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Fig. 29 in A taxonomic revision of geoemydid turtles from Siwalik-age of India and Pakistan
Fig. 29. IM E.90, referred to Clemmys sivalensis (Theobald, 1877) by Lydekker (1885a), here identified Melanochelys sivalensis (Theobald, 1877) comb. nov. A. Photograph of carapace. B. Illustration of carapace. C. Photograph of plastron. D. Illustration of plastron. Scale bar: 3 cm.
Fig. 17. BMNH 17435 in A taxonomic revision of geoemydid turtles from Siwalik-age of India and Pakistan
Fig. 17. BMNH 17435, referred to Pangshura sp. by Lydekker (1885a), herein confirmed. A. Photograph of carapace. B. Illustration of carapace. C. Photograph of plastron. D. Illustration of plastron. Scale bar: 3 cm.
Fig. 13. BMNH R.891 in A taxonomic revision of geoemydid turtles from Siwalik-age of India and Pakistan
Fig. 13. BMNH R.891, referred to Cachuga lineata (Gray, 1831) by Lydekker (1889b), here identified as Batagur kachuga (Gray, 1831). A. Photograph of carapace. B. Illustration of carapace. C. Photograph of plastron. D. Illustration of plastron. Scale bar: 3 cm.
Fig. 12. BMNH 39835a in A taxonomic revision of geoemydid turtles from Siwalik-age of India and Pakistan
Fig. 12. BMNH 39835a, holotype of Batagur bakeri Lydekker, 1885, here identified as synonym of Batagur kachuga (Gray, 1831). A. Photograph of carapace. B. Illustration of carapace. C. Photograph of plastron. D. Illustration of plastron. Scale bar: 3 cm.
Fig. 11 in A taxonomic revision of geoemydid turtles from Siwalik-age of India and Pakistan
Fig. 11. IM E.176, previously undescribed, here identified as Batagur sp. A. Photograph of carapace. B. Illustration of carapace. C. Photograph of plastron. D. Illustration of plastron. Scale bar: 3 cm.
Fig. 7. BMNH R.959 in A taxonomic revision of geoemydid turtles from Siwalik-age of India and Pakistan
Fig. 7. BMNH R.959, referred to Hardella thurgi (Gray, 1831) by Lydekker (1889b), here identified as Palatochelydia indet. A. Photograph of plastron. B. Illustration of plastron. Scale bar: 3 cm.
Fig. 1 in A taxonomic revision of geoemydid turtles from Siwalik-age of India and Pakistan
Fig. 1. Extent of the Himalayan foreland basin (green) on the Indian subcontinent, distribution of the Siwalik belt mountain range (dashed line), and the main localities that yielded geoemydid fossils. The main Siwalik-age localities here studied are marked with a red star. 1. Potwar Plateau, Punjab, Pakistan. 2. 'Typical' Siwalik Hills of India. 3. Piram Island, Gulf of Cambay, India. 4. Villages of Muwar and Doomar, Narmada Valley, India. Map modified from the United Nations, database of points from Flynn et al. (2013), range base from Valdiya (2010).
Fig. 8. BMNH 16204 in A taxonomic revision of geoemydid turtles from Siwalik-age of India and Pakistan
Fig. 8. BMNH 16204, referred to Hardella thurgi (Gray, 1831) by Lydekker (1889b), here identified as Palatochelydia indet. A. Photograph of carapace. B. Illustration of carapace. C. Photograph of plastron. D. Illustration of plastron. Scale bar: 3 cm.
Fig. 10. BMNH 39834 in A taxonomic revision of geoemydid turtles from Siwalik-age of India and Pakistan
Fig. 10. BMNH 39834, holotype of Batagur cautleyi Lydekker 1885, here identified as Batagur sp. A. Photograph of carapace. B. Illustration of carapace. C. Photograph of plastron. D. Illustration of plastron. Scale bar: 3 cm.
Fig. 3 in A taxonomic revision of geoemydid turtles from Siwalik-age of India and Pakistan
Fig. 3. Illustration of the plastron of four extant geoemydid species that were used for morphological comparison with Siwalik specimens. A. Plastron of Batagur borneoensis (Schlegel & Müller, 1845) (FMNH 251499), a geoemydid from Borneo that here illustrates the general shape of species of Batagur Gray, 1856. B. Geoclemys hamiltonii (Gray, 1830) (NMW 39986), a geoemydid from the Indian subcontinent C. Melanochelys trijuga (Schweigger, 1812) (NMW 37159), a geoemydid from the Indian subcontinent. D. Plastron of Hardella thurjii (Gray, 1831) (FMNH 224153).
Fig. 2 in A taxonomic revision of geoemydid turtles from Siwalik-age of India and Pakistan
Fig. 2. Illustration of the carapace of four extant geoemydid species that were used for morphological comparison with Siwalik specimens. A. Carapace of Batagur borneoensis (Schlegel & Müller, 1845) (FMNH 251499), a geoemydid from Borneo that here illustrates the general shape of species of Batagur Gray, 1856. B. Geoclemys hamiltonii (Gray, 1830) (NMW 39986), a geoemydid from the Indian subcontinent. C. Melanochelys trijuga (Schweigger, 1812) (NMW 37159), a geoemydid from the Indian subcontinent. D. Carapace of Hardella thurjii (Gray, 1831) (FMNH 224153), a geoemydid from India and Pakistan.
Fig. 5. BMNH R.603 in A taxonomic revision of geoemydid turtles from Siwalik-age of India and Pakistan
Fig. 5. BMNH R.603, referred to Hardella thurgi (Gray, 1831) by Lydekker (1889b), here identified as Palatochelydia indet. A. Photograph of right carapace side. B. Illustration of right carapace side. Scale bar: 3 cm.
SMART2: Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys
<p>To increase the evidence base supporting specific methods to measure social interaction, we compared data from self-reported contact surveys and wearable proximity sensors from a cohort of schoolchildren in the Pittsburgh metropolitan area.</p> <p> </p> <p>Enrollment in the Social Mixing and Respiratory Transmission (SMART) study operated on an opt-out basis, and all students registered in a participating school before the start of the study were eligible to participate. Students in kindergarten (typically aged 5 years) to 12<sup>th</sup> grade (typically aged 18 years) from two elementary (K to 4<sup>th</sup> grade, K to 5<sup>th</sup> grade), two middle (5<sup>th</sup> to 6<sup>th</sup> grade, 7<sup>th</sup> to 8<sup>th</sup> grade), two elementary-middle (K to 8<sup>th</sup> grade), and two high (both 9<sup>th</sup> to 12<sup>th</sup> grade) schools were eligible to participate in SMART. Participation rates were high in all schools (82 to 99%). Each school provided aggregate demographic information about the school population, and individual grade and sex of participating students.</p> <p><em>Proximity sensor deployments</em></p> <p>The details of proximity sensor deployments have been described in detail elsewhere (60). In brief, participating students were given proximity sensors in plastic pouches and instructed to wear the pouch around their neck for the duration of the school day without removing or otherwise tampering with the sensor. In six of the eight schools, all participating students were given a sensor; in two schools, the large student population limited the deployment to randomly selected classrooms in each grade. Deployments typically lasted from the first class period (08:00 – 09:00) to the last class period (14:00 – 15:00). Deployment days in each school were chosen to be representative of a typical school day, without any special schoolwide or grade-specific activities that could modify normal contact patterns.</p> <p> </p> <p>We used TelosB wireless sensors (61) programmed in the NesC language to send beacons every 20 seconds (beacon frequency 3 per minute). The receiving sensor recorded the contacting sensor’s identity, an internal time stamp, and a radio strength signal indicator (RSSI). Signal strength provided an estimate of physical proximity, but was highly dependent on the orientation of the two sensors and any obstructions between them and therefore could not be used to define an exact distance between contacts. Based on pilot studies and previous work on effective distances of respiratory virus transmission (29, 62), we chose a signal threshold (-80 dBm) that should correspond to contacts of relevance to respiratory disease transmission.</p> <p> </p> <p>The number of unique proximity sensor contacts recorded for a participant was defined as the total number of other participants with whom their proximity sensor recorded at least one interaction during each deployment. To explore patterns of contacts of varying length, we considered several values of the contact threshold, or the minimum number of recorded interactions between two proximity sensors required to be considered a unique contact. The number of interactions between any given pair of sensors was taken to be the maximum number of interactions recorded by either sensor, to account for battery failure, measurement error, or other malfunctions.</p> <p> </p> <p><em>Contact survey design</em></p> <p>Contact surveys were completed by participants in school under the supervision of project staff and teachers. Each sheet of the paper version allowed for information on up to 30 contacts to be recorded; additional sheets could be requested. Two versions were designed: one for middle- and high-school students, and a simplified version for elementary school students (although some elementary school children completed the middle- and high-school version, upon consultation with school administrators and teachers). Classrooms were randomly selected to participate from each grade, and students of several classrooms completed more than one contact survey over the course of the study period.</p> <p> </p> <p>Participants were asked to report information about any individual they talked with, played with, or touched the previous day, including the contact’s age and sex, whether they attended the same school as the participant, the context in which the contact was made, whether the contact involved direct or indirect (through a shared object) touch, and approximate duration of the contact. Students reported the total number of contacts made in the previous day, without detailed information, and additional demographic information about themselves and their household. The surveys were completed either on paper or by computer, depending on resources available in each school.</p> <p> </p> <p>We defined total survey contacts as the total number of individuals a student reported having interacted with on the day before the survey was completed. Detailed contacts were the subset of total contacts for which the student reported contact age, sex, duration, and context. We considered further subsets of detailed survey contacts, including those occurring within school, those reported to have lasted more than 10 minutes over the course of the day, and those occurring on the same day as a sensor deployment.</p> <p> </p>
Data for "Age effect on tree structure and biomass allocation in Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies [L.] Karst.)"
<p>VAPU dataset for tree biomass was collected from southern Finland in 1988-1990 by the Finnish Forest Research Institute (Metla, now Natural Resources Institute Finland, Luke) (VAPU data set).</p> <p>Those sample trees (162 Scots pine and 163 Norway spruce) are originated from the whole VAPU data set. The sheet 'Pine' and 'Spruce' data have been matched between 'sample branch measurements' and the 'biomass' information (by cluster X, Y, and plot, tree number).</p> <p>Biomass estimation for foliage and branches has been described here: https://doi.org/10.1016/j.ecolmodel.2004.04.024 and https://doi.org/10.1093/treephys/25.7.803<br> </p>
Data 1 for "Population- and age-specific patterns of haemosporidian assemblages and infection levels in European Bee-eaters (Merops apiaster)"
<p>Data related to the article "Population- and age-specific patterns of haemosporidian assemblages and infection levels in European Bee-eaters (<em>Merops apiaster</em>)" in the format .xlsx. The 1st sheet contains all variables for analyses done with the R script (see 10.5281/zenodo.3968360). The 2nd sheet contains explanations about the variables in sheet 1.</p>
Figure 3 in Observations on age and reproduction of the oceanic squid Ancistrocheirus lesueurii (d'Orbigny, 1842) (Cephalopoda: Ancistrocheiridae)
Figure 3. Ancistrocheirus lesueurii. (A) Statolith section of a mature intersexual male (ML 275 mm) in which 761 increments could be counted. The areas in boxes are enlarged in B, C and D, where B is the nucleus, C shows regular increments past the mid part of the rostrum and D shows the increments in the periphery of the rostrum.
40Ar/39Ar dating of the East Tenda Shear Zone: Chemical composition of white mica and ages data
<p>In order to clarify the link between <sup>40</sup>Ar/<sup>39</sup>Ar record in white mica and deformation, we performed <em>in situ</em> and bulkwise <sup>40</sup>Ar/<sup>39</sup>Ar dating over the East Tenda Shear Zone (Tenda massif, alpine Corsica). White micas from 11 samples were selected and extensively analyzed using <em>in situ</em> techniques across nested scales of strain-intensity gradients developed at the expense of a Late-Variscan protolith. <sup>40</sup>Ar/<sup>39</sup>Ar systematics are unaffected by inherited Ar and directly linked to deformation with little or no Ar lattice (volume) diffusion. Extensive sampling allows constraining the end of deformation related to burial and exhumation respectively at ~34 and ~22 Ma, bracketing the duration of regional extensional shear to ~12 Myr. Results also highlight a regional strain localization toward the upper contact of the unit with smaller-scale localization in specific lithologies, notably meta-aplites.</p> <p> Second-order complications exist, such as local ill-defined correlations between ages and finite-strain microstructures. Thus, the use of a strain gradient as a proxy for strain localization in time is regionally valid but sometimes locally too complex to track or resolve strain partitioning/localization trends at the meter (outcrop) scale and below. Age mixing and incomplete isotopic homogenization by dissolution/precipitation are identified as the main causes of local discrepancies that complicate the link between age and microstructure and the derivation of strain localization rates. Tracking temporal trends in shear distribution across regional-scale deformation gradients in such settings is possible but requires a multi-scale approach as implemented here to reveal younging patterns associated to strain localization.</p>
Genome-wide association summary statistics for sex- and age-specific analysis of chronic back pain
<p>The dataset comprises summary-level statistics for age- and sex-specific genome-wide association study of chronic back pain (cBP) in individuals of European descent from UK Biobank (<a href="https://www.ukbiobank.ac.uk/">https://www.ukbiobank.ac.uk/</a>). The study was carried out under UK Biobank approved project #18219. </p> <p><strong>The dataset accompanies the paper (please cite if using the dataset):</strong></p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/33021770/">Freidin, Maxim B.; Tsepilov, Yakov A.; Stanaway, Ian B.; Meng, Weihua; Hayward, Caroline; Smith, Blair H.; Khoury, Samar; Parisien, Marc; Bortsov, Andrey; Diatchenko, Luda; Børte, Sigrid; Winsvold, Bendik S.; Brumpton, Ben M.; Zwart, John-Anker; HUNT All-In Pain; Aulchenko, Yurii S.; Suri, Pradeep; Williams, Frances M.K. Sex- and age-specific genetic analysis of chronic back pain. Pain. 2020. doi:10.1097/j.pain.0000000000002100.</a></p> <p>The phenotype of cBP was defined as back pain for 3+ months. Linear mixed-effects additive model was fitted adjusting for age, genotyping array type, and 10 genetic PCs provided by UK Biobank. The following filters were applied: minor allele frequency >0.001, genotyping and individual call rates >0.98%, imputation quality score (INFO) >0.7. GWAS were carried out in males and females separately in the whole sample (<strong>allages</strong>) as well as in groups of younger than 65 years (<strong>under65</strong>) and 65+ years old (<strong>65plus</strong>) as detailed in the paper. Accordingly, 6 files are deposited here, corresponding to each group. </p> <p><strong>Column headers:</strong></p> <p>SNP, SNP rsID </p> <p>CHR, chromosome</p> <p>BP, genomic position (GRCh37 build)</p> <p>EA, effect allele (coded as "1")</p> <p>OTHER, other allele (coded as "0")</p> <p>A1FREQ, frequency of effect allele</p> <p>INFO, imputation quality</p> <p>BETA, effect size (for effect allele)</p> <p>SE, standard error of effect size</p> <p>PVAL, p-value for association</p>
Age-related humerothoracic, scapulothoracic, and glenohumeral kinematics during elevation and rotation motions
<p>Age affects gross shoulder range of motion (ROM), but biomechanical changes over a lifetime are typically only characterized for the humerothoracic joint. Suitable age-related baselines for the scapulothoracic and glenohumeral contributions to humerothoracic motion are needed to advance understanding of shoulder injuries and pathology. Notably, biomechanical comparisons between younger or older populations may obscure detected differences in underlying shoulder motion. Kinematics derived from healthy subjects aged <35 years and >45 years using biplane fluoroscopy and skin-marker motion analysis quantified humerothoracic, scapulothoracic, and glenohumeral motion during 3 static poses (resting neutral, internal rotation to L4-L5, and internal rotation to maximum reach) and 2 dynamic activities (scapular plane abduction and external rotation in adduction). These data are available for download to aid researchers and clinicians in characterizing non-pathologic shoulder motion during common clinical ROM activities. </p>
Photogrammetry dataset for two Bronze Age human teeth from Monte do Vale do Ouro 2 (Ferreira do Alentejo, Portugal)
<p>This photogrammetry dataset that accompanies the publication: "The embodiment of craft production in Bronze Age Portugal: Exceptional dental wear grooves in an individual from Monte do Vale do Ouro 2 (Ferreira do Alentejo, Portugal)" by John Charles Willman, António Carlos Valera, and Ana Maria Silva in the International Journal of Osteoarchaeology, 31(2):252-262. <a href="https://doi.org/10.1002/oa.2944" target="_blank" rel="noopener">https://doi.org/10.1002/oa.2944</a>. Includes photographs of a left and right mandibular lateral incisor (I<sub>2</sub>) with profound dental wear grooves on their lingual root surfaces. 3D models and texture files also included.</p> <p>Part of the “VAPP” Project (<em>Virtual Anthropology of Prehistoric Portugal, </em>Marie Skłodowska-Curie Actions Individual Fellowship, H2020-MSCA-IF-2018, No. 839822).</p>
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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.