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3,673 results for “Temporal”
Data sets and code for "Suprachiasmatic Nucleus-wide Estimation of Oscillatory Temporal Dynamics" (Yao et al, 2024)
<ul> <li>Data from iDISCO clearing and scanning of three adult mouse suprachaismatic nuclei. Brains are labeled as b1, b2, and b3. Each lobe of the SCN is recorded in a separate csv file. Animals were sacrificed at ZT 19. </li> <li>Data for PER2::LUC recordings of ix adult mouse suprachaismatic nuclei. For each slice there are two files: the time series data (labeled "slice-[orientation]-time-series-#" and the coordinates of the pixels represented (labeled "slice-[orientation]-pixel-coords-#."</li> <li>Code in MATLAB to perform phase extraction, linear modeling, phase estimation, and dynamical simulation.</li> </ul>
Spatial and temporal variability of the freezing level in Patagonia's atmosphere
<h3>Short Summary:</h3> <p>This repository houses the Python preprocessing scripts utilized in generating the metadata for García-Lee et al., (2024) dataset. With these files and scripts, you gain access to the algorithm and examples for generating gridded products in netCDF format, specifically featuring the 0°C isotherm field.</p> <h3>Dependencies:</h3> <ul> <li>numpy (tested with 1.24.4 in py3)</li> <li>pandas (tested with 2.0.3 in py3)</li> <li>netCDF4 (tested with 1.6.0 in py3)</li> <li>re (tested with 2.2.1 in py3)</li> <li>glob</li> <li>OS: Tested in Windows.</li> </ul> <h3>Technical Info:</h3> <table> <tbody> <tr> <td> <p>File</p> </td> <td> <p>Type</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p><a href="../records/10523940/files/1_H0_Detect.py?download=1">1_H0_Detect.py</a></p> </td> <td> <p>Python script</p> </td> <td> <p>0°C Isotherm Detection Algorithm.</p> </td> </tr> <tr> <td> <p><a href="../records/10523940/files/2_Daily_Mean_H0.py?download=1">2_H0_Daily_Mean.py</a></p> </td> <td> <p>Python script</p> </td> <td> <p>Calculation of Daily Mean.</p> </td> </tr> <tr> <td> <p><a href="../records/10523940/files/ISO0_1959_2021_GRID.nc?download=1">ISO0_1959_2021_GRID.rar</a></p> </td> <td> <p>netCDF</p> </td> <td> <p>0°C Isotherm Data at 6-Hour Intervals (1959-2021) in meters above sea level (m a.s.l.).</p> </td> </tr> <tr> <td> <p><a href="../records/10523940/files/ERA5_PATAGONIA_6H_T_GPH_1959.rar?download=1">ERA5_PATAGONIA_6H_T_GPH_1959.rar</a></p> </td> <td> <p>netCDF</p> </td> <td> <p>Raw ERA5 data example for 1959: Temperature (°K) and Geopotential (m**2 s**-2).</p> </td> </tr> </tbody> </table> <h3>Extra:</h3> <p>The file 'Observations and Charts.pdf' shows averages, standard deviations, bias, and trends of the 0°C isotherm for Puerto Montt, Río Gallegos, Comodoro Rivadavia, and Punta Arenas. These values were estimated using both observations and reanalysis ERA5 data.</p> <h3>Reference:</h3> <p>García-Lee, N., Bravo, C., Gónzalez-Reyes, Á., and Mardones, P.: Spatial and temporal variability of the freezing level in Patagonia's atmosphere, Weather Clim. Dynam., 5, 1137–1151, https://doi.org/10.5194/wcd-5-1137-2024, 2024.</p>
PubMed-Temporal: A dynamic graph dataset with node-level features
Open the record for dataset details and reuse information.
Temporal Analysis of Knowledge Networks
<p>This dataset contains 64 topic networks extracted from the English Wikipedia's internal link network. Every topic network contains 19 snapshots, which are taken on March 1st of each year from 2002 to 2020. More detailed information are available at: https://github.com/XikunHuang/TAKN.</p>
The Multi-Temporal Dual Channel Algorithm (MT-DCA)
<p>I) SUMMARY</p> <p>This soil moisture and vegetation optical depth product is called the Multi-Temporal Dual Channel Algorithm (MT-DCA). It retrieves surface soil moisture and vegetation optical depth (directly related to total water volume in the vegetation canopy) from <a href="https://nsidc.org/data/SPL1CTB_E">SMAP level 1C brightness temperature</a> observations using a robust estimation technique. It is an in-house MIT algorithm and is not an official SMAP product. The data are freely available on 9km and 36km grids from April 2015 to July 2021 in daily time steps.</p> <p>No co-authorship is required for use of this data in publications. However, to properly acknowledge the dataset when publishing any research using the MT-DCA, we ask data users to (1) cite the DOI as an in-text citation and/or in the data acknowledgements in any publication and (2) reference <a href="https://www.sciencedirect.com/science/article/pii/S0034425717302961">Konings et al. (2017</a>) when referring to the MT-DCA in the text. Feel free to send us an email at <a href="mailto:afeld24@mit.edu">afeld24@mit.edu</a> to let us know how you are using the data. </p> <p>The version 5 update is a re-implementation of the MT-DCA using the updated SMAP L1C brightness temperatures. It extends the data through July 2021.</p> <p>II) CONTACT</p> <p>For questions, please email Andrew Feldman at <a href="mailto:afeld24@mit.edu">afeld24@mit.edu</a>.</p> <p>III) ALGORITHM DESCRIPTION</p> <p>The algorithmic approach uses both horizontally and vertically polarized brightness temperatures to retrieve soil moisture and VOD simultaneously. The key innovation of the MT-DCA is that it recognizes that classical dual-channel algorithms are under-determined: brightness temperature observations are correlated and cannot retrieve two unknowns (soil moisture and VOD) (as illustrated in Konings et al, RSE 2016). This creates amplifying errors in retrievals from snapshot dual-channel algorithms. The MT-DCA uses a viable assumption that VOD changes more slowly than soil moisture between overpasses, and uses information from multiple SMAP overpasses to stabilize the retrieval. It is considered a regularization approach similar to the Sobolev Norm regularization. Specifically, this approach is applied to each temporally adjacent pair of overpasses (for SMAP, two overpasses approximately 2-3-days apart), which includes four brightness temperature measurements. For each overpass pair, the soil moisture at both overpasses is retrieved, along with a constant VOD for both overpasses. This leads to two retrievals of each of soil moisture and VOD at any given overpass time: one where the parameters are retrieved using additional TB information from the overpass before and one from the overpass after. Both retrievals of VOD and soil moisture values at each overpass are averaged. Ultimately, VOD is not held constant, but rather is slowed in time between overpasses. A second key innovation of the MT-DCA is that, because the retrievals are no longer under determined, it is also possible to retrieve a constant single scattering albedo for each pixel. The single scattering albedo is estimated through model selection of the value of the parameter that minimizes the sum of all overpass cost functions. The retrieved albedo is also included in the files here. VOD is reported at nadir.</p> <p>The single scattering albedo is assumed constant over the full record of SMAP data, as is currently accepted practice across approaches with SMAP, SMOS, and AMSR. There is a high amount of computational power required to retrieve an albedo over more than three years of SMAP data. Therefore, an adjustment was made: the single scattering albedo was retrieved over the third year of SMAP data (April 1st, 2017 to March 31st, 2018). This constant value was then applied to the other years without requiring the albedo optimization loop. Tests across many individual pixels revealed that albedo in the third year does not differ greatly from albedo over all years and the other individual years. </p> <p>The algorithm is described in more detail in Konings et al. (2017). The algorithm is based on principles explained in more detail in Konings et al. (2016), which describes the original algorithm development using Aquarius observations. See also the related Konings et al. (2015) publication for quantitative justification for the approach. While the dataset has not been officially validated, the MT-DCA soil moisture retrievals show in-situ comparison statistics similarly to the official baseline SMAP soil moisture product (SMAP soil moisture retrieval in-situ assessment can be found in Chan et al. (2016)). Finally, the MT-DCA vegetation optical depth retrievals are not validated due to only sparsely available ground information related to vegetation water content. Nevertheless, information about error propagation into the MT-DCA soil moisture and VOD retrievals as well as VOD error reductions using the MT-DCA regularization technique can be found in Feldman et al. (2021).</p> <p>Chan, S.K., Bindlish, R., O’Neill, P.E., Njoku, E., Jackson, T., Colliander, A., Chen, F., Burgin, M., Dunbar, S., Piepmeier, J., Yueh, S., Entekhabi, D., Cosh, M.H., Caldwell, T., Walker, J., Wu, X., Berg, A., Rowlandson, T., Pacheco, A., McNairn, H., Thibeault, M., Martinez-Fernandez, J., Gonzalez-Zamora, A., Seyfried, M., Bosch, D., Starks, P., Goodrich, D., Prueger, J., Palecki, M., Small, E.E., Zreda, M., Calvet, J.C., Crow, W.T., Kerr, Y., 2016. Assessment of the SMAP Passive Soil Moisture Product. IEEE Trans. Geosci. Remote Sens. 54, 4994–5007. <a href="https://doi.org/10.1109/TGRS.2016.2561938">https://doi.org/10.1109/TGRS.2016.2561938</a></p> <p>Feldman, A.F., D. Chaparro, and D. Entekhabi (2021). Error propagation in microwave soil moisture and vegetation optical depth retrievals. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. In Press.</p> <p>Konings, A.G., M. Piles, N. Das, and D. Entekhabi (2017). L-band vegetation optical depth and effective scattering albedo estimation from SMAP. Remote Sensing of Environment, 198:460-470. <a href="https://doi.org/10.1016/j.rse.2017.06.037">https://doi.org/10.1016/j.rse.2017.06.037</a></p> <p>Konings, A.G., M. Piles, K. Rötzer, K.A. McColl, S. Chan, and D. Entekhabi (2016). Vegetation optical depth and scattering albedo retrieval using time-series of dual-polarized L-band radiometer observations. Remote Sensing of Environment. 172, 178-189. https://doi.org/10.1016/j.rse.2015.11.009</p> <p>Konings, A.G., K.A. McColl, M. Piles and D. Entekhabi (2015): How many parameters can be maximally estimated from a set of measurements? IEEE Geoscience and Remote Sensing Letters, 12(5), 1081-1085. https://doi.org/10.1109/LGRS.2014.2381641</p> <p>IV) QUALITY CONTROL</p> <p>Several conditions can create uncertainty in the MT-DCA retrievals including surface water bodies (lakes, rivers, coastal areas, etc.), radio frequency interference (RFI), highly sloped surfaces (mountainous regions), dense vegetation, frozen ground, and others. The MT-DCA removes time periods of frozen ground and removes pixels with water body fractions of greater than 0.5. SMAP L1C brightness temperatures are adjusted considering RFI and surface water body information. Nevertheless, the MT-DCA retrievals are purposefully not substantially quality controlled to increase the range of science applications of the data. Therefore, the retrievals are subject to uncertainty in regions where and times when these aforementioned issues occur. We suggest the data user familiarize themselves with quality flags in the SMAP algorithm theoretical basis document in <a href="https://nsidc.org/data/SPL3SMP_E">https://nsidc.org/data/SPL3SMP_E</a>. Conservative quality control can be applied using SMAP quality flag information directly applicable to the dataset here. These quality flags can be downloaded from the SMAP official product files at <a href="https://nsidc.org/data/SPL3SMP_E">https://nsidc.org/data/SPL3SMP_E</a>.</p> <p>V) DATA FORMATTING AND FILE NAMES </p> <p>Data are provided in zipped folders in both netcdf4 (.nc) and matfile (.mat) formats. Each zipped folder contains soil moisture, vegetation optical depth, single scattering albedo, latitude, longitude, and time vector information. Note that as an update in Version 5, the zipped folders for 9km .mat files are separated into soil moisture and vegetation optical depth to reduce zip folder size. The other zipped folders still have all variables within them. These variables are provided at a 9km resolution as well as upscaled to 36km. For both .nc and .mat files, the 9km data are provided in 3-month periods with a naming convention of ‘YYYYMM_YYYYMM’ where YYYY is the 4-digit year, and MM is the 2-digit month. The first YYYYMM string represents the first month and the second YYYYMM string is the final month of the period. The 36km data are provided in 12-month periods with the same naming conventions in the file names.</p> <p>Retrievals are obtained from enhanced-resolution brightness temperatures from SMAP that are gridded at 9km. As such, they are on a 9km EASE2-grid. These retrievals are upscaled to 36km and gridded on a 36km EASE2 grid. Additional information and geolocation tools are available at <a href="https://nsidc.org/data/ease/ease_grid2.html">https://nsidc.org/data/ease/ease_grid2.html</a>. </p> <p>Information specific to folders with .nc and .mat formats is given below:</p> <p>a) NETCDF Files (.nc): The folders with netcdf files contain files with the convention MTDCA_YYYYMM_YYYYMM_Xkm_VX.nc where VX is the version number, Xkm is the grid scale, and YYYYMM strings are the first and last months of the range of data saved in the file. Soil moisture, vegetation optical depth, latitude, longitude, and time index information are provided in these files. A map of single scattering albedo for the full time series is saved in a separate file as MTDCA_OMEGA_Xkm_VX.nc along with latitude and longitude information.</p> <p>b) MATFILES (.mat): The folders with matfiles contain individual files for:</p> <ol> <li>Soil moisture: MTDCA_VX_SM_YYYYXX_YYYYXX_Xkm.mat</li> <li>Vegetation Optical Depth: MTDCA_VX_TAU_YYYYXX_YYYYXX_Xkm.mat</li> <li>Single Scattering Albedo: MTDCA_VX_OMEGA_Xkm.mat</li> <li>Latitude/Longitude: SMAPCenterCoordinatesXKM.mat</li> </ol> <p>A datevector variable in each soil moisture and vegetation optical depth file contains information on the year, month, and day corresponding to the timestep of each variable.</p>
Treatise on Hearing: The Temporal Auditory Imaging Theory Inspired by Optics and Communication (Supplementary Audio Demo Files)
<p>Audio files that supplement "Treatise on Hearing: The Temporal Auditory Imaging Theory Inspired by Optics and Communication". Please refer to the manuscript (preprint) for additional details.</p>
Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network
<p>Supporting information, datasets, and R and JavaScript code for the the National Ecological Observatory Network’s Airborne Observation Platform (AOP) sampling design and publication, <em>"Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network"</em></p>
Fig. 3 in Spatio-Temporal Structure And Reproductive Success In A Rook (Corvus Frugilegus) Colony
Fig. 3. Relationship between local nest density (number of neighbouring nests within 6 metres) at the date of hatching and hatchling's survival rate (medians). Spearman rank correlation: r s = 0.382, n = 80, P <0.001
Fig. 2 in Spatio-Temporal Structure And Reproductive Success In A Rook (Corvus Frugilegus) Colony
Fig. 2. Relationship between the date of nesting and the elapsed time from nesting to egg laying. Spearman rank correlation: r s = –0.695, n = 102, P <0.01
Fig. 1 in Spatio-Temporal Structure And Reproductive Success In A Rook (Corvus Frugilegus) Colony
Fig. 1. Temporal distribution of nest building and egg laying. Bars represent the number of nests that were started to build (white) and number of nests in which the first egg was laid (grey) grouped into five-day periods. n = 20, mean±SD: 40.00±5.94 and 76.33±8.80 days, respectively
Data: Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals
<p><strong>Data used in:</strong></p> <p>Schönauer, M., Prinz, R., Väätäinen, K., Astrup, R., Pszenny, D., Lindeman, H., et al. (2022). Spatiotemporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals. <em>International Journal of Applied Earth Observation and Geoinformation</em>, 102730. doi: 10.1016/j.jag.2022.102730</p>
The spatio-temporal program of liver zonal regeneration
<p>We performed mouse bulk liver mRNA sequencing (mcSCRBseq, Bagnoli et al., 2018), single-cell RNA seq (Feature Barcode 10x) and spatial transcriptomics (10x Visium) of hepatocytes and non-parenchymal cells (NPCs) at several time points along the time course of Acetaminophen (APAP)-induced liver damage and regeneration to identify spatio-temporal dynamics of liver regeneration. </p> <p> </p>
Sensing Echoes: Temporal misalignment as the Earliest Marker of Neurodevelopmental Derail
<p>FROM THE PREPRINT:</p> <p>Sensory transduction and transmission delays operate and propagate along different time scales. From microseconds in the auditory domain, to hundreds of milliseconds in the visual, and kinesthetic domains, the brain must successfully align disparate delays arising from endogenously self-generated streams of motor and visceral sensorial information, with exogenous sensory inputs. To produce a cohesive response to environmental goals, constantly explore, adapt, and develop a sense of simultaneity, the brain must resolve this major feat and compensate for excessive delays in any sensory modality. Disruption in these processes may lead to altered perception of the self and others, and inadvertently affect social interactions. But how early such issues may emerge and be reliably detectable, remains a challenge. Here we assess in neonates, the transmission latencies of a sound wave that travels from the cochlear nerve to the brainstem on its way to the primary auditory cortex. Already at birth, we find systematic and cumulative delays in the propagation of this wave in neonates that later received a diagnosis of autism. Furthermore, we discover that the distributions of such temporal delays have far narrower bandwidth than those from neonates who did not receive the autism diagnosis. We identify associated codependent genes’ networks and define a reliable marker of neurodevelopment derail, detectable at birth. Under the precision autism model, we propose that the brainstem contains an endogenous clock anchoring and aligning disparate timescales critical for the emergence and maintenance of congruent percepts of the self and others.</p>
Data from: Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds
<p>For ecosystem models to be applicable outside their context of development, temporal and spatial transferability must be demonstrated. This presents a challenge for modeling intertidal ecosystems where spatiotemporal variation arises at multiple scales. Models specializing in tidal dynamics are generally inhibited from having wider ecological applications by coarse spatiotemporal resolution or high user competency. The Tidal Inundation Model of Shallow-water Availability (TiMSA) uniquely simulates tides to empirically derive a time-integrated measure of availability for a shallow water depth range defined by the user. To evaluate temporal and spatiotemporal transferability, we employed TiMSA at the development site in the Florida Keys and at novel sub-sites in the Florida Bay (application site) under a different time period (application period). We used foraging Little Blue Herons (<em>Egretta caerulea</em>) as the ecological unit with which to constrain the model's 'water depth window', i.e., range of water depths to estimate shallow-water availability. At the development site, temporally consistent water depth windows contrasted with interannual variation in shallow-water availability which revealed short-term changes in Little Blue Heron foraging habitat. At the application site, water depth accuracy varied by sub-site and was correlated with spatial error in bathymetric elevation. Although TiMSA parameters were sensitive to environmental temporal variation and uncertainty in spatial data, a spatially-explicit water depth window generated reliable estimates of shallow-water conditions over space and time at the development and application sites. By exploring the contributing factors to model error, we provide solutions to reduce uncertainty of TiMSA parameters at potential application sites and recommendations for addressing bathymetric inaccuracy in digital elevation models. Accurately quantifying spatiotemporal changes of shallow-water has implications for monitoring habitat conditions for tidally-influenced species and projecting future changes to coastal ecosystems in response to anthropogenic stressors and natural disturbances such as sea level rise.</p>
Time changes everything: A multispecies analyses of temporal patterns in evaporative water loss - data
<p>The dataset was analysed in the manuscript “Žagar A., Carretero, M.A., de Groot M. (accepted) Time changes everything: A multispecies analyses of temporal patterns in evaporative water loss. Oecologia”</p> <p>The dataset consisted out of water loss by 23 populations of lizards from 16 different species and three families which was compiled from several different studies. All studies used the same standardized protocols. During the experiment every hour for 12 hours, the body weight of the lizard was measured (in total 13 measurements per lizard). The species name (SP), the snout-vent length of the animal (SVL, in millimetres), altitude (m a.s.l.), sampling location (site name, latitude and longitude), weight (in grams), sex (M=male, F=female), code of the individual lizard (CODE), date of experiment (DATE_H) and the reference of the study were noted down (full references are available in the manuscript). Per column the instantaneous water loss values (EWLi) were recorded per hour measured. First hour was EWLi8, second hour was EWLi9, etc. The EWLi was calculated by the weight minus the weight in the next hour divided by the weight multiplied by 100 ((W<sub>n</sub> – W<sub>n+1 </sub>/ W<sub>n</sub>) × 100).</p>
Geographic and temporal morphological stasis in the latest Cretaceous ammonoid Discoscaphites iris from the U.S. Gulf and Atlantic Coastal Plains
<p class="MsoNormal"><span>We examine temporal and spatial variation in morphology of the ammonoid cephalopod <em>Discoscaphites iris</em> using a large dataset from multiple localities in the Late Cretaceous (Maastrichtian) of the United States Gulf and Atlantic Coastal Plains, spanning a distance of 2000 km along the paleoshoreline. Our results suggest that the fossil record of <em>D. iris </em>is consistent with no within species net accumulation of phyletic evolutionary change across morphological traits or the lifetime of this species. Correlations between some traits and paleoenvironmental conditions as well as changes in the coefficient of variation may support limited population-scale ecophenotypic plasticity, however where stratigraphic data are available, no directional changes in morphology occur prior to the Cretaceous/Paleogene (K/Pg) boundary. This is consistent with models of 'dynamic' evolutionary stasis. Combined with knowledge of life history traits and paleoecology of scaphitid ammonoids, specifically a short planktonic phase after hatching followed by transition to a nektobenthic adult stage, these data suggest that scaphitids had significant potential for rapid morphological change in conjunction with limited dispersal capacity. It is therefore likely that evolutionary mode in the Scaphitidae (and potentially across the broader ammonoid clade) follows a model of cladogenesis wherein a dynamic morphological stasis is periodically interrupted by more substantial evolutionary change at speciation events. Finally, the lack of temporal changes in our data suggest that global environmental changes (such as those possibly related to the emplacement of the Deccan Traps Large Igneous Province) had a limited effect on the morphology of North American ammonoid faunas during the latest Cretaceous prior to the K/Pg mass extinction event.</span></p>
Fig. 2 in Spatial And Temporal Changes In Falconiformes And Strigiformes Nutrition: Causes, Significance, Consequences
Fig. 2. Possible factors affecting the feeding range of birds of prey (according to: Birrer, 2009, as amended).
Negative temporal autocorrelation in mast seeding dynamics positively influences both the long and short-term dynamics of a wild boar population
<p><span>Temporal autocorrelation in environmental conditions influences population dynamics through its effects on vital rates. However, a comprehensive understanding of how and to what extent temporal autocorrelation shapes population dynamics is still lacking because most empirical studies have unrealistically assumed that environmental conditions are temporally independent. Mast seeding is a biological event characterized by highly fluctuating and synchronized seed production at the tree population scale, as well as a marked negative temporal autocorrelation. In the current context of global change, mast seeding events are expected to become more frequent, leading to strengthened negative temporal autocorrelations and thereby amplified cyclicality in mast seeding dynamics with cycles of length 2 years. </span><span>Theory predicts that population growth rates are maximized when the environmental cyclicality of consumer resources and their generation times are closely matched. </span><span>To test this prediction, we took advantage of the long-term monitoring of a wild boar population, a widespread seed consumer species characterized by a short generation time (ca. 2 years). As expected, simulations indicated that its stochastic population growth rate increased as mast seeding dynamics became more negatively autocorrelated. Our findings demonstrate that accounting for temporal autocorrelations in environmental conditions relative to generation time of the focal population is required, especially under global warming where the cyclicality in resource dynamics is likely to change.</span></p>
Temporal trends in the spatial bias of species occurrence records
<p>Large-scale biodiversity databases have great potential for quantifying long-term trends of species, but they also bring many methodological challenges. Spatial bias of species occurrence records is well recognized. Yet, the dynamic nature of this spatial bias - how spatial bias has changed over time - has been largely overlooked. We examined the spatial sampling bias of species occurrence records within multiple biodiversity databases in Germany and tested whether spatial bias in relation to land cover or land use (urban and protected areas) has changed over time. We focused our analyses on urban and protected areas as these represent two well-known correlates of sampling bias in biodiversity datasets. We found that the proportion of annual records from urban areas has increased over time while the proportion of annual records within protected areas has not consistently changed. Using simulations, we examined the implications of this changing sampling bias for estimation of long-term trends of species' distributions. When assessing biodiversity change, our findings suggest that the effects of spatial bias depend on how it affects sampling of the underlying land-use change drivers affecting species. Oversampling of regions undergoing the greatest degree of change, for instance near human settlements, might lead to overestimation of the trends of specialist species. For robust estimation of the long-term trends in species' distributions, analyses using species occurrence records may need to consider not only spatial bias, but also changes in the strength of spatial bias through time.</p>
Atlantic salmon survival at sea: temporal changes that lack regional synchrony
<p>Spatial and temporal synchrony in abundance or survival trends can be indicative of whether populations are affected by common environmental drivers. In Atlantic salmon (<em>Salmo salar</em> L.), return rates to natal rivers have generally been assumed to be affected primarily by shared oceanic conditions, leading to spatially synchronous trends in mortality. Here, we investigate the existence of parallel trends in salmon sea survival, using data on migrating smolts and returning adults from seven Canadian populations presumed to share feeding grounds. We analyse sea survival, using a Bayesian change-point model capable of detecting non-stationarity in time series data. Our results indicate that while salmon have experienced broadly comparable patterns in survival, finer-scale temporal shifts are not synchronous among populations. Our findings are not consistent with the hypothesis that salmon populations consistently share the same mortality-related stressors in the marine environment. Although populations may have shared greater synchrony in survival patterns in the past, this synchrony may be breaking down. It may be prudent to direct greater attention to smaller-scale regional and population-level correlates of survival</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.