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944 results for “Human population”
Distinct population code for movement kinematics and changes of ongoing movements in human subthalamic nucleus
<p>The subthalamic nucleus (STN) is theorized to globally suppress movement through connections with downstream basal ganglia structures. Current theories are supported by increased STN activity when subjects withhold an uninitiated action plan, but a critical test of these theories requires studying STN responses when an ongoing action is replaced with an alternative. We perform this test in subjects with Parkinson's disease using an extended reaching task where the movement trajectory changes mid-action. We show that STN activity decreases during action switches, contrary to prevalent theories. Further, beta oscillations in the STN local field potential, which are associated with movement inhibition, do not show increased power or spiking entrainment during switches. We report an inhomogeneous population neural code in STN, with one sub-population encoding movement kinematics and direction and another encoding unexpected action switches. We suggest an elaborate neural code in STN that contributes to planning actions and changing the plans.</p>
All data for the preprint Population genetics of Glossina palpalis gambiensis in the sleeping sickness focus of Boffa (Guinea) before and after eight years of vector control: no effect of control despite a significant decrease of human exposure to the disease
<p>Data set for the paper titled "Population genetics of <em>Glossina palpalis gambiensis</em> in the sleeping sickness focus of Boffa (Guinea) before and after eight years of vector control: no effect of control despite a significant decrease of human exposure to the disease"</p>
Distinct population code for movement kinematics and changes of ongoing movements in human subthalamic nucleus
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Run and output files from: Holocene population expansion of a tropical bee coincides with early human colonisation of Fiji rather than climate change
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Varying genetic imprints of roads and human density in North American mammal populations
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Genomic footprints of (pre) colonialism: Population declines in urban and forest túngara frogs coincident with historical human activity
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Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders
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Data from: Protection status, human disturbance, snow cover and trapping drive density of a declining wolverine population in the Canadian Rocky Mountains
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Mammal population densities at a global scale are higher in human-modified areas
Global landscapes are changing due to human activities with consequences for both biodiversity and ecosystems. For single species, terrestrial mammal population densities have shown mixed responses to human pressure, with both increasing and decreasing densities reported in the literature. How the impacts of human activities on mammal populations translates into altered global density patterns remains unclear. Here we aim to disentangle the effect of human impacts on large-scale patterns of mammal population densities using a global dataset of 6729 population density estimates for 468 mammal species (representing 59% and 44% of mammalian orders and families). We fitted a mixed effect model to explain the variation in density based on a 1-degree resolution as a function of the Human Footprint Index (HFI), a global proxy of direct and indirect human disturbances, while accounting for body mass, trophic level and primary productivity (Normalized Vegetation Index; NDVI). We found a significant positive relationship between population density and HFI, where population densities were higher in areas with a higher HFI (e.g., agricultural or suburban areas – no populations were located in very high HFI urban areas) compared to areas with a low HFI (e.g., wilderness areas). We also tested the effect of the individual components of the HFI and still found a consistent positive effect. The relationships remained positive even across populations of the same species, although variability among species was high. Our results indicate shifts in mammal population densities in human modified landscapes, which is due to the combined effect of species filtering, increased resources, and a possible reduction in competition and predation. Our study provides further evidence that macroecological patterns are being altered by human activities, where some species will benefit from these activities, while others will be negatively impacted or even extirpated.
Eat or be eaten: Implications of potential exploitative competition between wolves and humans across predator- savvy and -naive deer populations
<p>Recolonization of predators to their former ranges is becoming increasingly prevalent. Such recolonization places predators amongst their prey once again; the latter having lived without predation (from such predators) for considerable time. This renewed coexistence creates opportunities to explore predation ecology at both fundamental and applied levels.</p> <p>We used a paired experimental design to investigate white-tailed deer risk allocation in the Upper and Lower Peninsulas (UP and LP) in Michigan, USA. Wolves are functionally absent in the LP, while deer in the UP coexist with a re-established wolf population. We treated 15 sites each in UP and LP with wolf olfactory cues and observed deer vigilance, activity, and visitation rates at the interface of habitat covariates using remote cameras. Such a paired design across wolf versus no-wolf areas allowed us to examine indirect predation effects while accounting for confounding parameters such as the presence of other predators and human activity.</p> <p>While wolf urine had no effect across most metrics in both UP and LP, we observed differences in deer activity in areas with versus without wolves. Sites treated with wolf urine in the UP showed a reduction in crepuscular deer activity, compared to control/novel-scent treated sites. Further, we observed a strong positive effect of vegetation cover on deer vigilance in these sites. This indicates that simulated predator cues likely affect deer vigilance more acutely in denser habitats, which presumably facilitates predation success. Such responses were however absent among deer in the LP that are presumably naïve towards wolf predation.</p> <p>Where human and non-human predators hunt shared prey, such as in Michigan, predators may constrain human hunting success by increasing deer vigilance. Hunters may avoid such exploitative competition by choosing hunting/bait sites located in open areas. Our results pertaining to fundamental predation ecology have strong applied implications that can promote human-predator coexistence.</p>
Data from: Genetic analysis of red deer (Cervus elaphus) administrative management units in a human-dominated landscape - patterns of genetic diversity, population structure and gene flow
<p><span><span>Red deer (</span><span><em>Cervus elaphus</em></span><span>) throughout central Europe are</span> impacted by different anthropogenic activities including habitat fragmentation, selective hunting, and translocations<span>. This has substantial influences on genetic diversity and the long-term conservation of local populations of this species. Here we use genetic samples from 480 red deer individuals to assess the genetic diversity and differentiation of the 12 administrative management units located in Schleswig Holstein, the northernmost federal state in Germany. </span></span><span><span>We applied multiple analytical approaches and show that the history of local populations (i.e., translocations, culling of individuals outside of designated red deer zones, and anthropogenic infrastructures) has led to comparably low levels of genetic diversity. The mean expected heterozygosity was below 0.6 and we observed on average 4.2 alleles across 12 microsatellite loci. Effective population sizes below the recommended level of 50 were estimated for multiple local populations. </span></span><span><span>Our estimates of genetic structure and gene flow show that red deer in northern Germany are best described as a complex network of asymmetrically connected subpopulations, with high genetic exchange among some local populations and reduced connectivity of others. Genetic diversity was also correlated with population densities of neighboring management units. </span></span></p> <p><span><span>Based on these findings, we suggest that connectivity among existing management units needs to be considered in the practical management of the species, which means that some administrative management units should be managed together, while the effective isolation of other units needs to be mitigated.</span></span></p>
Table 2 Cycle threshold obtained for the 6 in Investigation on possible transmission of monkeys' Plasmodium to human in a populations living in the equatorial rainforest of the Democratic republic of Congo
<p><b>Table 2</b> Cycle threshold obtained for the 6 plasmodium species.</p><table><tbody><tr><th></th><th>P. falciparum</th><th>P. malaria</th><th>P. ovale</th><th>P. vivax</th><th>P. vinckei</th><th><i>P. berghei</i></th></tr></tbody><tbody><tr><th>1st RT-PCR</th><td>16.98</td><td>26.01</td><td>26.08</td><td>30.15</td><td>15.18</td><td>20.33</td></tr><tr><th>2nd RT-PCR</th><td>18.07</td><td>26.69</td><td>26.66</td><td>30.48</td><td>No signal</td><td>No signal</td></tr></tbody></table>
Table 1 in Investigation on possible transmission of monkeys' Plasmodium to human in a populations living in the equatorial rainforest of the Democratic republic of Congo
<p><b>Table 1</b> Primers and probes sequences for the two RT-PCR.</p><table><tbody><tr><th>RT-PCR</th><th>Forward primer</th><th>Reverse primer</th><th>Probes</th></tr></tbody><tbody><tr><th><i>plasmodiums</i> <i>sp</i></th><td>5Į- GTTAAGGGAGTGAAGACGA TCAGA-3' (Plasmo1)</td><td>5Į- TTATGAGAAATCAAAGTCTTTGGGTT-3' (Plasmo2)</td><td>5Į- FAM-ACCGTCGTAA TCTTAACCAT AAACTATGCC GACTAG-TAMRA- 3' (plasprobe)</td></tr><tr><th><i>Pfal</i></th><td>5Į- CTAGGTGTTGGATG-3Į</td><td></td><td>5Į- FAM-AGCAATCTAAAAGTCACCTCGAAAGATGACT-DQ-3' (Falprobe)</td></tr><tr><th><i>Pova</i></th><td>5Į- CGACTAGGTTTTGGATG-3Į</td><td></td><td>5Į- VIC-CGAAAGGAATTTTCTTATT-DQ-3’ (Ovaprobe)</td></tr><tr><th><i>Pviv</i></th><td>5Į- GACTAGGCTTTGGATG-3Į</td><td></td><td>5Į- VIC-AGCAATCTAAGAATAAACTCCGAAGAGAAAATTCT-DQ-3' (Vivprobe)</td></tr><tr><th><i>Pmal</i></th><td>5Į- GACTAGGTGTTGGATG-3Į</td><td></td><td>5Į- FAM-CTATCTAAAAGAAACACTCAT-DQ-3’ (Malprobe)</td></tr></tbody></table>
Replication data for: Demographic declines and responses of breeding bird populations to human footprint in the Athabasca Oil Sands Region, Alberta, Canada
<p class="MsoNormal">This data package includes data files and an R script to reproduce results reported in the paper "Demographic declines and responses of breeding bird populations to human footprint in the Athabasca Oil Sands Region, Alberta, Canada". Analyses include hierarchical multispecies models applied to data from 31 bird species at 38 Monitoring Avian Productivity and Survivorship (MAPS) stations to assess 10-year (2011–2020) demographic trends and responses to energy sector disturbance (human footprint proportion) in the Athabasca oil sands region of Alberta, Canada. Adult captures, productivity, and residency probability all declined over the study period, and adult apparent survival probability also tended to decline. Trends in adult captures, productivity, and survival were all more negative at stations with larger increases in disturbance over the study period. Species associated with early seral stages were more commonly captured at more disturbed stations, while species typical of mature forests were more commonly captured at less disturbed stations. Productivity was positively correlated with disturbance within 5 km of stations after controlling for disturbance within 1 km of stations. Adult apparent survival showed relatively little response to disturbance; stresses experienced beyond the breeding grounds likely play a larger role in influencing survival. Residency probability was negatively related to disturbance within 1-km scale of stations and could reflect processes affecting the ability of birds to establish or maintain territories in disturbed landscapes.</p>
Data from: Population and community consequences of perceived risk from humans in wildlife
<p><span>Human activities catalyze risk avoidance behaviors in wildlife across taxa and systems. However, the broader ecological significance of human-induced risk perception remains unclear, with a limited understanding of how phenotypic responses scale up to affect population or community dynamics. We conducted a comprehensive literature review of non-consumptive effects (NCE; population effects) and trait-mediated indirect effects (TMIE; community effects) of anthropogenic disturbances. This dataset includes all papers identified from the comprehensive review of the different types of human-induced behavioral and physiological phenotypic change and their influence on vital rates and population parameters in wildlife. All papers in this database tested for a human-induced NCE or TMIE in wildlife but not all found evidence for an effect. Many of the papers did not explicitly measure the presumed phenotypic change linking human activity to vital rates or population parameters. The authors, paper title, journal, publication year, type of human disturbance, species, system, phenotypic response measured, demographic response measured, if a demographic effect was found, and whether an NCE or TMIE was tested are all included in the dataset. In addition, we include the source of the paper in our dataset (i.e. whether it came up in our Web of Science search, as a citing paper of Frid and Dill (2002), or in a review paper on human-induced fear in wildlife; column A). The papers in which multiple NCE or TMIE pathways were tested may have multiple values in a single cell. Papers are sorted alphabetically by author. Evidence for human-induced NCEs and TMIEs is mixed, with half of published studies finding a relationship between human activities, phenotypic change, and population outcomes. Strong research biases in taxa, systems, human disturbance type, and demographic measures prevent unified inference about the prevalence of population responses to human activities. Coexistence with and conservation of wildlife requires additional research linking human-induced phenotypic change to population and community outcomes.</span></p>
Human population dynamics in Upper Paleolithic Europe inferred from fossil dental phenotypes
<p>Despite extensive archaeological research, our knowledge of the human population history of Upper Paleolithic Europe remains limited, primarily due to the scarce availability and poor molecular preservation of fossil remains. As teeth dominate the fossil record and preserve genetic signatures in their morphology, we compiled a large dataset of 450 dentitions dating between ~47-7 thousand years ago (kya), outnumbering existing skeletal and paleogenetic datasets. We tested a range of competing demographic scenarios using a newly developed coalescent-based machine-learning Approximate Bayesian Computation (ABC) framework designed specifically for use with phenotypic data. Mostly in agreement with, but also challenging some of the hitherto available evidence, we identified a population turnover in western Europe at ~28 kya, isolates in western and eastern refugia between ~28-14.7 kya, and bottlenecks during the Last Glacial Maximum. Methodologically, this study marks the pioneering application of ABC to skeletal phenotypes, paving the way for exciting new research avenues.</p>
Human head models and populational framework for simulating brain stimulations: part 6
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 6. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p>
Human head models and populational framework for simulating brain stimulations: part 4
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 4. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p>
Human head models and populational framework for simulating brain stimulations: part 3
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 3. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></p>
Human head models and populational framework for simulating brain stimulations: part 5
<p>We share an organized computational model data collection for noninvasive brain stimulation (NIBS) modeling. This dataset includes a subset of the collection's 100 preprocessed, quality-assured, realistic head models based on imaging data from the Human Connectome Project (HCP) s1200 release (Van Essen et al., 2012). We provide verified finite-element meshes, underlying anatomical images, tissue segmentations, standard space co-registrations, quality metrics, and lead-field matrices. We suggest individual tissue conductivity values for each head model from a range of biologically plausible values (McCann et al., 2019). Additionally, we provide straightforward computer code for several use cases of the current dataset. </p> <p><strong> </strong></p> <ol> <li> <p>McCann, H., Pisano, G., & Beltrachini, L. (2019). Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5), 825–858. <a href="https://doi.org/10.1007/s10548-019-00710-2">https://doi.org/10.1007/s10548-019-00710-2</a></p> </li> <li> <p>Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., … WU-Minn HCP Consortium. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018</p> </li> </ol> <p> </p> <p>This dataset is split into 6 parts, you are currently on part 5. All parts are linked below:</p> <p>Dataset_1: <a href="../records/13259679">https://zenodo.org/records/13259679</a></p> <p>Dataset_2: <a href="../records/13259747">https://zenodo.org/records/13259747</a></p> <p>Dataset_3: <a href="../records/13259943">https://zenodo.org/records/13259943</a></p> <p>Dataset_4: <a href="../records/13260068">https://zenodo.org/records/13260068</a></p> <p>Dataset_5: <a href="../records/13259599">https://zenodo.org/records/13259599</a></p> <p>Dataset_6: <a href="../records/13260208">https://zenodo.org/records/13260208</a></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.