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145 results for “human adaptation”
Adaptive Introgression in Modern Human Circadian Rhythm Genes Datasets
<p><strong>README:</strong></p> <p>Modern human genetic data with evidence of adaptive introgression from Neanderthals or Denisovans within circadian rhythm genes. The data was generated from the phased gnomAD 1KGP + HGDP callset (Koenig <em>et al</em>., 2024) and introgressed segments were identified by SPrime (Browning <em>et al</em>., 2018). Genes of interest were downloaded from the Circadian Genome Database (CGDB) (Li <em>et al</em>., 2017). Additional variants, haplotypes, and genes that have been previously reported to influence circadian rhythm or chronotype that are thought to be derived from Neanderthals and Denisovans were compiled from Dannemann & Kelso (2017), McArthur et al. (2021), Dannemann et al. (2022), and Velazquez-Arcelay et al. (2023).</p> <p><strong>SPrime ND_Match Files</strong></p> <p>Raw SPrime identified files that we used for our entire analysis. These were modified to include the archaic allele, archaic allele frequency, and average introgressed segment allele frequency. Note that these have been lifted over (Hinrichs <em>et</em> <em>al</em>., 2006) from GRCh38 (hg38) to GRCh37 (hg19) coordinates to match the genome builds of the archaic samples used in our study. As such, any manually generated variant IDs (chromosome:position:ReferenceAllele_AlternativeAllele naming convention) may no longer match the position they are currently sitting on as they were generated with hg38 coordinates. However, all of these were subsequently filtered out of our final results and any proper SNP IDs (dbSNP labels) will be accurate.</p> <p><strong>Supplementary Tables</strong></p> <p>All supplementary tables have an associated README as the first sheet that explains in detail the contents.</p> <p><strong>NEXUS Files</strong></p> <p>NEXUS files were used to generate haplotype networks in PopArt (Leigh & Bryant, 2015). There is a larger, master haplotype file and a smaller subset file. The larger file contains 668 haplotypes from all populations generated in the phased gnomAD 1KGP + HGDP callset (Koenig <em>et al</em>., 2024) for the <em>SUSD1 </em>core haplotype. The smaller subset file is the top 50 haplotypes and ties based on frequency, all Oceanic haplotypes with frequencies of at least 2, and the Neanderthal and Denisovan haplotypes for <em>SUSD1</em>. </p> <p><strong>TRAITS file</strong></p> <p>Accompanies the NEXUS files to create pie graphs for the haplotype network and contains frequency counts of number of haplotypes per region.</p>
Dataset for: Infectious disease responses to human climate change adaptations
<p>Original and derived data products referenced in the original manuscript are provided in the data package.</p> <h3>Description of the data and file structure</h3> <p><em>Original data:</em></p> <p><code>Table_1_source_papers.csv</code>: Papers that met review criteria and which are summarized in Table 1 of the manuscript.</p> <ol> <li><strong>ID</strong>: The paper identification number</li> <li><strong>Topic</strong>: The broad topic (i.e., each row of Table 1)</li> <li><strong>Authors:</strong> The names of the authors of the paper</li> <li><strong>Article Title</strong>: The title of the paper</li> <li><strong>Source Title</strong>: The name of the journal in which the paper was published</li> <li><strong>Abstract</strong>: The paper's abstract, retrieved from the Web of Science search</li> <li><strong>study_type:</strong> Classification of the study methodology/approach. "A" = a designed study that shows effect ,"B" = a pre/post study, "C" = a comparison of health outcomes or pathogen risk relative to a 'control/comparison' area, "D" = some quantitative effect but no control, "E" = qualitative comments but little supporting evidence, and/or a qualitative review.</li> <li><strong>pathogen_broad</strong>: Broad classification of the type of pathogen discussed in the paper.</li> <li><strong>transmission_type</strong>: Categorization of indirect, direct, sexual, vector, or other transmission modes.</li> <li><strong>pathogen_type</strong>: Categorization of bacteria, helminth, virus, protozoa, fungi, or other pathogen types.</li> <li><strong>country:</strong> Country in which the study was performed or results discussed. When countries were not available, regions were used. NA values indicate papers in which a geographic region was not relevant to the study (i.e., a methods-based study).</li> </ol> <p><em>Derived data:</em></p> <p><code>change_livestock_country.csv:</code> A dataframe containing values used to generate Figure 4a in the manuscript.</p> <ol> <li><strong>County Name</strong>: The name of the county in Kenya</li> <li><strong>Sheep and goats 1980</strong>: The estimated number of sheep and goats in 1980</li> <li><strong>Sheep and goats 2016</strong>: The estimated number of sheep and goats in 2016</li> <li><strong>pct_change_shoat</strong>: The percent change in sheep and goat numbers from 1980 to 2016</li> <li><strong>Cattle 1980</strong>: The estimated number of cattle in 1980</li> <li><strong>Cattle 2016</strong>: The estimated number of cattle in 2016</li> <li><strong>pct_change_cattle</strong>: The percent change in cattle numbers from 1980 to 2016</li> <li><strong>Camel 1980</strong>: The estimated number of camels in 1980</li> <li><strong>Camel 2016</strong>: The estimated number of camels in 2016</li> <li><strong>pct_change_camel</strong>: The percent change in camel numbers from 1980 to 2016</li> <li><strong>human_pop 1980</strong>: The estimated human population in the county in 1980</li> <li><strong>human_pop 2016</strong>: The estimated human population in the county in 1980</li> <li><strong>pct_change_human</strong>: The percent change in the human population from 1980 to 2016</li> <li><strong>area_sq_km</strong>: The land area of the county</li> <li><strong>change_ind_per_sq_km_shoat:</strong> Absolute change in number of sheep and goats from 1980 to 2016</li> <li><strong>change_ind_per_sq_km_cattle:</strong> Absolute change in number of cattle from 1980 to 2016</li> <li><strong>change_ind_per_sq_km_camel:</strong> Absolute change in number of camels from 1980 to 2016</li> </ol> <p><code>country_avg_schist_wormy_world.csv</code>: A dataframe containing values used to generate Figure 3 in the manuscript.</p> <ul> <li><strong>Country:</strong> The country in which the schistosome prevalence studies were performed.</li> <li><strong>Latitude:</strong> The latitute in decimal degrees</li> <li><strong>Longitude:</strong> The longitute in decimal degrees</li> <li><strong>Maximum.prevalence:</strong> The mean maximum schistosomiasis prevalence of studies conducted within each country.</li> </ul> <p><code>kenya_precip_change_1951_2020.csv</code>: A dataframe containing values used to generate Figure 4b in the manuscript.</p> <ul> <li><strong>Precipitation (mm):</strong> Binned annual precipitation values</li> <li><strong>1951-1980:</strong> The density of observations for each annual precipitation value for the 1951-1980 period</li> <li><strong>1971-2000:</strong> The density of observations for each annual precipitation value for the 1971-2000 period</li> <li><strong>1991-2020:</strong> The density of observations for each annual precipitation value for the 1991-2020 period</li> </ul> <h3>Sharing/Access information</h3> <p>Data were derived from the following sources:</p> <ul> <li> <p>Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? <em>PloS ONE</em>, <em>11</em>(9), e0163249. https://doi.org/10.1371/journal.pone.0163249</p> </li> <li> <p>London Applied & Spatial Epidemiology Research Group (LASER). (2023). <em>Global Atlas of Helminth Infections: STH and Schistosomiasis</em> [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0</p> </li> <li> <p>World Bank Group. (2023). <em>Climate Data & Projections—Kenya</em>. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections</p> </li> </ul>
Evaluation of an adapted semi-automated DNA extraction for human salivary shotgun metagenomics
<p>This deposit contains :</p> <p>- a RMarkdown filte containing the codes for the mcirobial analysis of saliva samples</p> <p>- the html report with codes, results and figures</p> <p>- a RData containing microbial datasets (MSp species abundance table, genus, family and phylum abundance tables, matrix of genes correlations, taxonomy)</p> <p>- a RData containing associated metadata </p>
Local adaptation and archaic introgression shape global diversity at human structural variant loci
<p>Supporting data associated with the manuscript "Local adaptation and archaic introgression shape global diversity at human structural variant loci". These include:</p> <ul> <li>structural variant genotypes (Paragraph; <a href="https://github.com/Illumina/paragraph">https://github.com/Illumina/paragraph</a>)</li> <li>eQTL mapping results (fastqtl permutation pass; see <a href="http://fastqtl.sourceforge.net/">http://fastqtl.sourceforge.net/</a> for column descriptions)</li> <li>eQTL fine-mapping results (CAVIAR; see <a href="http://genetics.cs.ucla.edu/caviar/index.html">http://genetics.cs.ucla.edu/caviar/index.html</a>)</li> <li>structural variant selection scan results (Ohana; <a href="https://github.com/jade-cheng/ohana">https://github.com/jade-cheng/ohana</a>)</li> </ul> <p>Description of files in this directory:</p> <p><strong>Structural variant genotypes</strong></p> <p><code>SVs_paragraphFormat.vcf.gz</code> - merged long-read structural variant calls</p> <p><code>SVs_1KGP_pgGTs.vcf.gz</code> - genotypes for 1000 Genomes samples in VCF format</p> <p><strong>eQTL mapping results</strong></p> <p><code>fastqtl_out.txt</code> - results from fastQTL permutation pass; see <a href="http://fastqtl.sourceforge.net/">http://fastqtl.sourceforge.net/</a> for column descriptions</p> <p><code>caviar_out.txt</code> - results from fine-mapping SNPs and SVs at significant SV eQTL loci with CAVIAR. Description of columns:</p> <ul> <li>query_sv: SV that was a significant eQTL and underwent fine-mapping</li> <li>gene_id: gene exhibiting an expression association with the query_sv</li> <li>var_id: variant (SNV or SV) that was tested for expression association with the above gene in the fine-mapping analysis</li> <li>var_in_credible_causal_set: Boolean variable denoting whether the above variant is in the 95% credible causal set</li> <li>prob_in_pcausal_set: the amount that this variant contributes to 95% credible causal set</li> <li>causal_post_prob: the posterior probability that the variant is causal in the expression association</li> </ul> <p><strong>Structural variant selection scan results</strong></p> <p><code>chr21_pruned_50_Q.matrix</code> - admixture proportion matrix (generated by Ohana; <a href="https://github.com/jade-cheng/ohana">https://github.com/jade-cheng/ohana</a>)</p> <p><code>chr21_pruned_50_F.matrix</code> - matrix of inferred ancestral allele frequencies (generated by Ohana)</p> <p><code>chr21_pruned_50_C.matrix</code> - matrix of ancestry component covariances (generated by Ohana) Entries of the matrix can be modified to produce "selection hypothesis" matrices where allele frequencies are allowed to vary in one ancestry component (<a href="https://github.com/jade-cheng/ohana/wiki/Population-or-ancestry-specific-selection-scan">https://github.com/jade-cheng/ohana/wiki/Population-or-ancestry-specific-selection-scan</a>).</p> <p><code>selscan_50_k8_p*.txt.gz</code> - raw output of Ohana selscan (see <a href="https://github.com/jade-cheng/ohana">https://github.com/jade-cheng/ohana</a>)</p> <p><code>selscan_res.txt.gz</code> - Ohana selection scan results. These results have been filtered to exclude SVs that have low genotyping rates (<50% of samples), violate Hardy-Weinberg equilibrium expectations (excess of heterozygotes) in more than half of populations, or have extreme global log likelihood estimate (LLE) values. Description of columns:</p> <ul> <li>ID: SV ID</li> <li>#CHROM: SV chromosome</li> <li>POS: SV start position</li> <li>SVLEN: SV length (negative for deletions)</li> <li>step: number of steps needed to interpolate between genome-wide and selection hypothesis models</li> <li>lle_ratio: likelihood ratio statistic (LRS) of the genome-wide vs. selection hypothesis model</li> <li>global-lle: log likelihood of the genome-wide model</li> <li>local-lle: log likelihood of the selection hypothesis model</li> <li>f-pop0: inferred allele frequency in ancestry component 0</li> <li>f-pop1: inferred allele frequency in ancestry component 1</li> <li>f-pop2: inferred allele frequency in ancestry component 2</li> <li>f-pop3: inferred allele frequency in ancestry component 3</li> <li>f-pop4: inferred allele frequency in ancestry component 4</li> <li>f-pop5: inferred allele frequency in ancestry component 5</li> <li>f-pop6: inferred allele frequency in ancestry component 6</li> <li>f-pop7: inferred allele frequency in ancestry component 7</li> <li>ancestry_component: ancestry component tested by the selection hypothesis model. Note that we have added 1 to the ancestry component numbers to match the terminology used in paper (which orders the components from 1-8 rather than 0-7 for interpretability)</li> <li>snp_perc: SV's percentile in the LRS distribution for frequency-matched SNPs</li> <li>p_nominal: nominal p-value calculated from the likelihood ratio</li> <li>p_adj: adjusted p-value calculated from the likelihood ratio</li> </ul> <p> </p>
Figure 1. Necker cube depth illusion (Adapted from [http://en.wikipedia.org/wiki/Necker_cube])-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>Often the term Gestalt is used interchangeably with the term “emergent whole” (Johansson,<br> 1998). The emergence of a cognitive Gestalt structure adds dynamical and psychophysical forces,<br> which are different from the static notion of the “emergent whole”. An eminent example for the<br> dynamic nature of the emergent process is the Necker cube, which cannot be perceived as static, but<br> rotates in front of our eyes to the complete exhaustion of the eye gazing process (figure 1).</p>
Figure 7. Kanizsa square makes us see a non-existing figure – white square (Adapted from [http://en.wikipedia.org/wiki/Optical_illusion]-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>A special case of Gestalt processing is the perceiving of illusions. Illusions make us see<br> things or processes that are not there – for example the Kanizsa square like the one depicted in<br> figure 7.</p>
Data from: Adaptive multi-objective control explains how humans make lateral maneuvers while walking
<p>To successfully traverse their environment, humans often perform maneuvers to achieve desired task goals while simultaneously maintaining balance. Humans accomplish these tasks primarily by modulating their foot placements. As humans are more unstable laterally, we must better understand how humans modulate lateral foot placement. We previously developed a theoretical framework and corresponding computational models to describe how humans regulate lateral stepping during straight-ahead continuous walking. We identified goal functions for step width and lateral body position that define the walking task and determine the set of all possible task solutions as Goal Equivalent Manifolds (GEMs). Here, we used this framework to determine if humans can regulate lateral stepping during non-steady-state lateral maneuvers by minimizing errors consistent with these goal functions. Twenty young healthy adults each performed four lateral lane-change maneuvers in a virtual reality environment. Extending our general lateral stepping regulation framework, we first re-examined the requirements of such transient walking tasks. Doing so yielded new theoretical predictions regarding how steps during any such maneuver should be regulated to minimize error costs, consistent with the goals required at each step and with how these costs are adapted at each step during the maneuver. Humans performed the experimental lateral maneuvers in a manner consistent with our theoretical predictions. Furthermore, their stepping behavior was well modeled by allowing the parameters of our previous lateral stepping models to adapt from step to step. To our knowledge, our results are the first to demonstrate humans might use evolving cost landscapes in real time to perform such an adaptive motor task and, furthermore, that such adaptation can occur quickly – over only one step. Thus, the predictive capabilities of our general stepping regulation framework extend to a much greater range of walking tasks beyond just normal, straight-ahead walking.</p>
Data from: Adaptive multi-objective control explains how humans make lateral maneuvers while walking
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IntelliMan_WP4_Adaptive Shared Autonomy_T4.2_Advanced human-robot interaction modalities_human robot handover_v0
<p><span>The dataset contains data related to the experiments presented in the publication:</span></p> <p><em><span>M. Costanzo, C. Natale and M. Selvaggio, "Visual and Haptic Cues for Human-Robot Handover*," 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Busan, Korea, Republic of, 2023, pp. 2677-2682, doi: 10.1109/RO-MAN57019.2023.10309480.</span></em></p>
Separability of human motor memories during reaching adaptation with force cues
<p><span>Judging by the breadth of our motor repertoire during daily activities, it is clear that learning different tasks is a hallmark of the human motor system. However, for reaching adaptation to different force fields, the conditions under which this is possible in laboratory settings have remained a challenging question. Previous work has shown that independent movement representations or goals enabled dual adaptation. Considering the importance of force feedback during limb control, here we hypothesised that independent cues delivered by means of background loads could support simultaneous adaptation to various velocity-dependent force fields, for identical kinematic plan and movement goal. We demonstrate in a series of experiments that indeed healthy adults can adapt to opposite force fields, independently of the direction of the background force cue. However, when the cue and force field were in the same direction but differed by their magnitude, the formation of different motor representations was still observed but the associated mechanism was subject to increased interference. Finally, we highlight that this paradigm allows dissociating trial-by-trial adaptation from online feedback adaptation, as these two mechanisms are associated with different time scales that can be identified reliably and reproduced in a computational model. </span></p>
Data and Scripts associated with "Fisher activity patterns show potential for behavioural adaptations to human modified landscapes"
<p>Script and data for running both models from manuscript. Data contains daily and nightly mean activity levels (ODBA) for fisher captured from 2021-2023 in Rhode Island, USA. </p>
Data from: Adaptations to climate-mediated selective pressures in humans.
<p>This dataset contains the genotype data in PLINK binary format for the 5 populations genotyped in the Di Rienzo lab and published in </p> <p>Hancock AM, Witonsky DB, Alkorta-Aranburu G, Beall CM, Gebremedhin A, Sukernik R, Utermann G, Pritchard JK, Coop G, Di Rienzo A (2011) Adaptations to climate-mediated selective pressures in humans. PLoS Genet. 7(4):e1001375</p>
DAGHAR: A Benchmark for Domain Adaptation and Generalization in Smartphone-Based Human Activity Recognition
<p>DAGHAR benchmark is a curated dataset collection designed for domain adaptation and domain generalization studies in HAR tasks, using inertial sensors such as accelerometers and gyroscopes, from "A benchmark for domain adaptation and generalization in smartphone-based human activity recognition" work. It features raw inertial sensor data sourced exclusively from smartphones. Six public datasets were selected and standardized in terms of accelerometer units of measurement, sampling rate, gravity component, activity labels, user partitioning, and time window size. This standardization process allows for creating a comprehensive benchmark for evaluating the generalization capabilities of HAR models in cross-dataset scenarios.</p> <p>The benchmark is based on the following datasets:</p> <ul> <li><strong>Ku-HAR</strong>, from "Sikder, N. and Nahid, A.A., 2021. KU-HAR: An open dataset for heterogeneous human activity recognition. Pattern Recognition Letters, 146, pp.46-54", avaliable at <a href="https://data.mendeley.com/datasets/45f952y38r/5">Mendeley</a>. Distributed under CC BY 4.0.</li> <li><strong>MotionSense</strong>, from "Malekzadeh, M., Clegg, R.G., Cavallaro, A. and Haddadi, H., 2019, April. Mobile sensor data anonymization. In Proceedings of the international conference on internet of things design and implementation (pp. 49-58)", available at <a href="https://www.kaggle.com/datasets/malekzadeh/motionsense-dataset" target="_blank" rel="noopener">Kaggle</a>. Distributed under Open Data Commons Open Database License (ODbL) v1.0.</li> <li><strong>RealWorld</strong>, from "Sztyler, T. and Stuckenschmidt, H., 2016, March. On-body localization of wearable devices: An investigation of position-aware activity recognition. In 2016 IEEE international conference on pervasive computing and communications (PerCom) (pp. 1-9). IEEE", available at <a href="https://www.uni-mannheim.de/dws/research/projects/activity-recognition/dataset/dataset-realworld/" target="_blank" rel="noopener">this link</a>. We obtained explicitly permission to distribute a copy of the preprocessed data from the original authors.</li> <li><strong>UCI-HAR</strong>, from "Reyes-Ortiz, J.L., Oneto, L., Samà, A., Parra, X. and Anguita, D., 2016. Transition-aware human activity recognition using smartphones. Neurocomputing, 171, pp.754-767", available at <a href="https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones">UCI Repository</a>. Distributed under CC BY 4.0.</li> <li><strong>WISDM</strong>, from "Weiss, G.M., Yoneda, K. and Hayajneh, T., 2019. Smartphone and smartwatch-based biometrics using activities of daily living. Ieee Access, 7, pp.133190-133202", available at <a href="https://archive.ics.uci.edu/dataset/507/wisdm+smartphone+and+smartwatch+activity+and+biometrics+dataset">UCI repository</a>. Distributed under CC BY 4.0.</li> </ul>
Ancient genomes from Bronze Age remains reveal deep diversity and recent adaptive episodes for human oral pathobionts
<p>Supporting data for "Ancient genomes from Bronze Age remains reveal deep diversity and recent adaptive episodes for human oral pathobionts"</p>
Visual Activity Evoked by Infrared in Humans After Dark Adaptation
ClinicalTrials.gov study NCT02909985. IPD Sharing: NO. Countries: 1. Publications: 14.
Data from: Biogeography of shell morphology in over-exploited shellfish reveals adaptive tradeoffs on human-inhabited islands and incipient selectively driven lineage bifurcation
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Data from: Africa-wide diversification of livelihoods strategies: Isotopic insights into Holocene human adaptations to climate change
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Separability of human motor memories during reaching adaptation with force cues
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The diel niche of brown bears: constraints on adaptive capacity in human-modified landscapes
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Data for: Quantifying human adaptation to a novel split-belt walking condition after broad experience at different belt speeds
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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.