migration case study dataset v1.1
<p>Bird migration case study dataset, updated during the WG3 workshop ‘Visualisations: from show cases to production’ in 2015 by @peterdesmet.</p> <p>Changeset</p> <ul> <li>Add aggregation instructions for bird migration altitude profiles</li> <li>Add instructions to create basemap</li> <li>Add forward trajectory data</li> <li>Change license to CC0</li> <li>Update README & documentation where necessary</li> </ul>
Multi-aspect Integrated Migration Indicators (MIMI) dataset
<p>Nowadays, new branches of research are proposing the use of non-traditional data sources for the study of migration trends in order to find an original methodology to answer open questions about cross-border human mobility. The Multi-aspect Integrated Migration Indicators (MIMI) dataset is a new dataset to be exploited in migration studies as a concrete example of this new approach. It includes both official data about bidirectional human migration (traditional flow and stock data) with multidisciplinary variables and original indicators, including economic, demographic, cultural and geographic indicators, together with the Facebook Social Connectedness Index (SCI). It is built by gathering, embedding and integrating traditional and novel variables, resulting in this new multidisciplinary dataset that could significantly contribute to nowcast/forecast bilateral migration trends and migration drivers.</p> <p>Thanks to this variety of knowledge, experts from several research fields (demographers, sociologists, economists) could exploit MIMI to investigate the trends in the various indicators, and the relationship among them. Moreover, it could be possible to develop complex models based on these data, able to assess human migration by evaluating related interdisciplinary drivers, as well as models able to nowcast and predict traditional migration indicators in accordance with original variables, such as the strength of social connectivity. Here, the SCI could have an important role. It measures the relative probability that two individuals across two countries are friends with each other on Facebook, therefore it could be employed as a proxy of social connections across borders, to be studied as a possible driver of migration. </p> <p>All in all, the motivations for building and releasing the MIMI dataset lie in the need of new perspectives, methods and analyses that can no longer prescind from taking into account a variety of new factors. The heterogeneous and multidimensional sets of data present in MIMI offer an all-encompassing overview of the characteristics of human migration, enabling a better understanding and an original potential exploration of the relationship between migration and non-traditional sources of data.</p> <p> </p> <p>The MIMI dataset is made up of one single CSV file that includes 28,821 rows (records/entries) and 876 columns (variables/features/indicators). Each row is identified uniquely by a pairs of countries, built from the joining of the two ISO-3166 alpha-2 codes for the origin and destination country, respectively. The dataset contains as main features the country-to-country bilateral migration flows and stocks, together with multidisciplinary variables measuring cultural, demographic, geographic and economic variables for the two countries, together with the Facebook strength of connectedness of each pair. </p> <p> </p> <p><strong>Related paper: </strong>Goglia, D., Pollacci, L., Sirbu, A. (2022). Dataset of Multi-aspect Integrated Migration Indicators. <a href="https://doi.org/10.5281/zenodo.6500885">https://doi.org/10.5281/zenodo.6500885</a></p>
Dataset from: Spatially heterogeneous shifts in vegetation phenology induced by climate change threaten the integrity of the avian migration network
<p>Original data and code for the study:</p> <p>Wei, J., Xu, F., Cole, E. F., Sheldon, B. C., de Boer, W. F., Wielstra, B., Fu, H., Gong, P., & Si, Y. (2024, Accepted). Spatially heterogeneous shifts in vegetation phenology induced by climate change threaten the integrity of the avian migration network. Global Change Biology.</p> <p>The dataset mainly contains data showing the climate change-induced heterogeneous shifts in vegetation phenology and the migration integrity change from 2000 to 2020 for 16 Asian herbivorous waterfowl species. These data were derived from the following resources available in the public domain.</p> <p>The Global Lakes and Wetlands Database is available from “https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database”. The global land cover datasets are available from European Space Agency (ESA) Climate Change Initiative (CCI) products, “https://maps.elie.ucl.ac.be/CCI/viewer/download.php”. The Global Multi-resolution Terrain Elevation Data are available from “https://www.usgs.gov/centers/eros/science/terrain-monitoring-and-modeling”. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance product is available from “https://modis.gsfc.nasa.gov/data/dataprod/mod09.php”. The bird distribution maps are available from Birdlife International, “https://www.birdlife.org/”. The bird foraging attribute data are available from EltonTraits 1.0, “https://figshare.com”. The bird occurrence data are available from eBird Basic Dataset (EBD), “https://science.ebird.org/en/use-ebird-data/download-ebird-data-products”. The Hackett backbone phylogenetic trees are available from “https://birdtree.org/”.</p> <p>The code contains the R scripts and MATLAB scripts that we used for this study.</p> <p>For details please see the file “Readme.txt”, and the research paper.</p>
Dataset for 'Meteorological Conditions Influence the Migration of a Marine Dune Field in the Southern North Sea'
<p>This dataset complements the paper 'Meteorological Conditions Influence the Migration of a Marine Dune Field in the Southern North Sea' accepted <span>for publication in Journal of Geophysical Research - Earth Surface</span>.</p> <p> </p> <p>This dataset contains Digital Terrain Maps (DTMs) of specific areas offshore from Dunkirk, on the northern coast of France, opening to the Southern Bight of the North Sea. These areas host marine dunes (sand waves) that have been numerically investigated in this research to identify the parameters influencing their migration. The openTELEMAC system (version v8p4) can be downloaded from <a href="https://opentelemac.org/">https://opentelemac.org/</a>. The model development, calibration and validation is described in Durand (2024).</p> <p> </p> <p>The site-specific data collected by France Energies Marines (2021) are currently proprietary. To protect these data, DTMs of bathymetric changes are provided, calculated as the difference in metres between the final and initial seabed levels. Negative values indicate lowering of the seabed (erosion) and positive values indicate rising (accretion).</p> <p>The initial and final periods are:</p> <ul> <li>S1: 17-Nov-2019</li> <li>S2: 17-Mar-2020</li> <li>S5: 5-Dec-2020</li> </ul> <p>The DTMs are provided for two areas (refer to paper for locations):</p> <ul> <li>Tile #1</li> <li>Tile #3</li> </ul> <p> </p> <p>Included in the dataset are observations, Case I model output (without wind and atmospheric pressure), and Case II model output (with wind and atmospheric pressure).</p>
Comparative Dataset on Migration
<p>The purpose of this dataset is to provide a systematic set of standardised contextual (economic, socio-political, cultural and legal) indicators in order to identify and measure on a comparative basis those contextual factors that have an (beneficial or inhibiting) impact on European, but not exclusively, responses to mass migration. Attention has been paid to existing socio-economic conditions and to national policies related to immigrants and asylum seekers. In this respect, the dataset comprises a set of both macro-level indicators measuring the socio-economic, political and institutional context of migration and cultural – or individual-level – indicators addressing ordinary citizens’ subjective attitudes, behaviours and perceptions about migration related-phenomena (e.g. perceived discrimination on ethnic grounds; immigration being bad or good for a country's economy; a country's cultural life being undermined or enriched by immigration).</p>
MMoveT15: A Twitter Dataset for Extracting and Analysing Migration-Movement Data of the European Migration Crisis 2015
<p>In the 2015 migration crisis thousands of refugees and migrants crossed the border to Hungary, Austria and Germany. The movements of these people are reflected in social media, especially on Twitter. We present a dataset of 3275 Tweets form the months September and October 2015. These Tweets are annotated regarding their relevance to the quantitative movement of refugees/migrants into Hungary, Austria and Germany. We present this dataset for a posterior analysis of the 2015 migration crisis or as a basis for an early warning or forecasting system</p>
Source code and simulation datasets for the paper 'Migration and accumulation of bacteria with chemotaxis and chemokinesis'
<p>Source code and simulation data files for the paper 'Migration and accumulation of bacteria with chemotaxis and chemokinesis'</p> <p>The zipped folder 'SimulationCode.zip' contains Matlab source code files which have been used to generate the simulations in the paper: fixed attractant gradient, axisymmetric agar-plate like migration and transient attractant source. The file 'main.m' controls all simulations run with initial conditions specified in the files with suffix '_ic'. The file 'PDEsolver.m' specifies the finite difference solver used so solve the model PDEs, while 'FourthOrderFD.m' creates the matrices that are required<br> for the finite differnce solver. The chosen scheme is of fourth order accuracy.</p> <p>The zipped folder 'SimulationData.zip' contains Matlab data files generated by running the simulation code. The results correspont to the figures in the paper.</p>
Migration and tuberculosis in Berlin (dataset)
<p>This is the supporting data file for the manuscript entitled "Higher rate of tuberculosis in second generation migrants compared to native residents in a metropolitan setting in Western Europe" (Marx et al., PLoS ONE). The dataset includes anonymized, routinely collected notification data (variables labeled as "nd") for 314 individuals and anonymized survey data (i.e. data obtained through interviews; variables labeled as "sd") for a subset of 154 individuals. The data are published open-access, in accordance with the PLoS ONE data policy (2014).</p>
Time trees and Clock genes: a Systematic Review and Comparative Analysis of Contemporary Avian Migration Genetics (Dataset)
<p>Complete dataset of <em>Clock</em> and <em>Adcyap1</em> alleles, distance matrices, and migration data used in the review and meta-analysis "<strong>Time trees and Clock genes: a Systematic Review and Comparative Analysis of Contemporary Avian Migration Genetics".</strong> </p>
Dataset for paper "Freihardt (2025): Environmental shocks and migration among a climate-vulnerable population in Bangladesh. Population and Environment. DOI 10.1007/s11111-025-00478-7"
<p>This is the dataset underlying the paper: </p> <p>Freihardt, J. (2025): Environmental shocks and migration among a climate-vulnerable population in Bangladesh. Population and Environment, 47, 6. DOI: 10.1007/s11111-025-00478-7.</p>
[wrong version, find the dataset here: https://doi.org/10.5281/zenodo.13789648] Migration and Expulsion in the Reichsgau Wartheland
<p>Find the dataset here: https://doi.org/10.5281/zenodo.13789648</p>
Dataset about An Exploratory Framework of Land-Sea Movement Model for Early Austronesians Migration
<p>Dataset about An Exploratory Framework of Land-Sea Movement Model for Early Austronesians Migration https://zenodo.org/records/14997527</p>
Nocturnal flight calls dataset: long-term acoustic monitoring of birds migrating at night
<p><strong>General Description:</strong></p> <p>This is a development set used in the experiments in the Ph.D. thesis: "Nowe metody akustycznej identyfikacji ptaków migrujących nocą" (<em>"Novel methods of acoustic identification of birds migrating at night"</em>) by Hanna Pamula. The project focuses on the detection (and - partially - classification) of passerine birds' calls from long-term audio recordings collected during bird autumn migration between 2016 and 2019. The dataset consists of >56,5 hours of recordings with annotations of nocturnal flight calls of passerine birds migrating along the Baltic Sea coast, Poland.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p>Development_Set_3.1.zip</p> <p>|_Development_Set_3.1/</p> <p> |__Training_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Validation_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Testing_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p>Training Set: 86 recordings</p> <p>Validation Set: 8 recordings</p> <p>Testing set: 18 recordings (BUT: uploaded 20 recordings, as in the previous version of the dataset - version 3, two additional recordings were used. Then, they were deleted in the final version of development set 3.1. Two additional recordings are: 'BUK5_20161101_002104a and BUK5_20161101_002104b)</p> <p>Names of waveforms and annotations are matching.</p> <p><strong>Waveforms:</strong></p> <p>The whole dataset consists of 114 recordings. One hundred thirteen recordings are about 30 minutes long (29min56s – 29min 59s), one recording is 1min20s. All data were recorded at 44,100 Hz sampling rate, one channel, with SM2 Wildlife Acoustics recorders + SMX-NFC microphone. The recording sessions were performed at night (starting time and date denoted in a file name) on the Baltic Sea coast in Poland (Dąbkowice, near Darłowo).</p> <p><strong>Annotations:</strong></p> <p>Transcriptions were produced using Audacity 2.4.1: https://www.audacityteam.org/ by an experienced birdwatcher, Hanna Pamula. While every effort has been made to ensure the quality and accuracy of the labels, some errors may occur, taking into account the difficulty of nocturnal call recognition and transcription tasks in general.</p> <p>Transcription format:</p> <p>[Starting time (sec)] [Ending time (sec)] [Label]</p> <p><strong>Meaning of the labels:</strong></p> <p>1. Positive classes – migrating passerine birds:</p> <ul> <li>'s' – song thrush call (Turdus philomelos)</li> <li>'k' – blackbird call (Turdus merula)</li> <li>'d' – redwing call (Turdus iliacus)</li> <li>'r' – robin call (Erithacus rubecula)</li> <li>‘kwiczol’ – fieldfare call (Turdus pilaris)</li> <li>‘skowronek’ – skylark call (Alauda arvensis)</li> <li>Each of the above labels could also have a question mark '?', e.g. 'r?', 'k?' – meaning that it's not a sure label. In a bird call detection task, they are regarded as positive chunks containing bird call(s).</li> <li>'ni' – non identified bird call (distant/quiet/not recognized)</li> </ul> <p>Only the supposed calls of migrating passerine birds were labeled; other sounds of species were ignored (e.g., robin's tik-calling, which can be often heard at dusk, and may be regarded as warning sounds).</p> <p>2. Negative classes – other marked sound events:</p> <ul> <li>'g' – other bird calls/songs/sounds. Sounds that could confuse the model; for example, sounds of migrating geese, cranes, plovers calls, etc.</li> <li>'gh' – human voices</li> <li>'t' – cracks, clicks, raindrops, other noise</li> <li>‘puszczyk’ – tawny owl voice (Strix aluco)</li> <li>'czapla' – grey heron voice (Ardea cinerea)</li> </ul> <p>Not all occurrences of the negative sounds were labeled – only some chosen examples to represent the possible noises/negative samples. Thus these annotations can't be used for entirely different detection / classification tasks than intended, e.g., detecting migrating cranes or human voices in long-term recordings.</p> <p>3. Labels to be excluded from analysis:</p> <ul> <li>'???', '??? mysz', '??? high freq' – unknown, not sure if the sound event is a birds' call or not. Uncertainty about belonging to a positive/negative class in the detection task.</li> </ul>
Dataset of answers to the Questionnaire "Climate changes, migrations, security: the role of Municipalities"
<p>Results of a statistical questionnaire addressed to Sardinian Mayors in order to understand the Local Authorities' position with regard to migration flows, security and their nexus with climate change.</p>
Simulated datasets from modelling demographic events and migration patterns
<p>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:</p> <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> <p>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. </p>
Dataset part one to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"
<p>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.</p>
Migration Provisions in Preferential Trade Agreements (MITA) Dataset
<p>The MITA dataset is the broadest and most inclusive databases on preferential trade agreements with regard to migration. We include all international trade agreements that were signed between 1960 and 2020 and whose text is publicly available. This includes bilateral, plurilateral and regional agreements. We exclude framework agreements. We do not list accessions and withdrawals to existing agreements as they only constitute a change in the signing parties but not a change in an agreement’s provisions. Accordingly, we include consolidated PTAs that include additional migration provisions to the initial agreement. Signing parties can be countries but also group of countries such as regional and supranational organizations. Beside the main text of an agreement, we also take into account annexes and side-letters that are part of an agreement. To maximize the coverage of the database, we combined various data sources such as the World Trade Organization, the DESTA database (Dür et al., 2014) and websites of governments and intergovernmental organizations. As source data, we consider all text documents that are an integrated part of an agreement, such as the main text, annexes, side letters and additional protocols that were signed together with the main agreement.</p>
WHiM-BC Dataset: Evolution of CSCs and non CSCs MCF-7 Migration in Wound Healing Assay
<p>The WHiM-BC Dataset has been utilized to train Deep Learning models for the prediction of migration capabilities of MCF-7 cells, facilitated by the <a href="https://github.com/frangam/wound-healing">Predicting Wound Healing Progress Framework (PWPF)</a>.</p> <p>The software can be accessed on GitHub: <a href="https://github.com/frangam/wound-healing">https://github.com/frangam/wound-healing</a>.</p> <p>Or you can download from Zenodo: <a href="https://doi.org/10.5281/zenodo.8130984">https://doi.org/10.5281/zenodo.8130984</a></p> <p>The dataset comprises two distinct parts:</p> <p>MCF-7 Monolayer Cells: This part includes photographs of 12 different wells taken at 0h, 3h, 6h, 9h, 12h, 24h, and 27h, marking the final time of wound closure.</p> <p>MCF-7 Spheres: This portion of the dataset mirrors the first, with images of 12 wells captured at the following time intervals: 0h, 3h, 6h, 9h, 12h, and 15h, indicating the closure time.</p> <p>The dataset consists of a total of (12<em>7) for the MCF-7 Monolayer Cells and (12</em>6) for the MCF-7 Spheres, amounting to a sum of photographs from both parts.</p> <p> </p> <p>If you utilize this tool in your research, please acknowledge it by citing the following reference:</p> <p><br><br>@article{Garcia-Moreno-PWPF,<br> title={Using Deep Learning for Predicting the Dynamic Evolution of Breast Cancer Migration},<br> author={Garcia-Moreno, Francisco Manuel and Ruiz-Espigares, Jesus and Marchal, Juan Antonio and Gutierrez-Naranjo, Miguel Angel},<br> year={2024},<br> journal={Computers in Biology and Medicine},<br> doi={10.1016/j.compbiomed.2024.108890},<br> note={\url{https://authors.elsevier.com/tracking/article/details.do?aid=108890&jid=CBM&surname=Garcia-Moreno}}<br>}</p> <p>And also cite our MCF-7 Dataset used to train our software:</p> <p><br>@misc{WHiM-BC_Dataset,<br> title={WHiM-BC Dataset: Evolution of CSCs and non CSCs MCF-7 Migration in Wound Healing Assay},<br> author={Garcia-Moreno, Francisco Manuel and Ruiz-Espigares, Jesus and Marchal, Juan Antonio and Gutiérrez-Naranjo, Miguel Ángel},<br> year={2023},<br> doi={10.5281/zenodo.8131123},<br> url={https://doi.org/10.5281/zenodo.8131123},<br> note = {version 1.0}<br>}<br><br></p>
Teleseismic reverse time migration image dataset of southwest Japan in "Three-dimensional teleseismic elastic reverse-time migration with deconvolution imaging condition and its application to southwest Japan"
<p>This dataset contains the teleseismic elastic reverse time migration results of southwest Japan used in the manuscript entitled "Three-dimensional teleseismic elastic reverse-time migration with deconvolution imaging condition and its application to southwest Japan". submitted to Journal of Geophysical Research Letters.</p>
Data from: Forecasting nocturnal bird migration for dynamic aeroconservation: the value of short-term dataset
<p>Placing wind turbines within large migration flyways, such as the North Sea basin, can contribute to the decline of vulnerable migratory bird populations by increasing mortality through collisions. Curtailment of wind turbines limited to short periods with intense migration can minimize these negative impacts, and near-term bird migration forecasts can inform such decisions. Although near-term forecasts are usually created with long-term datasets, the pace of environmental alteration due to wind energy calls for urgent development of conservation measures that rely on existing data, even when it does not have long temporal coverage. Here, we use five years of tracking bird radar data collected off the western Dutch coast, weather, and phenological variables to develop seasonal near-term forecasts of low-altitude nocturnal bird migration over the southern North Sea. Overall, the models explained 71% of the variance and correctly predicted migration intensity above or below a threshold for intense hourly migration in more than 80% of hours in both seasons. However, the percentage of correctly predicted intense migration hours (top 5% of hours with the most intense migration) was low, likely due to the short-term dataset and their rare occurrence. We, therefore, advise careful consideration of a curtailment threshold to achieve optimal results. Synthesis and applications: Near-term forecasts of migration fluxes evaluated against measurements can be used to define curtailment thresholds for offshore wind energy. We show that to minimize collision risk for 50% of migrants, if predicted correctly, curtailments should be applied during 18 hours in spring and 26 in autumn in the focal year of model assessments, resulting in an estimated annual wind energy loss of 0.12%. Drawing from the Dutch curtailment framework, which pioneered the 'international first' offshore curtailment, we argue that using forecasts developed from limited temporal datasets alongside expert insight and data-driven policies can expedite conservation efforts in a rapidly changing world. This approach is particularly valuable in light of increasing interannual variability in weather conditions.</p>
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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
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.