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12,871 results for “aging;”
Data for "Nonagenarian men and women in Switzerland at the end of the 19th century: Inspecting verified outliers at the top end of the historical age distribution"
<p>Datasets underlying the analysis of the paper: "Nonagenarian men and women in Switzerland at the end of the 19th century: Inspecting verified outliers at the top end of the historical age distribution"</p> <ul> <li><strong>Data_1888_1900.xlsx</strong> : Individual data of persons aged 90 and older from the 1888 and 1900 censuses</li> <li><strong>Cofactors.xlsx</strong> : Ecological co-factors for each Swiss district</li> </ul>
Initial status and change in cognitive function mediate the association between academic education and physical activity in adults over 50 years of age
<p>The scripts for data management (i.e., from raw data to data used in the analyses) and for the models tested in the paper titled "Initial status and change in cognitive function mediate the association between academic education and physical activity in adults over 50 years of age".</p> <p>“SHARE_Data_merge_w8.R” --> merges the data between the 8 waves of measurement.</p> <p>“SHARE_computingVars_w8.R” --> computes the variables used in the abovementionned study.</p> <p>“Growth_curve_models.R” --> contains the statistical models tested in the abovementionned study.</p>
Paired blood-brain transcriptomes (GTEx v8), age and sex preserved
<p>This dataset includes pre-normalized paired blood-brain RNA-sequencing profiles from donors included in the Genotype Tissue Expression (GTEx) Project (v8). Variances in gene expression associated with age and sex were preserved during normalization. These data are formatted for BrainGENIE. </p>
Hygro-thermo-mechanical model for concrete pavement from an early age to a long-term performance
<p>A new 3D hygro-thermo-mechanical model for a concrete pavement slab is formulated, capturing its behavior from casting to long-term drying. The model includes a microprestress-solidification viscoelastic model for concrete ageing creep. Calibrations and validations are based on lab experiments and a four year continuous monitoring of a real highway slab. The results show that slab drying leads to the highest principal tensile stress on the surface as high as 3.5 MPa. Simulated temperature cycles and traffic loading yield smaller stresses. In this regard, concrete crack resistance during drying should be considered in material and structural design.</p>
Hand mask for the RSNA bone age dataset
<p>Masks for semantic segmentation of hands from scanned X-Rays in the RSNA Bone Age dataset (released for the <a href="https://www.rsna.org/education/ai-resources-and-training/ai-image-challenge/rsna-pediatric-bone-age-challenge-2017">RSNA Pediatric Bone Age Challenge</a> in 2017).</p> <p>The masks were obtained manually using thresholding and edge detection and all masks were quality checked and, if needed, corrected.</p> <p>Based on this two models (Tensormask and Efficient-UNet) were trained to obtain the masks on the full RSNA Bone Age dataset.</p> <p> </p> <p>If you use this dataset for your work, please cite the paper this dataset is part of:</p> <p>Rassmann, S., Keller, A., Skaf, K. <em>et al.</em> Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias. <em>Pediatr Radiol</em> <strong>54</strong>, 82–95 (2024). https://doi.org/10.1007/s00247-023-05789-1 </p> <p> </p>
Scheib_Stoll_Randerath_Frontiers_Does Aging Amplify the Rule-based Efficiency Effect in Action Selection_Dataset
<p>Dataset belongs to publication in Frontiers in Psychology, section Cognition</p> <p>Title: Does Aging Amplify the Rule-based Efficiency Effect in Action Selection?<br> Authors: Jean P. P. Scheib 1; Sarah E. M. Stoll 1,2; Jennifer Randerath 3,2,1</p> <p>Affiliations:<br> 1: University of Konstanz, Konstanz, Germany,</p> <p>2: Lurija Institute for Rehabilitation Science and Health Research, Kliniken Schmieder, Allensbach, Germany</p> <p>3: University of Vienna, Vienna, Austria</p>
The Comprehensive Brazilian Age Database
<p>The archaeological database is composed mainly of radiocarbon ages, and they were subject to calibration using the CalPal program (Weninger and Jöris 2008), version 2020.11.</p> <p>In some portions of Brazil, OSL and TL ages were widely used, and we had to cope with this issue by means of a “reverse calibration”, i.e., entering ages into the CalPal program and running the calibration until a given (fictious) radiocarbon age, once calibrated, matched as closely as possible the luminescence age. The TL/OSL ages are marked in gray, with blue numbers standing for the “fictious” age, and the actual age appearing in the “Cal age” column.</p> <p>We also decided to be inclusive in our database, and by this we mean that we are making available all radiocarbon or luminescence ages that were considered <em>bona fide</em> by the researchers who published them, regardless of the fact that other researchers consider these ages inconsistent with their own models or beliefs. The same goes for papers that select ages based on the standard deviations. A large standard deviation means low precision, not necessarily low accuracy. We take for granted that judgements about the appropriateness of the ages can be made individually by the reader, since we provided the full references. We prefer to publish an age with a large associated error than to ignore it. Once again, since we are providing the tables as supplementary material, it is up to the reader to disregard specific ages and run his/her own analysis.</p> <p>In terms of the geographic location of the sites/ages, we chose to provide UTM coordinates of the nearest municipality, instead of providing “exact” locations. This decision was made on three grounds: first, in the scale of analysis we are presenting, the location of the nearest municipality is more than sufficient to provide an adequate overview of the spatial distribution of the ages; second, the majority of the sites published before the advent and popularization of handheld GPS devices do not have an accurate location and therefore, to provide an “exact” location would be meaningless; third, when trying to plot sites using available databases, be they compilations of data or first publications of a given site, it is common to observe that the apparently “exact” geographic coordinates were plainly wrong, falling outside a given region or even the state. This is something that plagues large databases, generally compiled by several researchers and their students, so we argue that it is much easier to detect errors and convey the right location of a given site, at least approximately, if the municipality is taken into account. Hence, our database has a redundant location scheme: state, municipality, and UTM coordinates. If by some reason the UTM is wrong, the reader at least knows in which state and municipality it is located. The only exception to this procedure was made in the Amazon region (states of Amazonas, Pará, Maranhão, Rondonia). Municipalities in the region are fairly large, and we chose to plot the location of the site when it was considered to be too far away from the nearest urbanized area.</p>
Supplementary material 1 from: Woch MW, Kapusta P, Stanek M, Możdżeń K, Grześ IM, Rożej-Pabijan E, Stefanowicz AM (2023) Effects of invasive Rosa rugosa on Baltic coastal dune communities depend on dune age. NeoBiota 82: 163-187. https://doi.org/10.3897/neobiota.82.97275
Detailed information and results of supplementary analyses on the structure of resident plant communities
Zooming in on ZooMS: New Approaches to ZooMS Protocols and Further Insights into Animal Identifications in Neolithic and Bronze Age Indus Valley Sites
<p>Method Development of the ZooMS protocol to increase success rate of taxonomic identification of heavily degraded faunal materials. The methods were tested on Indus Valley bones- samples that are known to be heavily degraded.</p>
Figure 1 in A new putative moss bug (Insecta: Hemiptera) from the lower Permian of the Saar-Nahe Basin, SW Germany, and the age of Coleorrhyncha
Figure 1. Approximate geographical and stratigraphical position of the Grügelborn locality (marked by asterisk) with rough absolute ages indicated (slightly modified from Nel and Poschmann 2020).
Figure 4. Peloridium hammoniorum Breddin 1897 in A new putative moss bug (Insecta: Hemiptera) from the lower Permian of the Saar-Nahe Basin, SW Germany, and the age of Coleorrhyncha
Figure 4. Peloridium hammoniorum Breddin 1897, an extant moss bug for comparison. Parque Nacional de Chiloé, Rancho Grande, Chile (leg. D. Burckhardt). Photograph with wing venation labelled. Scale bar = 0.2 mm.
Figure 3 in A new putative moss bug (Insecta: Hemiptera) from the lower Permian of the Saar-Nahe Basin, SW Germany, and the age of Coleorrhyncha
Figure 3. Permoridium fresenaci gen. et sp. nov., holotype UGKU 1096, explanatory sketch drawing with venation labelled. Scale bar = 5 mm.
Linking water age, nitrate export regime, and nitrate isotope biogeochemistry in a tile-drained agricultural field
<p>This repository contains the SAS model input data and the model results that can be used to reproduce the water age results for the three study tiles presented in Yu et al. Linking water age, nitrate export regime, and nitrate isotope biogeochemistry in a tile-drained agricultural field</p> <p><strong>File 1: SAS_model_input_TileX.csv</strong></p> <p>The input data for the SAS model calibration for the three tiles.</p> <p><strong>File 2: behavioral_parameter_sets_Model#1_TileX.csv</strong></p> <p>The behavioral parameter sets obtained from the calibration of SAS model 1 (i.e., time-invariant kQ).</p> <p><strong>File 3: behavioral_parameter_sets_Model#2_TileX.csv</strong></p> <p>The behavioral parameter sets obtained from the calibration of SAS model 2 (i.e., time-variant kQ).</p> <p><strong>File 4: Cl_simulation_results_Model#1_TileX.csv</strong></p> <p>Simulated chloride concentration based on the optimal parameter set of SAS model 1 (i.e., time-invariant kQ).</p> <p><strong>File 5: Cl_simulation_results_Model#2_TileX.csv </strong></p> <p>Simulated chloride concentration based on the optimal parameter set of SAS model 2 (i.e., time-variant kQ).</p> <p><strong>File 6: median_water_age_Model#1_TileX.csv</strong></p> <p>Median water age of tile discharge based on the optimal parameter set of SAS model 1 (i.e., time-invariant kQ).</p> <p><strong>File 7: median_water_age_Model#2_TileX.csv</strong></p> <p>Median water age of tile discharge based on the optimal parameter set of SAS model 2 (i.e., time-variant kQ).</p> <p><strong>File 8: File_column_names.txt</strong></p> <p> A text file that explains the column names for each file</p>
Linking water age, nitrate export regime, and nitrate isotope biogeochemistry in a tile-drained agricultural field [Dataset]
<p>This repository contains the SAS model input data and the model results that can be used to reproduce the water age results for the three study tiles presented in Yu et al. Linking water age, nitrate export regime, and nitrate isotope biogeochemistry in a tile-drained agricultural field</p> <p><strong>File 1: SAS_model_input_TileX.csv</strong></p> <p>The input data for the SAS model calibration for the three tiles.</p> <p><strong>File 2: behavioral_parameter_sets_Model#1_TileX.csv</strong></p> <p>The behavioral parameter sets obtained from the calibration of SAS model 1 (i.e., time-invariant kQ).</p> <p><strong>File 3: behavioral_parameter_sets_Model#2_TileX.csv</strong></p> <p>The behavioral parameter sets obtained from the calibration of SAS model 2 (i.e., time-variant kQ).</p> <p><strong>File 4: Cl_simulation_results_Model#1_TileX.csv</strong></p> <p>Simulated chloride concentration based on the optimal parameter set of SAS model 1 (i.e., time-invariant kQ).</p> <p><strong>File 5: Cl_simulation_results_Model#2_TileX.csv </strong></p> <p>Simulated chloride concentration based on the optimal parameter set of SAS model 2 (i.e., time-variant kQ).</p> <p><strong>File 6: median_water_age_Model#1_TileX.csv</strong></p> <p>Median water age of tile discharge based on the optimal parameter set of SAS model 1 (i.e., time-invariant kQ).</p> <p><strong>File 7: median_water_age_Model#2_TileX.csv</strong></p> <p>Median water age of tile discharge based on the optimal parameter set of SAS model 2 (i.e., time-variant kQ).</p> <p><strong>File 8: File_column_names.txt</strong></p> <p> A text file that explains the column names for each file</p>
The Vegetation Dynamics in Response to Cooling Events from the Medieval Warm Period to the Little Ice Age in Southeast China
<p>Full palynological dataset from three-peats (Wangdongyang peat bog: WDY, 27°40′48″N、119°38′15″E, elevation: 1303 m; Xiyaohu peat bog: XYH15, 28°44′N, 115°40′E, elevation: 735m and XYH14 is close to XYH15; Yuhuashan peat bog: YHS2, 27°50′29″N, 115°38′54.98″E, elevation: 882 m) in the southeast of China.</p> <p>The dataset contains raw counts for pollen and ferns.</p>
Figure 2 in Geographical, climatic and biological constraints on age at sexual maturity in amphibians
Figure 2. Histograms depicting the age at sexual maturity distribution through the sampled species for females (left) and males (right).
Co-occurrence of behavioural risk factors for non-communicable diseases among 40-year and above aged community members in three regions of Myanmar
<p>Data were collected from 660 community members in three Regions of Myanmar in 2019.</p>
Fig. 3 in Age- and sex-specific survivorship of the Southern Hemisphere long-finned pilot whale (Globicephala melas edwardii)
Fig. 3.—Age-specific mortality rates (qx) for male and female longfinned pilot whales (Globicephala melas edwardii) mass-stranded on the New Zealand coast between 2006 and 2017. Points are based on traditional life table calculations (qx) and smoothed curves were fitted using the Siler model (Siler qx). Age-class = age x to x + 1.
Fig. 1 in Age- and sex-specific survivorship of the Southern Hemisphere long-finned pilot whale (Globicephala melas edwardii)
Fig. 1.—Age distribution of female (n = 227), male (n = 154), and unknown sex (n = 3) long-finned pilot whales (Globicephala melas edwardii) mass-stranded on the New Zealand coast between 2006 and 2017.
Fig. 2 in Age- and sex-specific survivorship of the Southern Hemisphere long-finned pilot whale (Globicephala melas edwardii)
Fig. 2.—Age-specific survivorship (lx) for male and female longfinned pilot whales (Globicephala melas edwardii) mass-stranded on the New Zealand coast between 2006 and 2017. Points are based on traditional life table calculations (lx) and smoothed curves were fitted using the Siler model (Siler lx). Age-class = age x to x + 1.
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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.