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646 results for “Migration data”
Data for Patterns of Settlement Following Forced Migration: The Case of Bosnians in the United States
<p>This repository contains the raw data used in Patterns of Settlement Following Forced Migration: The Case of Bosnians in the United States. These data were downloaded in February 2018 from the U.S. Census Bureau’s American Community Survey website at https://www.census.gov/programs-surveys/acs. The code used to analyze these data and intermediate datasets derived from them is available in GitHub at https://github.com/JohnPalmer/bosnian_settlement.</p>
Data for Aeolian Ripple Migration and Associated Creep Transport Rates
<p><strong>Overview:</strong></p> <p>The attached spreadsheet, "AeolianRippleMigration_ShermanEtAl2019.csv," summarizes the ripple migration and related data acquired from the wind tunnel and field experiment literature and from the field experiments at Jericoacoara, Ceará, Brazil (2008) and Oceano, California, USA (2015), associated with the article "Aeolian Ripple Migration and Associated Creep Transport Rates" by Douglas J. Sherman, Pei Zhang, Raleigh L. Martin, Jean T. Ellis, Jasper F. Kok, Eugene J. Farrell, and Bailiang Li.</p> <p><strong>Notes for data sources:</strong></p> <p>"a" - indicates that the data from a particular study were included in our final analyses</p> <p>"b" - indicates an estimate of threshold shear velocity (calculated as per Lorenz et al., 2011) with A = 0.1</p> <p>"c" - the value for ripple height in this study is the average of about 200 measurements for ripples in equilibrium or near-equilibrium with the wind field</p> <p>"d" - the data from this study were digitized as depicted in terms of ust/ust_th and u_r/(gd)^0.5 (see "Key to variables" below)</p> <p>"e" - Shear velocity (ust) values are from Martin & Kok, 2017. Median grain diameter (d) and threshold shear velocity (ust_th) values are from Martin & Kok, 2018 (see Table 2: "Date interval")</p> <p><br> <strong>Key to variables [units]:</strong></p> <p>Source - literature origin of previous studies or field location of observations for this study</p> <p>Note - annotation for additional information about study (see above "Notes for data sources")</p> <p>StudyType - classified as "field" or "wind tunnel"</p> <p>Date - date of observation for observations at Jericoacoara and Oceano ("N/A" for other sites)</p> <p>StartTime - start time of observation window (local time) for observations at Jericoacoara and Oceano ("N/A" for other sites)</p> <p>EndTime - end time of observation window (local time) for observations at Jericoacoara and Oceano ( "N/A" for other sites)</p> <p>u_r [mm/s] - calculated ripple migration speed ( "N/A" for Zhu et al, 2011, see "u_r_alt" below)</p> <p>sigma_u_r [mm/s] - uncertainty in ripple migration speed. Calculated as fixed percentage for Jericoacoara and as standard error for Oceano. For literare-derived values, "N/A" indicates lack of uncertainty estimates. For Oceano, "N/A" indicates inability to calculate standard error for certain measurement intervals containing only a single observation.</p> <p>u_r_alt - dimensionless proxy values for ripple migration speed for Zhu et al, 2011 (marked as "N/A" for other sites) calculated as u_r/(gd)^1/2, where "g" is gravitational acceleration and "d" is median surface grain diameter </p> <p>ust [m/s] - shear velocity ( "N/A" for Zhu et al, 2011, see "ust_over_ust_th" below)</p> <p>d [mm] - median surface grain diameter ("N/A" if not reported for literature studies)</p> <p>ust_th [m/s] - threshold shear velocity ("N/A" for Zhu et al, 2011, see "ust_over_ust_th" below)</p> <p>ust_over_ust_th - dimensionless proxy values for shear velocity for Zhu et al, 2011 (marked as "N/A" for other sites) calculated as ust/ust_th</p> <p>length [m] - ripple wavelength ("N/A" if not reported or measured)</p> <p>height [mm] - ripple amplitude ("N/A" if not reported or measured)</p> <p>sigma_height [mm] - uncertainty in ripple amplitude. Calculated as fixed percentage for Jericoacoara and as standard error for Oceano. For literare-derived values, "N/A" indicates lack of uncertainty estimates. For Oceano, "N/A" indicates inability to calculate standard error for certain measurement intervals containing only a single observation.</p>
Data for Aeolian Ripple Migration and Associated Creep Transport Rates
<p><strong>Overview:</strong></p> <p>The attached spreadsheet, "AeolianRippleMigration_ShermanEtAl2019.csv," summarizes the ripple migration and related data acquired from the wind tunnel and field experiment literature and from the field experiments at Jericoacoara, Ceará, Brazil (2008) and Oceano, California, USA (2015), associated with the article "Aeolian Ripple Migration and Associated Creep Transport Rates" by Douglas J. Sherman, Pei Zhang, Raleigh L. Martin, Jean T. Ellis, Jasper F. Kok, Eugene J. Farrell, and Bailiang Li.</p> <p><br> <strong>Notes for data sources:</strong></p> <p>"a" - indicates that the data from a particular study were included in our final analyses</p> <p>"b" - indicates an estimate of threshold shear velocity (calculated as per Lorenz et al., 2011) with A = 0.1</p> <p>"c" - the value for ripple height in this study is the average of about 200 measurements for ripples in equilibrium or near-equilibrium with the wind field</p> <p>"d" - the data from this study were digitized as depicted in terms of ust/ust_th and u_r/(gd)^0.5 (see "Key to variables" below)</p> <p>"e" - Shear velocity (ust) values are from Martin & Kok, 2017. Median grain diameter (d) and threshold shear velocity (ust_th) values are from Martin & Kok, 2018 (see Table 2: "Date interval")</p> <p><br> <strong>Key to variables [units]:</strong></p> <p>Source - literature origin of previous studies or field location of observations for this study</p> <p>Note - annotation for additional information about study (see above "Notes for data sources")</p> <p>StudyType - classified as "field" or "wind tunnel"</p> <p>Date - date of observation for observations at Jericoacoara and Oceano ("N/A" for other sites)</p> <p>StartTime - start time of observation window (local time) for observations at Jericoacoara and Oceano ("N/A" for other sites)</p> <p>EndTime - end time of observation window (local time) for observations at Jericoacoara and Oceano ( "N/A" for other sites)</p> <p>u_r [mm/s] - calculated ripple migration speed ( "N/A" for Zhu et al, 2011, see "u_r_alt" below)</p> <p>sigma_u_r [mm/s] - uncertainty in ripple migration speed. Calculated as fixed percentage for Jericoacoara and as standard error for Oceano. For literare-derived values, "N/A" indicates lack of uncertainty estimates. For Oceano, "N/A" indicates inability to calculate standard error for certain measurement intervals containing only a single observation.</p> <p>u_r_alt - dimensionless proxy values for ripple migration speed for Zhu et al, 2011 (marked as "N/A" for other sites) calculated as u_r/(gd)^1/2, where "g" is gravitational acceleration and "d" is median surface grain diameter </p> <p>ust [m/s] - shear velocity ( "N/A" for Zhu et al, 2011, see "ust_over_ust_th" below)</p> <p>d [mm] - median surface grain diameter ("N/A" if not reported for literature studies)</p> <p>ust_th [m/s] - threshold shear velocity ("N/A" for Zhu et al, 2011, see "ust_over_ust_th" below)</p> <p>ust_over_ust_th - dimensionless proxy values for shear velocity for Zhu et al, 2011 (marked as "N/A" for other sites) calculated as ust/ust_th</p> <p>length [m] - ripple wavelength ("N/A" if not reported or measured)</p> <p>height [mm] - ripple amplitude ("N/A" if not reported or measured)</p> <p>sigma_height [mm] - uncertainty in ripple amplitude. Calculated as fixed percentage for Jericoacoara and as standard error for Oceano. For literare-derived values, "N/A" indicates lack of uncertainty estimates. For Oceano, "N/A" indicates inability to calculate standard error for certain measurement intervals containing only a single observation.</p> <p><br> <strong>References:</strong></p> <p>Andreotti, B.; Claudin, P.; Pouliquen, O. Aeolian Sand Ripples : Experimental Study of Fully Developed States. 2006, 028001, 1-4.</p> <p>Borsy, Z. A homokfodrok. Fldrajzi rtesito 1973, 22, 109-115.</p> <p>Cheng, H.; Liu, C.; Zou, X.; Li, J.; He, J.; Liu, B.; Wu, Y.; Kang, L.; Fang, Y. Aeolian creeping mass of different grain sizes over sand beds of varying length. Journal of Geophysical Research: Earth Surface 2015, 120, 1404-1417.</p> <p>Cornish, V. On the formation of sand-dunes. The Geographical Journal 1897, 9, 278-302.</p> <p>Kindle, E.M. Recent and fossil ripple-mark; Canada Department of Mines, Geological Survey: 1917; pp 9-29.</p> <p>Ling, Y.-q.; Qu, J.-j.; Li, C.-z. Study on sand ripple movement with close shoot method. Journal of Desert Research 2003, 23, 118-120.</p> <p>Lorenz, R.D. Observations of wind ripple migration on an Egyptian seif dune using an inexpensive digital timelapse camera. Aeolian Research 2011, 3, 229-234.</p> <p>Martin, R.L.; Kok, J.F. Aeolian saltation fieldwork 30-minute wind and saltation values (Dataset). Zenodo, https://doi.org/10.5281/zenodo.291798: 2017.</p> <p>Martin, R.L., Kok, J.F. Distinct Thresholds for the Initiation and Cessation of Aeolian Saltation From Field Measurements. Journal of Geophysical Research - Earth Surface 2018, 123, 1546–1565. https://doi.org/10.1029/2017JF004416</p> <p>Seppälä, M.; Lindé, K. Wind tunnel studies of ripple formation. Geografiska Annaler: Series A, Physical Geography 1978, 60, 29-42.</p> <p>Sharp, R.P. Wind ripples. The Journal of Geology 1963, 71, 617-636.</p> <p>Stone, R.O.; Summers, H.J. Study of Subaqueous and Subaerial Sand Ripples; University of Southern California: Los Angeles, 1972.</p> <p>Zhu, W. Investigations on the formation and evolution of aeolian sand ripples. Lanzhou University, 2011.</p>
Data for article "Seasonal variation in migration routes of Common Whitethroat Curruca communis"
<p><strong>Abstract</strong></p> <p>This archive contains raw geolocator data and daily positions of analysed data from seven Common Whitethroats tracked by light geolocators from breeding sites in the Czech Republic and Latvia. Country and year of geolocator deployment is indicated in folder names. All geolocators, except for a logger with an ID BG630, are model SOI-GDL2 (Swiss Ornithological Institute) geolocators. Geolocator BG630 is model Intigeo-P50Z11-7-DIP geolocator (Migrate Technology Ltd.).</p> <p> </p>
Data from: Do precipitation extremes drive growth and migration timing of a Pacific salmonid fish in Mediterranean‐climate streams?
Climate change is expected to increase weather extremes and variability, including more frequent weather whiplashes or extreme swings between severe drought and extraordinarily wet years. Shifts in precipitation patterns will alter stream flow regimes, affecting critical life history stages of sensitive aquatic organisms. Understanding how threatened fish species, such as steelhead/rainbow trout (Oncorhynchus mykiss), are affected by stream flows in years with contrasting environmental conditions is important for their conservation. Here, we report how extreme wet and dry years, from 2015 to 2018, affected stream flow patterns in two tributaries to the South Fork Eel River, California, USA, and aspects of O. mykiss ecology, including over‐summer fish growth and body condition as well as spring out‐migration timing. We found that stream flow patterns differed across years in the timing and magnitude of large winter–spring flow events and in summer low‐flow levels. We were surprised to find that differences in stream flows did not impact growth, body condition, or timing of out‐migration of O. mykiss. Fish growth was limited in the late summer in these streams (average of 0.02 ± 0.05 mm/d), but was similar across dry and wet years, and so was end‐of‐summer body condition and pool‐specific biomass loss from the beginning to the end of the summer. Similarly, O. mykiss migrated out of tributaries during the last week of March/first week of April regardless of the timing of spring flow events. We suggest that the muted response to inter‐annual hydrologic variability is due to the high quality of habitat provided by these unimpaired, groundwater‐fed tributaries. Similar streams that are likely to maintain cool temperatures and sufficient base flows, even in the driest years, should be a high priority for conservation and restoration efforts.
Data from: Global migration is driven by the complex interplay between environmental and social factors
<p><strong>The datasets were produced in the following article. When using the data, please use the following citation:</strong></p> <p>Niva V, Kallio M, Muttarak R, Taka M, Varis O, Kummu M. 2021. Global migration is driven by the complex interplay between environmental and social factors. Environmental Research Letters. <a href="https://doi.org/10.1088/1748-9326/ac2e86">https://doi.org/10.1088/1748-9326/ac2e86</a></p> <p>The data include the following files:</p> <p><strong>AC.tif </strong></p> <p> Composite index computed based on the four AC variables by taking a mean over the respective variables:</p> <p> <strong>economy.tif</strong></p> <p> Downscaled and min-max normalized income data.</p> <p> <strong>education.tif</strong></p> <p> Min-max normalized education data.</p> <p> <strong>governance.tif</strong></p> <p> Min-max normalized governance data.</p> <p> <strong>health.tif</strong></p> <p> Min-max normalized health data.</p> <p>For all of the above data, 0 and 1 represent the lowest and highest <strong>capacity</strong>, respectively.</p> <p><strong>ES.tif</strong></p> <p> Composite index computed based on the four ES variables by taking a mean over the respective variables:</p> <p> <strong>foodProdScarcityScaled.tif</strong></p> <p> Food production scarcity data based on food production data.</p> <p> <strong>droughtRiskScaled.tif</strong></p> <p> Computed and scaled drought risk based on SPEI index.</p> <p> <strong>waterRiskScaled.tif</strong></p> <p> Computed and scaled water risk data based on three water stress indices.</p> <p>For all of the above data 0 and 1 represent the lowest and highest <strong>stress</strong>, respectively. Kindly note that data for natural hazards is available at its source (please see the list below). </p> <p><strong>class_raster.tif</strong></p> <p> Spatial representation of the classification matrix.</p> <p><strong>cntryID.gpkg</strong></p> <p> Country polygons with country IDs.</p> <p><strong>cntry_raster_masked.tif</strong></p> <p> Country raster with country IDs.</p> <p><strong>countriesRegionsZones.csv</strong></p> <p> Country groups and countries.</p> <p>Dataset specifications:</p> <p>spatial extent: -180, 180, -90, 90</p> <p>spatial resolution: 5 arc-min (0.083333333 degrees)</p> <p>projection: long/lat WGS84</p> <p>no data value: NA</p> <p> </p> <p><strong>Original data to produce the above indicators and to replicate the full analysis is available at the following sources:</strong></p> <p>Net-migration data (30 arc-sec resolution): https://doi.org/10.7927/H4319SVC</p> <p>Natural hazards: https://datadryad.org/stash/dataset/doi:10.5061/dryad.h2v2398</p> <p>Governance effectiveness: https://datadryad.org/stash/dataset/doi:10.5061/dryad.h2v2398</p> <p>Human Development Indicators (income, education, health): <a href="https://doi.org/10.1038/sdata.2019.38">https://doi.org/10.1038/sdata.2019.38</a></p> <p>Water risk indicators: <a href="https://doi.org/10.46830/writn.18.00146">https://doi.org/10.46830/writn.18.00146</a></p> <p>Drought (SPEI index): <a href="https://doi.org/10.1175/2009JCLI2909.1">https://doi.org/10.1175/2009JCLI2909.1</a></p> <p>Food production: <a href="https://doi.org/10.1038/nature11420">https://doi.org/10.1038/nature11420</a></p> <p>Population data: <a href="https://doi.org/10.1177%2F0959683609356587">https://doi.org/10.1177/0959683609356587</a></p>
Data for: Barriers to seedling establishment in grasslands: Implications for Nothofagus forest restoration and migration
<p>Tree seedling establishment outside forest boundaries is controlled by many interacting factors. Understanding the relative importance of different pressures is essential for improving techniques for forest restoration and for understanding the potential of forest boundaries to shift and adapt to changing climate conditions. We investigated constraints on the spread of a key southern hemisphere forest type into grasslands by undertaking a multi-factorial experiment sowing <em>Nothofagus cliffortioides</em> (Nothofagaceae) in a historically deforested retired pasture. We manipulated factors to determine the relative effects of sheltered microsites, competition with pasture species, availability of ectomycorrhizal fungi, soil nutrients, and rabbit herbivory, on seedling emergence and survival. Overall survival was low (11.7% after six months and 1.5% after ten months), but there were strong treatment effects on both seedling establishment and survival. The availability of shelter and competition with pasture species had strongest effects, with the presence of pasture species initially aiding seedling emergence due to a sheltering effect, but negatively affecting survival at later stages. Most remaining seedlings at the conclusion of the experiment had formed ectomycorrhizae regardless of whether inoculum was supplied, and seedling survival and health was positively related to ectomycorrhizal colonization. Fertilizing had less of an effect than other factors, and results regarding rabbit herbivory were inconclusive. Synthesis and applications: This study provides new insights into how factors interact to limit tree seedling establishment in grasslands and forest expansion into neighboring ecosystems. This improves understanding of the ability of an ectomycorrhizal keystone forest species to migrate and adapt to climate change. This study also assists restoration practitioners in selecting techniques that will enhance seedling establishment, and highlights that direct seeding approaches in open grasslands are unlikely to result in high rates of <em>Nothofagus </em>establishment.</p>
Data from: Like a rolling stone: Colonization and migration dynamics of the gray reef shark (Carcharhinus amblyrhynchos)
<p><span>Designing appropriate management plans requires knowledge of both the dispersal ability and what has shaped the current distribution of the species under consideration. Here we investigated the evolutionary history of the endangered gray reef shark (<em>Carcharhinus</em> <em>amblyrhynchos</em>) across its range by sequencing thousands of RAD-seq loci in 173 individuals in the Indo-Pacific (IP). We first bring evidence of the occurrence of a range expansion (RE) originating close to the Indo-Australian Archipelago (IAA) where two stepping-stone waves (east and westward) colonized almost the entire IP. </span><span>Coalescent modeling additionally highlighted </span><span>a homogenous connectivity (Nm~10 </span><span>per generation</span><span>) throughout the range, and</span><span> </span><span>an isolation-by-distance model suggested the absence of barriers to dispersal despite the affinity of <em>C</em>. <em>amblyrhynchos</em> to coral reefs. This coincides</span><span> with long-distance swims previously recorded, suggesting that the strong genetic structure at the IP scale (FST ~ 0.56 between its ends) is the consequence of its broad current distribution and organization in a large number of demes. Our results strongly suggest that management plans for the gray reef shark should be designed on a range-wide rather than a local scale due to its continuous genetic structure. We further </span><span>contrasted these results with those obtained previously for the sympatric but strictly lagoon-associated <em>Carcharhinus</em> <em>melanopterus</em>, known for its restricted dispersal ability. <em>C</em>. <em>melanopterus</em> exhibits a similar RE dynamic but is characterized by stronger genetic structure and a non-homogeneous connectivity largely dependent on local coral reefs availability. </span><span>This sheds new light on shark evolution, emphasizing the roles of IAA as a source of biodiversity and of life history traits in shaping the extent of genetic structure and diversity.</span></p>
Data from: Contrasting long-term trends in juvenile abundance of a widespread cold-water salmonid along a latitudinal gradient: Effects of climate, stream size and migration strategy
<p><span>A changing climate reshapes the range distribution of many organisms, and species with relatively low thermal optima, like many salmonids, are increasingly expected to face local population extinctions at lower latitudes. Understanding where and how fast these changes are happening is of pivotal importance for successful mitigation and conservation efforts.</span></p> <p><span>We used an extensive electrofishing database to explore temporal trends of brown trout juveniles (<em>Salmo</em> <em>trutta</em> L.) in 218 locations from 174 Swedish streams, over the last 30 years (1991–2020). We hypothesized that 1) declines in abundance have occurred predominately in the warmer, southern regions, while increases have occurred in the colder, northern regions, 2) larger stream sizes may partly offset negative effects of climate, and 3) migrating and resident populations are affected differently by a warming climate.</span></p> <p><span>We found that abundance of brown trout juveniles generally declined in warmer regions, especially in smaller streams (≤ 6 m wide), while the abundance increased in colder regions. In larger streams, negative effects of higher temperatures were seemingly buffered, as we found lower rates of decline or even positive trends. The rate of change (i.e. the slopes of the trends in abundance) was more pronounced towards the climate extremes and was on average zero in regions with a normal annual air temperature (average temperature over 30-year period) around 5–6 ºC. Warmer climate had stronger effects on migrating compared to resident populations, suggesting that climate-induced loss of stream connectivity could be an additional factor that hinders recruitment in anadromous populations in a changing climate.</span></p> <p><span>Considering predictions of increasing temperatures and frequency of summer droughts, management of cold-water salmonid populations should focus on conserving and restoring riparian vegetation, wetlands, climate and thermal refugia, and habitat integrity overall. Such measures may, however, not suffice for small streams at lower latitudes, unless hydrological connectivity is maintained.</span></p>
European migration scenarios with probabilistic uncertainty assessment – Data Description
<p>This open data deposit contains the data and code accompanying used in the report: Bijak (2023): European migration scenarios with probabilistic uncertainty assessment, QuantMig Project Deliverable D9.4. The cover note should be read in conjunction with the report, available via www.quantmig.eu, and with the individual readme files in the data folders that can be found within this Zenodo repository (DOI: 10.5281/zenodo.7954150).</p>
Kirchhoff pre-stack depth migration images of the multi-channel seismic data, SO190, RV. SONNE
<p>The dataset consists of four newly processed 2-D pre-stack depth migrated multi-channel seismic lines (BGR06_303, BGR06_305, BGR06_311 and BGR06_313) collected by GEOMAR and BGR in 2006. The dataset reveals the subducted oceanic reliefs and detailed accretionary wedge structure offshore eastern Java, Bali, Lombok, and Sumbawa islands, along the Sunda arc. The dataset is saved in standard SEGY format and could be loaded in open-source or commercial software. </p>
Output and data used in publication "Glacial isostatic adjustment modulates lateral migration rate and morphology of the Red River (North Dakota, USA, and Manitoba Canada)" in GRL
<p>Here we provide the output sea level used to calculate change in slope along studied rivers as well as locations of meanders and cutoffs. Please see the read.me file and publication for details. </p>
Data from: Conserving habitat for migratory ungulates: how wide is a migration corridor?
<ol> <li>Conserving migratory ungulates relies on the analysis of GPS collar data and associated maps of migration corridors to inform management and policy actions. Current methods for identifying migratory corridors use complex statistical models designed to account for movement uncertainty rather than estimating the amount of space required by animals to migrate. Further, such methods can complicate conservation efforts by producing highly variable corridor widths and non-contiguous corridors that do not fully connect seasonal ranges.</li> <li>To remedy this, we propose an intuitive line buffer approach for delineating individual migration corridors that is simple to implement and focuses on the functional corridor widths needed by migratory ungulates. </li> <li>By buffering a line that connects successive GPS locations, we can delineate individual migration corridors with consistent widths that are robust to variable parameters (GPS fix-rate, travel speed, tortuosity) and provide contiguous connection between seasonal ranges. Using a combination of expert knowledge, simulation, and 10-min GPS collar data collected from mule deer (<em>Odocoileus</em> <em>hemionus</em>) and pronghorn (<em>Antilocapra</em> <em>americana</em>), we suggest 400 to 600 m are reasonable estimates of functional migration corridor widths for individuals of those species.</li> <li>Our line buffer approach is intended to simplify migration corridor delineation, improve transparency, and encourage a broader discussion of functional corridor widths. These considerations help advance efforts to conserve habitat within migration corridors and prioritize conservation efforts within a single corridor or across multiple corridors.</li> </ol>
Data for "Upward migration of calanoid copepods is driven by high food quality in surface waters in an alpine lake"
<p>In this study, we explored why zooplankton migrated to surface waters at night from the perspective of their physiological characteristics and adaptability to the environment. The calanoid Arctodiaptomus sp. accumulated large amounts of polyunsaturated fatty acids (PUFAs) and astaxanthin, which relieved oxidative stress to fatty acids. The concentrations of lutein, a precursor of astaxanthin synthesis, were highest in surface water, indicating the enhancement of ultraviolet radiation (UVR) to precursor synthesis, which was confirmed by our indoor experiment. The calanoids migrated to surface water at night to obtain high concentrations of lutein and PUFAs from their diets. Relevant data for this study include: the vertical distribution of <em>Arctodiaptomus</em> sp. during the day and at night; concentrations of total astaxanthin, free astaxanthin, astaxanthin esters in <em>Arctodiaptomus</em> sp. at night and during the day; fatty acid concentration and the ratio of SAFAs (saturated fatty acids), MUFAs (monounsaturated fatty acids), and PUFAs in<em> Arctodiaptomus</em> sp. during the day and at night; the carotenoid concentrations in seston at different depth of Lake Heihai during the day and at night; the lutein concentration in seston under UVR and dark treatment; main characteristics of Lake Heihai; fatty acid concentrations and the ratio of SAFAs , MUFAs, and PUFAs of seston in Lake Heihai; the relative abundance of Chlorophytes with the size of greater than 5 μm and 0.2-5 μm in different layers of Lake Heihai; fatty acid concentrations of the calanoids in Fuxian Lake.</p>
Migration of mechanical perturbations estimated by seismic coda wave interferometry during the 2018 pre-eruptive period at Kīlauea volcano, Hawaii : Noise Cross-correlation Functions, Seismic catalog, and GNSS data
<p>ARCHIVE_NCFs_KILAUEA_2018.zip : Compress folder with (1) the daily noise cross-correlation functions (in MSEED format) of the station pairs used in the paper and (2) the one hour noise cross-correlation functions (in H5 format) of the station pairs used in the figure 9 of the paper.</p> <p>Code_Data_HVO.ipynb : Code to download the seismic data, available on IRIS, used in this paper.</p> <p>GPS_data_AHUP.zip : Compress folder with the daily GPS data of the station AHUP used in the paper [Year, Month, Day, Day_of_the_year, Second_of_the_day, East_comp(mm), North_comp(mm), Vertical_comp(mm), Sig_East_comp, Sig_North_comp, Sig_Vertical_comp].</p> <p>Radial_tilt_UWD.txt : Daily radial tilt measurement of the tiltmeter UWD [Year, Month, Day, Radial_tilt(µrad)].</p> <p>Seismic_stations_Kilauea.txt : Name code and location of the seismic stations used in the paper [Station_code, Longitude, Latitude].</p> <p>Seismicity_Catalog_Kilauea_2018_USGS.txt : Seismic catalog from USGS used in the paper [Date_Time, Latitude, Longitude, Depth, Magnitude].</p>
Population dynamics and seasonal migration patterns of Spodoptera exigua in northern China based on 11 years of monitoring data
<p>The beet armyworm, <em>Spodoptera exigua </em>(Hübner) is an important migratory pest worldwide that has caused serious economic losses in the main crop-producing areas of China. To effectively monitor and control this pest, it is necessary to investigate its interannual and seasonal migration patterns in northern China. In this study, we weekly monitored the population dynamics of <em>S. exigua</em> using sex pheromone traps in Shenyang, Liaoning Province from 2012 to 2022 and simulated the migration trajectories using the HYSPLIT model. Overall, the migration numbers varied significantly among years, with large migrations in 2018 and 2020 that resulted in a total catch of more than 2000 individuals. </p>
Data for: Stable isotopes in eye lenses reveal migration and mixing patterns of diamond squid in the western North Pacific and its marginal seas
<p><span>Knowledge of the movements of marine organisms is essential for effective conservation schemes. Here, we investigated the lifetime habitat use of diamond squid, <em>Thysanoteuthis</em> <em>rhombus</em>, collected in the western North Pacific and its marginal seas (the Sea of Japan and the East China sea) during 2021–2022, whose migratory ecology is poorly known, using bulk stable nitrogen and carbon isotope ratios in eye lenses. From the eye lens isotope profiles, the chronology of the isotopic baseline of squid habitat was estimated by removing the effect of size-dependent changes of trophic position. Then, the baseline estimates were compared to the isoscapes of particulate organic matter. The baseline chronologies showed fluctuations during the paralarval and juvenile stages, becoming stable during the adult stage, suggesting that significant movements mainly occur during the early life stages due to current transport, with adults potentially not undertaking long-distance migrations. The squids in the marginal seas mostly originated from outside the subtropical gyre, while the squids in the subtropical gyre had various sources, including outside the gyre and southern and northern parts within the gyre, revealing a complex mixing pattern of the species. These results show that isotope chronology combined with baseline isoscapes are effective tools to understand animal migrations, which can help to manage various cephalopods and fishes.</span></p>
Data from: Feeding en route: Prey availability and traits influence prey selection by an avian predator on migration
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Data from: Recovery from infection is more likely to favor the evolution of migration than social escape from infection
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Data from: Concealed by darkness: interactions between predatory bats and nocturnally migrating songbirds illuminated by DNA sequencing
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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.