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Figure 2 from: Long P, Zhou S-Y, Li S-N, Liu F-F, Chen Z-H (2024) Three new species of Cortinarius section Delibuti (Cortinariaceae, Agaricales) from China. MycoKeys 101: 143-162. https://doi.org/10.3897/mycokeys.101.114705
Figure 2 Basidiomes of Cortinarius fibrillososalor (a, bMHHNU 32494 c, dMHHNU 8657).
Stock prices, dividends, and structural changes in the long-term: The case of U.S.
Open the record for dataset details and reuse information.
LTM_AC_crop: dataset of Long term monitoring of crop yield in young arable alley cropping systems in Belgium
<p>Dataset including results from crop yield and crop quality measurements obtained on a set of six alley cropping fields in Belgium during the first decade after their establishment. Sampled crops include maize, wheat, barley and potato. Measurements started in 2015 and are at present still continued. A transect approach is used in wich crop production is monitored at fixed distances from the trees (2m, 5m and 12m). Ton dry matter is assessed as well as crop specific quality parameters (a.o. for winter cereals: protein content & thousend kernel weight, silage maize: protein content & digestability, potato: under water weight and diameter fractions). For a detailed description of methodologies used see also: Pardon et al. (2018) (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.agsy.2018.08.008" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.agsy.2018.08.008</span></span></a>).</p>
Figure 14 from: Pham TN, Long KD, van Achterberg C, Cao TQN, Pham VP, Dang TH (2024) Revisionary notes on the genus Aulacocentrum Brues (Hymenoptera, Braconidae, Macrocentrinae) from Vietnam. ZooKeys 1197: 13-41. https://doi.org/10.3897/zookeys.1197.116092
Figure 14 Habitus of Aulacocentrum philippinense (Ashmead), male, dorsal, "Macr.126" (IEBR).
Figure 16 from: Pham TN, Long KD, van Achterberg C, Cao TQN, Pham VP, Dang TH (2024) Revisionary notes on the genus Aulacocentrum Brues (Hymenoptera, Braconidae, Macrocentrinae) from Vietnam. ZooKeys 1197: 13-41. https://doi.org/10.3897/zookeys.1197.116092
Figure 16 Distribution map of Aulacocentrum species.
Dataset: Vocal complexity in the long calls of Bornean orangutans
<div> <p>Data and code used for PeerJ article titled, "Vocal complexity in the long calls of Bornean orangutans"</p> <p> </p> <p><strong>Abstract</strong></p> <p>Vocal complexity is central to many evolutionary hypotheses about animal communication. Yet, quantifying and comparing complexity remains a challenge, particularly when vocal types are highly graded. Male Bornean orangutans (Pongo pygmaeus wurmbii) produce complex and variable “long call” vocalizations comprising multiple sound types that vary within and among individuals. Previous studies described six distinct call (or pulse) types within these complex vocalizations, but none quantified their discreteness or the ability of human observers to reliably classify them. We studied the long calls of 13 individuals to: 1) evaluate and quantify the reliability of audio-visual classification by three well-trained observers, 2) distinguish among call types using supervised classification and unsupervised clustering, and 3) compare the performance of different feature sets. Using 46 acoustic features, we used machine learning (i.e., support vector machines, affinity propagation, and fuzzy c-means) to identify call types and assess their discreteness. We also additionally used Uniform Manifold Approximation and Projection (UMAP) to visualize the separation of pulses using both extracted features and spectrogram representations. We found low inter-observer reliability and poor classification accuracy using Ssupervised approaches, showed low inter-observer reliability and poor classification accuracy, indicating that pulse types were not discrete. We propose an updated pulse type classification scheme approach that is highly reproducible across observers and exhibits high strong classification accuracy using support vector machines. Although the low number of call types suggests long calls are fairly simple, the continuous gradation of sounds seems to greatly boost the complexity of this system. This work responds to calls for more quantitative research to define call types and measure quantifythe gradedness of in animal vocal systems and highlights the need for a more comprehensive framework for studying vocal complexity vis-à-vis graded repertoires. </p> </div>
ARTEMIS project - Long-term field experiment (LTE) dataset for crop modelling
<p>The spreadsheets aim at collecting general information and specific data (e.g., crop yield, soil properties and weather) from long-term field experiments (LTE's) that are used to calibrate and validate ARMOSA model for ARTEMIS project - WP3 task 3.1 activity. Particularly, information reported will be useful for providing an overview of the distribution of agro-ecological systems, management strategies, field measures, and weather data available, useful to run ARMOSA model under current and future climate change scenarios.</p>
Increasing Contribution of Adolescent Type 1 Diabetes Drives Incidence Rates in Poland - a 40-year-long Observational Study
<div> <div> <p><span><span>Aims/hypothesis</span></span><span><span>: </span><span>40-year-long longitudinal observation</span> <span>of long-term trends of type 1 diabetes</span><span> incidence</span><span> and prevalence</span> <span>in children </span><span>in Central P</span><span>o</span><span>land </span></span><span> </span></p> </div> <div> <p><span><span>Methods</span></span><span><span>: This was a prospective observational study performed </span><span>by </span><span>a reference </span><span>regional </span><span>center</span><span> for pediatric diabetes care for Lodz Province (currently 2</span><span>·</span><span>4M </span><span>inhabitants</span><span>, 360K children). </span><span>We </span><span>registered </span><span>e</span><span>ach case of new-onset </span><span>type 1 </span><span>diabetes </span><span>admitted</span> <span>to regional pediatric diabetes centers </span><span>between </span><span>the </span><span>years 1983 and 2022 in children between 0 and 14 </span><span>y.o.</span> <span>The diagnosis</span><span> was based on </span><span>currently </span><span>available guidelines</span><span>.</span> <span>C</span><span>ases of other types of diabetes (e.g.</span><span>,</span><span> monogenic) were excluded</span><span> upon identification from incidence and prevalence rates</span><span>. Yearly data on the at</span><span>-</span><span>risk</span> <span>population were </span><span>acquired</span><span> from Poland`s General Statistical Office. Sex-specific data </span><span>on population structure </span><span>were available from 1989 onwards.</span><span> </span></span><span> </span></p> </div> <div> <p><span><span>Results</span></span><span><span>: In the </span><span>analyzed</span> <span>period, </span><span>the </span><span>incidence rate</span><span> of type 1 diabetes</span><span> increased </span><span>tenfold </span><span>from 3</span><span>·</span><span>29/100</span><span>,</span><span>000 (95%CI: </span><span>1</span><span>·</span><span>85</span><span>-</span><span>4</span><span>·</span><span>73</span><span>) in 1983 to 3</span><span>2.43</span><span> (2</span><span>6</span><span>·</span><span>42-38</span><span>·</span><span>44</span><span>) in 2022, with </span><span>an </span><span>average annual percentage change of 5</span><span>·</span><span>73</span><span>% (95%CI: 4</span><span>·</span><span>9</span><span>9</span><span>%-6</span><span>·</span><span>44</span><span>%). Joinpoint analysis detected two distinct periods of increase</span><span>:</span> <span>rapid in 1983-200</span><span>5</span><span> (annual percentage increase </span><span>of </span><span>7</span><span>·</span><span>38</span><span>%</span><span>, 95%CI: </span><span>6</span><span>·</span><span>30</span><span>-1</span><span>0</span><span>·</span><span>52</span><span>%</span><span>) and </span><span>a </span><span>slower </span><span>one</span><span> in </span><span>200</span><span>5</span><span>-2022 (3</span><span>·</span><span>65</span><span>%</span><span>, 95%CI: -0</span><span>·</span><span>86</span><span>-</span><span>5</span><span>·</span><span>13</span><span>%</span><span>). </span><span>I</span><span>ncidence rates among the youngest children (0-4</span> <span>y.o.</span><span>) were significantly lower than in 5-9</span> <span>y.o.</span><span> (</span><span>β±</span><span>SE: -0</span><span>·</span><span>5</span><span>67</span><span>±</span><span>0</span><span>·</span><span>059</span><span>, p<0</span><span>·</span><span>0001</span><span>)</span><span> and 10-14</span> <span>y.o.</span><span> (</span><span>β±</span><span>SE: -0</span><span>·</span><span>520</span><span>±</span><span>0</span><span>·</span><span>0</span><span>60</span><span>, p<0</span><span>·</span><span>0001)</span><span>. </span><span>The </span><span>incidence </span><span>growth dynamic </span><span>for</span> <span>the two older groups showed </span><span>a </span><span>consistent </span><span>increase</span><span>, </span><span>whereas</span><span> the incidence in </span><span>0-4 year-olds</span><span> plateaued after 2007.</span> <span>Incidence rates varied </span><span>seasonally,</span><span> with the </span><span>most cases diagnosed </span><span>during the </span><span>winter months (December, January, </span><span>and </span><span>February</span><span>;</span> <span>mean difference from remaining seasons of 29</span><span>±11</span><span>·</span><span>6 percentage points</span><span>, p</span><span><0</span><span>·</span><span>0001</span><span>). </span><span>Corresponding with increasing incidence rate, estimated prevalence of type 1 diabetes increased over the years and reached </span><span>177</span><span>·</span><span>21</span><span>/100</span><span>,</span><span>000 (95%CI: 163</span><span>·</span><span>18-191</span><span>·</span><span>24)</span><span> for children 0-14 </span><span>y.o.</span><span>,</span><span> and</span><span> </span> <span>1</span><span>7</span><span>·</span><span>11</span> <span>(95%CI:</span><span> 9</span><span>·</span><span>2</span><span>-</span><span>2</span><span>5</span><span>·</span><span>02</span><span>),</span><span> 1</span><span>90</span><span>·</span><span>54</span><span> (95%CI:</span> <span>16</span><span>5</span><span>·</span><span>03</span><span>-</span><span>21</span><span>5</span><span>·</span><span>75</span><span>),</span><span> 2</span><span>38</span><span>·</span><span>73</span><span> (95%CI: </span><span>21</span><span>1</span><span>·</span><span>7</span><span>-26</span><span>5</span><span>·</span><span>76</span><span>)</span><span> for 0-4</span><span>,</span><span> 5-9</span><span>, </span><span>and 10-14</span> <span>y.o.</span><span>, respectively.</span></span><span> </span></p> </div> <div> <p><span><span>Conclusion/i</span><span>nterpretation</span></span><span><span>: </span><span>Over the past 40 years, the incidence of </span><span>type 1 diabetes</span><span> in </span><span>children in </span><span>Central Poland </span><span>has increased significantly, but the rate of increase </span><span>appears to be</span><span> slowing.</span><span> As</span> <span>majority</span><span> of patients with type 1 diabetes are 10 years old or older, with the </span><span>most new</span><span> cases occurring in that age group</span><span> the healthcare systems should prepare for care of young adults who are extensive users of new diabetes technologies</span><span>.</span></span><span> </span></p> </div> </div>
Long-term persistence of arbuscular mycorrhizal fungi in the rhizosphere and bulk soils of non-host Brassica napus and their networks of co-occurring microbes
<p>Arbuscular mycorrhizal fungi (AMF) are obligate plant symbionts that improve the nutrition and health of their host. Most, but not all crops form a symbiosis with AMF. It is the case for canola (<em>Brasica napus</em>), an important crop in the Canadian prairies that is known to not form this association. From 2008 to 2018, an experiment was replicated at three locations of the Canadian prairies and it was used to assess the impact of canola on the community of AMF naturally occurring in three cropping systems, canola monoculture, canola in a two rotation systems (two-years, canola-wheat and three-years barley-pea-canola). We sampled canola rhizosphere and bulk soils to (i) determine diversity and community structure of AMF, and (ii) assess how these AMF communities interact with other fungi and bacteria. We detected 49 AMF Amplicon Sequence Variant (ASV) in canola rhizosphere and bulk soils, confirming the persistence of a diversified AMF community in canola-planted soil, even after 10 years of canola monoculture, which was unexpected considering that canola is among non-mycorrhizal plants. Network analysis revealed a broad range of potential interactions between canola-associated AMF and some fungal and bacterial taxa. We report for the first time that two AMF, <em>Funneliformis mosseae</em> and <em>Rhizophagus iranicus,</em> shared their bacterial cohort almost entirely in bulk soil. Our results suggest the existence of non species-specific AMF-bacteria or AMF-fungi relationships that could benefit AMF in absence of host plants. The persistence of an AMF community in canola rhizosphere and bulk soils bring a new light on AMF ecology and lead to new perspectives for further studies about AMF and soil microbes interactions and AMF subsistence without mycotrophic host plants.</p>
Fig. 6 in Long-Term Development Of Fish Assemblage In Lake Fenéki (Kis-Balaton Water Protection System, Hungary): Succession, Invasion And Stabilization
Fig. 6. Relative abundance of the fish species in Lake Fenéki in the sampling years
Neglected very-long-chain hydrocarbons and the incorporation of body surface areas reveal novel perspectives for chemical profile analysis in insects
<p>Most of what we know about insect cuticular hydrocarbons (CHCs) has been informed by analyses with gas-chromatography coupled with mass spectrometry (GC-MS). However, this analysis technique has its limit, particularly in detecting compounds in a chain length range beyond around C40. Here, we attempt to differentiate the range of CHCs detectable by GC-MS with the largely neglected very-long-chain CHC range up to C58 analyzed by silver-assisted laser desorption/ionization mass spectrometry (Ag-LDI-MS), in seven species of the order Blattodea. For all tested species we unveiled a considerable range of very-long-chain CHCs not detectable by traditional GC-MS technology. This strongly suggests that general studies on insect CHCs routinely miss compounds in that range, and we encourage future studies to implement technologies capable of detecting very-long-chain CHCs. Furthermore, we implement 3D scanned insect body surface areas when quantifying CHC amounts per GC-MS, as Ag-LDI-MS is less suited to quantify total CHC contents. Proportional CHC distributions differed considerably when adjusted for body surface areas, suggesting the importance of their consideration. Moreover, correlations between body surface areas and GC-MS assessed CHC amounts were not significant, further hinting the inaccessibility of whole CHC profile ranges by this technique, potentially by missing the clearly present very-long-chain CHCs.</p>
Long time supernova neutrino simulations
<p>Long time neutrino simulation data up to 20 seconds with no neutrino oscillation</p> <p>File names are formatted as below:</p> <p>model.A.bary.B.BB_grav.C.CC.dat</p> <p>A is a model number and B.BB is a baryonic mass and C.CC is a gravitational mass.</p> <p>Neutron star masses are measured at 20 seconds.</p> <p>Readme.txt<br> explanation of each file</p> <p>model1.bary.1.27_grav.1.36.dat<br> <br> includes neutrino fluxes and luminosities from z9.6 in the Nakazato format (see below and http://asphwww.ph.noda.tus.ac.jp/snn/guide.pdf).<br> <br> See<br> https://academic.oup.com/ptep/article/2021/2/023E01/6045986<br> for more details on the z9.6 supernova explosion.</p> <p><br> model2.bary.1.41_grav.1.31.dat<br> <br> includes neutrino spectra from a supernova which leave a neutron star of 1.41 Msun in baryonic mass and 1.31 in gravitational mass.</p> <p><br> model3.bary.1.22_grav.1.29.dat</p> <p> includes neutrino spectra from a supernova which leave a neutron star of 1.22 Msun in baryonic mass and 1.29 in gravititonal mass.</p> <p><br> sn_spectra.py<br> a python script which interprets the nakazato format.<br> If you run the script from command line, the script outputs fluxes and luminosities and average energies for each flavor.</p> <p> </p> <p><br> Nakazato format</p> <p> The Nakazato format contains time evolutions of neutrino spectra and is a suitable format for estimation of supernova neutrino signals on earth.</p> <p>"T0<br> E0 E1 dN_nue(T0)/dE1 dN_anue(T0)/dE1 dN_nux(T0)/dE1 dL_nue(T0)/dE1 dL_anue(T0)/dE1 dL_nux(T0)/dE1 <br> E1 E2 dN_nue(T0)/dE2 dN_anue(T0)/dE1 dN_nux(T0)/dE2 dL_nue(T0)/dE2 dL_anue(T0)/dE2 dL_nux(T0)/dE2 <br> .<br> .<br> .<br> E19 E20 dN_nue(T0)/dE20 dN_anue(T0)/dE20 dN_nux(T0)/dE20 dL_nue(T0)/dE20 dL_anue(T0)/dE20 dL_nux(T0)/dE20</p> <p> T1<br> E0 E1 dN_nue(T0)/dE1 dN_anue(T1)/dE1 dN_nux(T1)/dE1 dL_nue(T1)/dE1 dL_anue(T1)/dE1 dL_nux(T1)/dE1<br> .<br> .<br> .<br> ",where Tn [s] is a time measured from the bounce and Ek [MeV] is a neutrino energy.</p>
Data on nest choices by long-tailed tit helpers
<p>Cooperative breeding sometimes occurs when adult breeders form groups following natal dispersal and mating. In such cases, individuals typically face a choice of potential social partner with whom to cooperate. Selection of appropriate social partners is crucial to maximising the fitness payoffs from cooperation, but our understanding of the criteria guiding social partner choice is limited. Here, we analyse helping decisions by long-tailed tits (<em>Aegithalos caudatus</em>), which may redirect their care to assist breeders in raising offspring following the failure of their own breeding attempts. In this species, helpers show a preference for helping relatives at nearby nests, but it is unclear whether other criteria that may affect helper fitness are also important in helping decisions. Contrary to expectations, when controlling for kinship, we found that helping decisions were not sensitive to the size or age of broods available to be helped. Individuals were, however, more likely to help broods that were closer to their own failed nesting attempts and that were already being cared for by other helpers. Both effects likely reflect the limited choice available to helpers: although individuals breed close to relatives within kin neighbourhoods, a high rate of nest predation constrains helpers' choice of broods. In other species where cooperatively-breeding groups form after natal dispersal, a greater range of options may be available and here more detailed analysis of group formation will be helpful for determining the decision rules that underpin partner choice and permit stable cooperation in the face of alternative options.</p>
Observations of the breakthrough phase of attachment process in laboratory long sparks
<p>The data supports the manuscript entitled "Observations of the breakthrough phase of attachment process in laboratory long sparks". The *.xlsx file contains the applied voltage waveform and discharge current waveform, and can be opened by Excel. The *.rar file contains the high-speed video frames, and can be decompressed and opened. The data can be used freely for scientific purposes with appropriate citation.</p>
Long-term performance assessment of fully automatic biomedical glottis segmentation at the point of care
<p>Minimal dataset to replicate findings and figures for the publication Groh et al., Long-term performance assessment of fully automatic biomedical glottis segmentation at the point of care.</p>
Fig. 1 in PREVALENCE OF NOSEMA CERANAE (MICROSPORIDIA) IN THE APIS MELLIFERA MELLIFERA BEE COLONIES FROM LONG TIME ISOLATED APIARIES OF SIBERIA
Fig. 1. The map of localization of the Yenisei population and studied apiaries (dots 1–6)
Colorful Mural in Long Beach, CA
Love how Long Beach has a bunch of artful murals throughout its city. Here's one I scanned with Trnio Plus using LiDar capture. Source: Objaverse 1.0 / Sketchfab
Long House
Mesa Verde Colorado cliff dwelling is part of our National Parks at the present time. https://www.nps.gov/meve/learn/historyculture/cd_long_house.htm from 284 images Source: Objaverse 1.0 / Sketchfab
Figure 9 from: Peneva V, Elshishka M, Lazarova S (2012) Studies of the genus Enchodelus Thorne, 1939 (Nematoda, Nordiidae) from Arctic polar deserts. 1. Species with long odontostyle: E. makarovae sp. n. and E. groenlandicus (Ditlevsen, 1927) Thorne, 1939, with an identification key to the species of the E. macrodorus group. ZooKeys 212: 1-23. https://doi.org/10.3897/zookeys.212.3464
Figure 9 - Enchodelus groenlandicus. A–E Female A–E Tail ends. Scale bars: A–E 50 µm.
Figure 71 from: van Achterberg C, Long KD, Chen X-X (2017) Review of Stantonia Ashmead (Hymenoptera, Braconidae, Orgilinae) from Vietnam, China, Japan, and Russia, with descriptions of six new species. ZooKeys 723: 61-119. https://doi.org/10.3897/zookeys.723.21668
Figure 71 Stantonia ruficornis Enderlein, ♀, Vietnam, Cuc Phuong N.P., habitus, lateral aspect.
ScienceDex guides
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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.