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Data for: When does early-life telomere length predict survival? A case study and meta-analysis
<p>Suboptimal conditions during development can shorten telomeres, the protective DNA caps on the end of chromosomes. Shorter early-life telomere length (TL) can indicate reduced somatic maintenance, leading to lower survival and shorter lifespan. However, despite some clear evidence, not all studies show a relationship between early-life TL and survival or lifespan, which may be due to differences in biology or study design (e.g., survival period measured). In superb fairy-wrens (<em>Malurus cyaneus</em>), we assessed whether early-life TL predicts mortality across different life-history stages (fledgling, juvenile, adult). However, in contrast to a similar study on a congener, early-life TL did not predict mortality across any life stage in this species. We then performed a meta-analysis including 32 effect sizes from 23 studies (15 birds and 3 mammals) to quantify the effect of early-life TL on mortality whilst taking into consideration potential sources of biological and methodological variation. Overall, the effect of early-life TL on mortality was significant, corresponding to a 15% reduction in mortality risk with each standard deviation increase in TL. However, the effect became weaker when correcting for publication bias. Contrary to our predictions, there was no evidence that effects of early-life TL on mortality varied with species lifespan or the period over which survival was measured. However, negative effects of early-life TL on mortality risk were pervasive throughout life. These results imply that effects of early-life TL on mortality are more likely context-dependent rather than age-dependent, although substantial power and publication bias issues highlight the need for more research.</p>
Diversity investigation by application of DNA barcoding: a case study of lepidopteran insects in Xinjiang wild fruit forests, China
<p><span>To investigate the species diversity of lepidopteran insects in Xinjiang wild fruit forests, establish insect community monitoring systems and determine the local species pool, we test the applicability of DNA barcoding based on </span><span>cytochrome c oxidase subunit I</span><span> (COI) gene for accurate and rapid identification of insect species</span><span>. From 2017 to 2019, a total of 212 samples with ambiguous morphological identification were selected for DNA barcoding analysis. Five sequence-based methods for species delimitation (ABGD, BINs, GMYC, jMOTU and bPTP) were conducted for comparison to traditional morphology-based identification. In total, 2,422 samples were recorded, representing 143 species of 110 genera in 17 families in Lepidoptera. The diversity analysis showed that the richness indices for Noctuidae was the highest (54 species), and for Pterophoridae, Cossidae, Limacodidae, Lasiocampidae, Pieridae and Lycaenidae were the lowest (all with 1 species). The Shannon-Wiener species diversity index (H') and Pielou's evenness (J') of lepidopteran insects first increased and then decreased across these three years, while the Simpson diversity index showed a trend of subtracted-then-added. For molecular-based identification, 67 lepidopteran species within 61 genera in 14 families were identified through DNA barcoding. Neighbor Joining (NJ) analysis showed that conspecific individuals were clustered together and formed monophyletic groups with a high support value, except for Lacanobia contigua (Denis & Schiffermüller, 1775) (Noctuidae: Hadeninae). Sixty-seven morphospecies were classified into various numbers of MOTUs based on ABGD, BINs, GMYC, jMOTU and bPTP (70, 96, 2, 71, and 71, respectively). In Xinjiang wild fruit forests, the family with the largest number of species is Noctuidae, followed by Geometridae, Crambidae, and the remaining families. The highest Shannon diversity index is observed for the family Noctuidae. Our results indicate that the distance-based methods (ABGD and jMOTU) and character-based method (bPTP) outperform GMYC. BINs is inclined to overestimate species diversity compared to other methods.</span></p>
A Model-driven and Template-based Approach for Requirements Specification : Case studies
<p>This dataset includes the requirements specified using our templates as part of our case studies :</p> <ol> <li>ARINC-653 case study : Time management requirements.</li> <li>ARINC-653 case study : Intra-partition communication requirements.</li> <li>AUTOSAR case study : Crypto service manager requirements.</li> <li>Traditional software case study : Requirements from Kaggle.</li> </ol>
Fig. 3 in Evaluating the principles of wildlife conservation: a case study of wolf (Canis lupus) hunting in Michigan, United States
Fig. 3.—Seasonal occurrence of wolves killing cattle (depredation events) in Michigan, 1996–2012. Note that only 3.8% of depredations occur during the planned hunting season. Source: Michigan Department of Natural Resources (May 2014).
Fig. 2 in Evaluating the principles of wildlife conservation: a case study of wolf (Canis lupus) hunting in Michigan, United States
Fig. 2.—Verified instances of wolves killing cattle (depredations) in Michigan, 1996–2013. The solid line indicates losses from throughout the geographic range of wolves in Michigan, excluding losses that occurred on 1 farm. The dashed line is the additional losses attributable to that 1 farm, where poor animal husbandry likely increased the risk of depredations (see "Characterizing the Problems"). The number of depredations declined considerably just prior to the planning and implementation of Michigan's 1st wolf hunt in 2013. Source: Michigan Department of Natural Resources (May 2014).
Fig. 1 in Evaluating the principles of wildlife conservation: a case study of wolf (Canis lupus) hunting in Michigan, United States
Fig. 1.—Timeline of key political events pertaining to the management of Michigan wolves. The details of each event are given in "Chronology of Political Events." Events above the timeline disfavor wolves and events below the timeline favor wolves. Dashed boxes indicate actions initiated by citizens. Solid boxes indicate actions initiated by the government. Key actors in the timeline are the Michigan Natural Resource Commission (NRC), Michigan Department of Natural Resources (DNR), and the United States Endangered Species Act (ESA). The online version of this figure includes color to indicate which events were associated with the executive branch (green), the courts (black), and each of the particular laws—Public Act (PA) 520 (blue), Senate Bill (SB) 288 and PA 21 (red), and PA 281 (magenta).
Supplementary material 4 from: Eusébio RP, Fonseca PE, Rebelo R, Mathias ML, Reboleira ASPS (2023) How to map potential mesovoid shallow substratum (MSS) habitats? A case study in colluvial MSS. Subterranean Biology 45: 141-156. https://doi.org/10.3897/subtbiol.45.96332
Abundance of invertebrates collected in colluvial Mesovoid Shallow Substratum (MSS) at the Arrábida National Park
Supplementary material 3 from: Eusébio RP, Fonseca PE, Rebelo R, Mathias ML, Reboleira ASPS (2023) How to map potential mesovoid shallow substratum (MSS) habitats? A case study in colluvial MSS. Subterranean Biology 45: 141-156. https://doi.org/10.3897/subtbiol.45.96332
Results of criteria met for each location pinpointed as potential colluvial Mesovoid Shallow Substratum (MSS)
Supplementary material 1 from: Eusébio RP, Fonseca PE, Rebelo R, Mathias ML, Reboleira ASPS (2023) How to map potential mesovoid shallow substratum (MSS) habitats? A case study in colluvial MSS. Subterranean Biology 45: 141-156. https://doi.org/10.3897/subtbiol.45.96332
Characterization of each of the locations found in situ as colluvial Mesovoid Shallow Substratum (MSS): latitude, longitude and estimated area (m2)
Supplementary material 5 from: Eusébio RP, Fonseca PE, Rebelo R, Mathias ML, Reboleira ASPS (2023) How to map potential mesovoid shallow substratum (MSS) habitats? A case study in colluvial MSS. Subterranean Biology 45: 141-156. https://doi.org/10.3897/subtbiol.45.96332
Total invertebrate abundance, collected in colluvial Mesovoid Shallow Substratum (MSS) at the Arrábida National Park
Effect of urban heat island mitigation strategies on precipitation and temperature in Montreal, Canada: case studies - Data from numerical experiments
<p>Surface data from the numerical experiments for the paper : "Effect of urban heat island mitigation strategies on precipitation and temperature in Montreal, Canada: case studies", submitted in PLOS Climate. </p> <p>The data is in NetCDF, structure as follow: 1 file per initialization date that contains the model outputs for the surface variables (air temperature, dew-point, relative humidity) and 1 file per initialization date that contains the accumulated precipitation. </p>
Data for: Can pharmaceutical pollution alter the spread of infectious disease? A case study using fluoxetine.
<p>Human activity is changing global environments at an unprecedented rate, imposing new ecological and evolutionary ramifications on wildlife dynamics, including host-parasite interactions. Here we investigate how an emerging concern of modern human activity, pharmaceutical pollution, influences the spread of disease in a population, using the water flea <em>Daphnia</em> <em>magna</em> and the bacterial pathogen <em>Pasteuria</em> <em>ramosa</em> as a model system. We found that exposure to different concentrations of fluoxetine—a widely prescribed psychoactive drug and widespread contaminant of aquatic ecosystems—affected the severity of disease experienced by an individual in a non-monotonic manner. The direction and magnitude of any effect, however, varied with both the infection outcome measured, as well as the genotype of the pathogen. In contrast, the characteristics of unexposed animals, and thus the growth and density of susceptible hosts, were robust to fluoxetine. Using our data to parameterise an epidemiology model, we show that fluoxetine is unlikely to lead to a net increase or decrease in the likelihood of an infectious disease outbreak, as measured by a pathogen's transmission rate or basic reproductive number. Instead, any given pathogen genotype may experience a two-fold change in likely fitness, but often in opposing directions. Our study demonstrates that changes in pharmaceutical pollution give rise to complex genotype-by-environment interactions in its influence on disease dynamics, with repercussions on pathogen genetic diversity and evolution.</p>
Figure 3 in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
Figure 3. Example of Grad-CAM heatmaps obtained for Podarcis lusitanicus. The upper images show two common patterns observed in male dorsal images (also found, albeit with some differences, in females). The bottom images exhibit the patterns most frequently found in male and female head lateral images (here illustrated in two females).
Figure 2 in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
Figure 2. Confusion matrix for male (upper) and female (lower) image classification for the two-class case based on a combination of predictions from six models. Abbreviations used: Pboc, P. bocagei; Plus, P. lusitanicus.
Highly variable (no clear pattern). All portions of the dorsal views were equally used. In head images the area around the eye, the top of the head, the snout and the throat were all used in similar proportions. P. carbonelli Variable for both views. Snout and middle of the dorsum used in dorsal view. Top of the head most frequently (but not strictly) used in lateral view. P. guadarramae Whole body used for dorsal view (but variable); either throat (most common) or ear region used in head lateral views. P. hispanicus Variable. Anterior portion of snout used more frequently than in other species for both dorsal and head lateral views. P. liolepis Highly variable. Whole body used in most dorsal images, area around the eye and throat used in head lateral views, but other patterns common. P. lusitanicus Highly variable. All parts of the dorsum used (but frequently the most posterior part); area around the ear frequently used in head lateral images. P. tunesiacus Highly variable. Dorsal area near the insertion of the posterior limbs used more frequently than in other species; different regions of the head used, often simultaneously. P. Ʋaucheri Highly variable. Different regions of dorsum (from head to the posterior region) used in dorsal images, all portions of the head, but most frequently the throat, used in lateral images. P. Ʋirescens Highly variable. All parts of both images used. Head and anterior part of the dorsum more used than in other species. in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
Highly variable (no clear pattern). All portions of the dorsal views were equally used. In head images the area around the eye, the top of the head, the snout and the throat were all used in similar proportions. P. carbonelli Variable for both views. Snout and middle of the dorsum used in dorsal view. Top of the head most frequently (but not strictly) used in lateral view. P. guadarramae Whole body used for dorsal view (but variable); either throat (most common) or ear region used in head lateral views. P. hispanicus Variable. Anterior portion of snout used more frequently than in other species for both dorsal and head lateral views. P. liolepis Highly variable. Whole body used in most dorsal images, area around the eye and throat used in head lateral views, but other patterns common. P. lusitanicus Highly variable. All parts of the dorsum used (but frequently the most posterior part); area around the ear frequently used in head lateral images. P. tunesiacus Highly variable. Dorsal area near the insertion of the posterior limbs used more frequently than in other species; different regions of the head used, often simultaneously. P. Ʋaucheri Highly variable. Different regions of dorsum (from head to the posterior region) used in dorsal images, all portions of the head, but most frequently the throat, used in lateral images. P. Ʋirescens Highly variable. All parts of both images used. Head and anterior part of the dorsum more used than in other species.
Figure 1 in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
Figure 1. The two image types analysed in this study (before pre-processing): above, a dorsal view; below, a head lateral image. Both images correspond to the same Podarcis Ʋaucheri s.l. male.
Highly variable. Mid-portion of the dorsum used frequently (although other areas as well). Tip of the snout used often, but area around the ear and throat are also relevant. P. carbonelli Variable. In the dorsal view, the tip of the snout is frequently used. In the head lateral view, the tip of the snout is also com- monly used, as well as the most posterior region of the head. P. guadarramae Variable. Mid portion of the dorsum and tip of the snout are the regions used more frequently in dorsal and head lateral views, respectively. P. hispanicus Variable. The head and most anterior part of the dorsum are frequently used in the dorsal view. Snout and/or top of posterior region of head used. P. liolepis Variable. Different parts of the dorsum are used, whereas the tip of the snout is used in most head lateral images. P. lusitanicus Anterior dorsum, in the dorsal view, and both snout and posterior side of the head (in head lateral views) frequently used. P. tunesiacus Variable. Tip of the snout and posterior part of the trunk more used than in other species; snout and top head region behind the eye used with some frequency. P. Ʋaucheri Highly variable. All parts of the dorsum used in dorsal images, various parts of the head (but frequently snout and throat combined) used in head lateral images. P. Ʋirescens Highly variable. All portions of the dorsum used in dorsal images, region around and behind the ear more used than in other species for head lateral images. in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
Highly variable. Mid-portion of the dorsum used frequently (although other areas as well). Tip of the snout used often, but area around the ear and throat are also relevant. P. carbonelli Variable. In the dorsal view, the tip of the snout is frequently used. In the head lateral view, the tip of the snout is also com- monly used, as well as the most posterior region of the head. P. guadarramae Variable. Mid portion of the dorsum and tip of the snout are the regions used more frequently in dorsal and head lateral views, respectively. P. hispanicus Variable. The head and most anterior part of the dorsum are frequently used in the dorsal view. Snout and/or top of posterior region of head used. P. liolepis Variable. Different parts of the dorsum are used, whereas the tip of the snout is used in most head lateral images. P. lusitanicus Anterior dorsum, in the dorsal view, and both snout and posterior side of the head (in head lateral views) frequently used. P. tunesiacus Variable. Tip of the snout and posterior part of the trunk more used than in other species; snout and top head region behind the eye used with some frequency. P. Ʋaucheri Highly variable. All parts of the dorsum used in dorsal images, various parts of the head (but frequently snout and throat combined) used in head lateral images. P. Ʋirescens Highly variable. All portions of the dorsum used in dorsal images, region around and behind the ear more used than in other species for head lateral images.
Figure 4 in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
Figure 4. Confusion matrix for male (upper) and female (lower) image classification for the nine-class experiment based on a combination of predictions from six models. Abbreviations used: Pboc, P. bocagei; Pcar, P. carbonelli; Phis, P. hispanicus; Plio, P. liolepis; Plus, P. lusitanicus; Pvsl, P. Ʋaucheri s.l.; Pvss, P. Ʋaucheri s.s.; Pvir, P. Ʋirescens.
BOLD Insecta and Araneae data files for "Mining biodiversity databases establishes a global baseline of cosmopolitan Insecta mOTUs: a case study on Platygastroidea (Hymenoptera) with consequences for biological control programs"
<p>These are the BOLD Insecta and Araneae DWC files for use with the automated scripting procedure from "Mining biodiversity databases establishes a global baseline of cosmopolitan Insecta mOTUs: a case study on Platygastroidea (Hymenoptera) with consequences for biological control programs".</p>
Can Archean Impact Structures be Discovered? A Case Study from Earth's Largest, Most Deeply Eroded Impact Structure. Additional Data.
<p>Modeling inputs: Input files for iSALE. </p> <p>Code: Files for Matlab. </p> <p>Out: Graphs and tabulated results. </p>
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.